diff --git a/scripts/generate-datasets.mjs b/scripts/generate-datasets.mjs
index 4a2b98d..29a1403 100644
--- a/scripts/generate-datasets.mjs
+++ b/scripts/generate-datasets.mjs
@@ -66,6 +66,20 @@ function isFiniteNumber(value) {
   return typeof value === 'number' && Number.isFinite(value);
 }
 
+// Mirrors isRecord in src/lib/performance.ts — keep in sync. `typeof x === 'object'` alone is
+// true for arrays too, which previously let a stray `"finetune": []` be misclassified as a
+// fine-tuned run here while the browser-side isRecord() correctly rejected it.
+function isRecord(value) {
+  return typeof value === 'object' && value !== null && !Array.isArray(value);
+}
+
+// Mirrors toText in src/lib/performance.ts — keep in sync.
+function toText(value) {
+  if (typeof value !== 'string') return null;
+  const trimmed = value.trim();
+  return trimmed ? trimmed : null;
+}
+
 function resolveMetricKey(task, metrics) {
   const candidates = TASK_METRIC_KEYS[task] ?? [];
   for (const key of candidates) {
@@ -119,27 +133,39 @@ function computeCategoryScores(metrics) {
   return { f1, map, precision, recall };
 }
 
+// Mirrors buildRunNote in src/lib/performance.ts — keep in sync. entry.notes carries the run's
+// full methodology text (prompt used, parsing rules, metric definitions, etc.) straight from the
+// benchmark script, appended after the generated summary line rather than discarded.
 function buildRunNote(entry) {
-  const parts = [];
-  if (isFiniteNumber(entry.num_samples)) parts.push(`${entry.num_samples} samples`);
-  if (typeof entry.device === 'string' && entry.device.trim()) parts.push(entry.device.trim());
-  return parts.length ? parts.join(' · ') : null;
+  const parts = ['Zero-shot'];
+  if (isFiniteNumber(entry.num_samples)) parts.push(`evaluated on ${entry.num_samples} images`);
+  const summary = parts.join(' · ');
+  const detail = toText(entry.notes);
+  return detail ? `${summary}\n\n${detail}` : summary;
 }
 
-function buildFinetuneNote(finetune) {
-  if (!finetune || typeof finetune !== 'object') return null;
-  const parts = [];
+// Mirrors buildFinetuneNote in src/lib/performance.ts — keep in sync.
+function buildFinetuneNote(finetune, entryNotes) {
+  if (!isRecord(finetune)) return null;
+  const parts = ['Fine-tuned'];
+  if (isFiniteNumber(finetune.train_samples)) parts.push(`trained on ${finetune.train_samples} images from this dataset`);
+  if (isFiniteNumber(finetune.val_samples)) parts.push(`validated on ${finetune.val_samples} images`);
   if (isFiniteNumber(finetune.epochs)) parts.push(`${finetune.epochs} epochs`);
-  if (isFiniteNumber(finetune.train_samples)) parts.push(`${finetune.train_samples} train samples`);
-  return parts.length ? parts.join(' · ') : null;
+  if (isFiniteNumber(finetune.lr)) parts.push(`lr=${finetune.lr}`);
+  if (isFiniteNumber(finetune.weight_decay)) parts.push(`weight decay=${finetune.weight_decay}`);
+  if (isFiniteNumber(finetune.split_seed)) parts.push(`seed=${finetune.split_seed}`);
+  if (isFiniteNumber(finetune.train_ratio)) parts.push(`train ratio=${finetune.train_ratio}`);
+  const summary = parts.join(' · ');
+  const detail = toText(entryNotes);
+  return detail ? `${summary}\n\n${detail}` : summary;
 }
 
 // Rendered as train/test/val percentages in that fixed order, e.g. "10/80/10" — mirrors
 // buildSplitBreakdown in src/lib/performance.ts, kept in sync for the same reason as above.
 function buildSplitBreakdown(entry, finetune) {
   const testSamples = isFiniteNumber(entry.num_samples) ? entry.num_samples : null;
-  const trainSamples = finetune != null && typeof finetune === 'object' && isFiniteNumber(finetune.train_samples) ? finetune.train_samples : null;
-  const valSamples = finetune != null && typeof finetune === 'object' && isFiniteNumber(finetune.val_samples) ? finetune.val_samples : null;
+  const trainSamples = isRecord(finetune) && isFiniteNumber(finetune.train_samples) ? finetune.train_samples : null;
+  const valSamples = isRecord(finetune) && isFiniteNumber(finetune.val_samples) ? finetune.val_samples : null;
   const total = (trainSamples ?? 0) + (testSamples ?? 0) + (valSamples ?? 0);
   if (!total) return null;
   const toShare = (value) => (value != null ? ((value / total) * 100).toFixed(0) : '-');
@@ -161,17 +187,17 @@ function makeLeaderboardRow(entry, metricKey, variant) {
     categoryScores: computeCategoryScores(entry.metrics),
     benchmarkId: isFiniteNumber(entry.benchmark_id) ? entry.benchmark_id : null,
     variant,
-    date: typeof entry.timestamp === 'string' ? entry.timestamp.slice(0, 10) : null,
+    date: toText(entry.timestamp)?.slice(0, 10) ?? null,
     submitted_by: null,
     link: null,
-    notes: variant === 'fine-tuned' ? buildFinetuneNote(finetune) : buildRunNote(entry),
-    optimized: isOptimized(entry.optimized) || (finetune != null && typeof finetune === 'object' && isOptimized(finetune.optimized)),
-    platform: typeof entry.device === 'string' && entry.device.trim() ? entry.device.trim() : null,
+    notes: variant === 'fine-tuned' ? buildFinetuneNote(finetune, entry.notes) : buildRunNote(entry),
+    optimized: isOptimized(entry.optimized) || (isRecord(finetune) && isOptimized(finetune.optimized)),
+    platform: toText(entry.device),
     splitBreakdown: buildSplitBreakdown(entry, finetune),
     // dataset_config is the result set's own config field (raw / augmented) — no fallback to
     // `split`, which is a different concept (the run's data split, e.g. "train"). Absent config
     // means the run used the dataset's raw (unaugmented) form.
-    datasetConfig: typeof entry.dataset_config === 'string' && entry.dataset_config.trim() ? entry.dataset_config.trim() : 'raw',
+    datasetConfig: toText(entry.dataset_config) ?? 'raw',
   };
 }
 
@@ -190,7 +216,7 @@ function buildLeaderboardFromRawResults(rawResults) {
       entry,
       metricKey,
       score: entry.metrics[metricKey],
-      isFinetune: entry.finetune != null && typeof entry.finetune === 'object',
+      isFinetune: isRecord(entry.finetune),
     });
   }
 
@@ -279,10 +305,14 @@ function buildGlobalPerformanceRecords(performanceDatasets, metadataLookup) {
         machine_learning_task: meta.machine_learning_task,
         benchmarkId: entry.benchmarkId ?? null,
         variant: entry.variant === 'zero-shot' || entry.variant === 'fine-tuned' ? entry.variant : null,
-        optimized: Boolean(entry.optimized),
-        platform: typeof entry.platform === 'string' && entry.platform.trim() ? entry.platform.trim() : null,
-        splitBreakdown: entry.splitBreakdown ?? null,
-        datasetConfig: entry.datasetConfig ?? null,
+        // isOptimized(), not Boolean() — a pre-built {leaderboard:[...]} entry may still encode
+        // this as the raw "yes"/"no" string convention (see isOptimized above), and Boolean("no")
+        // is true.
+        optimized: isOptimized(entry.optimized),
+        platform: toText(entry.platform),
+        splitBreakdown: toText(entry.splitBreakdown),
+        datasetConfig: toText(entry.datasetConfig),
+        notes: toText(entry.notes),
       });
     });
   }
diff --git a/src/components/LeaderboardDetailModal.module.css b/src/components/LeaderboardDetailModal.module.css
index 268e618..d94c886 100644
--- a/src/components/LeaderboardDetailModal.module.css
+++ b/src/components/LeaderboardDetailModal.module.css
@@ -29,53 +29,6 @@
   margin-bottom: 1.1rem;
 }
 
-.badgeRow {
-  display: flex;
-  gap: 0.4rem;
-  flex-wrap: wrap;
-  align-items: center;
-  margin-bottom: 0.5rem;
-}
-
-.taskBadge {
-  font-family: var(--ifm-code-font-family);
-  font-size: 0.65rem;
-  padding: 0.15rem 0.5rem;
-  border-radius: 4px;
-  display: inline-block;
-  white-space: nowrap;
-}
-
-.badgeClassification {
-  background: var(--agml-badge-classification-bg);
-  color: var(--agml-badge-classification-fg);
-}
-
-.badgeDetection {
-  background: var(--agml-badge-detection-bg);
-  color: var(--agml-badge-detection-fg);
-}
-
-.badgeSegmentation {
-  background: var(--agml-badge-segmentation-bg);
-  color: var(--agml-badge-segmentation-fg);
-}
-
-.badgeOther {
-  background: var(--agml-badge-other-bg);
-  color: var(--agml-badge-other-fg);
-}
-
-.resultTypeBadge {
-  font-family: var(--ifm-code-font-family);
-  font-size: 0.65rem;
-  padding: 0.15rem 0.5rem;
-  border-radius: 4px;
-  background: var(--agml-tag-bg);
-  color: var(--agml-tag-fg);
-  display: inline-block;
-}
-
 .title {
   font-family: var(--ifm-code-font-family);
   font-weight: 700;
@@ -243,3 +196,16 @@
   line-height: 1.5;
   color: var(--agml-muted);
 }
+
+.notesMeta {
+  margin: 0 0 0.4rem;
+}
+
+.notesText {
+  margin: 0;
+  color: var(--agml-text);
+  overflow-wrap: anywhere;
+  white-space: pre-wrap;
+  max-height: 220px;
+  overflow-y: auto;
+}
diff --git a/src/components/LeaderboardDetailModal.tsx b/src/components/LeaderboardDetailModal.tsx
index 361b34d..c2fe542 100644
--- a/src/components/LeaderboardDetailModal.tsx
+++ b/src/components/LeaderboardDetailModal.tsx
@@ -1,31 +1,12 @@
 import { Fragment, useEffect, useState } from 'react';
 import Link from '@docusaurus/Link';
-import type { GlobalLeaderboardEntry } from '../lib/performance';
+import { formatGlobalResultTypeKey, globalResultTypeKey, METRIC_LABELS } from '../lib/performance';
+import type { ModelComparisonDetail } from '../lib/performance';
+import { toDisplayLabel } from '../lib/labelOverrides';
 import styles from './LeaderboardDetailModal.module.css';
 
-function toLabel(value: string) {
-  return value.replace(/_/g, ' ');
-}
-
-// 1st, 2nd, 3rd, 4th, ..., 11th-13th stay "th" (the exception the mod-10 rule alone gets wrong).
-function ordinal(value: number): string {
-  const rounded = Math.round(value);
-  const mod100 = rounded % 100;
-  if (mod100 >= 11 && mod100 <= 13) return `${rounded}th`;
-  switch (rounded % 10) {
-    case 1:
-      return `${rounded}st`;
-    case 2:
-      return `${rounded}nd`;
-    case 3:
-      return `${rounded}rd`;
-    default:
-      return `${rounded}th`;
-  }
-}
-
-function formatPercentile(value: number | null) {
-  return value == null ? null : `${ordinal(value)} pctl`;
+function formatRank(rank: number | null, totalModels: number) {
+  return rank == null ? null : `#${rank} of ${totalModels}`;
 }
 
 function formatScore(value: number) {
@@ -33,25 +14,16 @@ function formatScore(value: number) {
 }
 
 function formatResultType(entry: { variant: 'zero-shot' | 'fine-tuned' | null; optimized: boolean }) {
-  const base = entry.variant === 'fine-tuned' ? 'Fine-tuned' : entry.variant === 'zero-shot' ? 'Zero-shot' : '—';
-  if (base === '—') return base;
-  return entry.optimized ? `${base} (optimized)` : base;
-}
-
-function taskBadgeClass(task: string | null): string {
-  if (!task) return styles.badgeOther;
-  if (task.includes('classif')) return styles.badgeClassification;
-  if (task.includes('detect')) return styles.badgeDetection;
-  if (task.includes('segment')) return styles.badgeSegmentation;
-  return styles.badgeOther;
+  const key = globalResultTypeKey(entry);
+  return key ? formatGlobalResultTypeKey(key) : '—';
 }
 
 export function LeaderboardDetailModal({
-  entry,
+  detail,
   open,
   onClose,
 }: {
-  entry: GlobalLeaderboardEntry | null;
+  detail: ModelComparisonDetail | null;
   open: boolean;
   onClose: () => void;
 }) {
@@ -73,7 +45,7 @@ export function LeaderboardDetailModal({
   }, [open, onClose]);
 
   const [expandedKeys, setExpandedKeys] = useState<Set<string>>(new Set());
-  useEffect(() => setExpandedKeys(new Set()), [entry]);
+  useEffect(() => setExpandedKeys(new Set()), [detail]);
   const toggleExpanded = (key: string) => {
     setExpandedKeys((current) => {
       const next = new Set(current);
@@ -83,7 +55,7 @@ export function LeaderboardDetailModal({
     });
   };
 
-  if (!open || entry == null) return null;
+  if (!open || detail == null) return null;
 
   return (
     <div className={styles.backdrop} role="presentation" onClick={onClose}>
@@ -97,17 +69,12 @@ export function LeaderboardDetailModal({
         <div className={styles.header}>
           <div>
             <h2 id="leaderboard-detail-title" className={styles.title}>
-              {entry.model}
+              {detail.model}
+              {detail.task ? ` (${toDisplayLabel(detail.task)})` : ''}
             </h2>
-            <div className={styles.badgeRow}>
-              <span className={`${styles.taskBadge} ${taskBadgeClass(entry.machineLearningTask)}`}>
-                {entry.machineLearningTask ? toLabel(entry.machineLearningTask) : 'Unknown task'}
-              </span>
-              <span className={styles.resultTypeBadge}>{entry.resultType}</span>
-            </div>
             <p className={styles.summaryLine}>
-              {entry.appearances} result{entry.appearances === 1 ? '' : 's'} across {entry.datasets.length} dataset
-              {entry.datasets.length === 1 ? '' : 's'}
+              Ranked by {METRIC_LABELS[detail.metric]} across {detail.datasets.length} dataset
+              {detail.datasets.length === 1 ? '' : 's'} shared with all {detail.totalModels} compared models
             </p>
           </div>
           <button type="button" className={styles.closeButton} onClick={onClose} aria-label="Close model details">
@@ -115,17 +82,17 @@ export function LeaderboardDetailModal({
           </button>
         </div>
 
-        <p className={styles.sectionTitle}>Datasets included ({entry.datasetDetails.length})</p>
+        <p className={styles.sectionTitle}>Datasets contributing to this average ({detail.datasets.length})</p>
         <div className={styles.tableWrap}>
           <div className={styles.table} role="table">
             <div className={styles.tableRow} role="row">
               <span role="columnheader">Dataset</span>
               <span role="columnheader">Split %</span>
               <span role="columnheader">Config</span>
-              <span role="columnheader">Scores (percentile)</span>
+              <span role="columnheader">Scores (rank)</span>
             </div>
-            {entry.datasetDetails.map((detail, index) => {
-              const rowKey = `${detail.dataset}-${index}`;
+            {detail.datasets.map((datasetDetail, index) => {
+              const rowKey = `${datasetDetail.dataset}-${index}`;
               const isExpanded = expandedKeys.has(rowKey);
               const scoreLines = (
                 [
@@ -136,12 +103,12 @@ export function LeaderboardDetailModal({
                 ] as [string, 'f1' | 'map' | 'precision' | 'recall'][]
               )
                 .map(([label, category]) => {
-                  const score = detail.scores[category];
-                  const pctl = detail.percentiles[category];
-                  if (score == null || pctl == null) return null;
-                  return [label, formatScore(score), formatPercentile(pctl)] as [string, string, string];
+                  const score = datasetDetail.scores[category];
+                  const rank = formatRank(datasetDetail.ranks[category], detail.totalModels);
+                  if (score == null) return null;
+                  return [label, formatScore(score), rank] as [string, string, string | null];
                 })
-                .filter((line): line is [string, string, string] => line != null);
+                .filter((line): line is [string, string, string | null] => line != null);
 
               return (
                 <Fragment key={rowKey}>
@@ -161,14 +128,14 @@ export function LeaderboardDetailModal({
                     <span role="cell">
                       <div className={styles.datasetCell}>
                         <button type="button" className={styles.datasetName}>
-                          {toLabel(detail.dataset)}
+                          {toDisplayLabel(datasetDetail.dataset)}
                           <span className={styles.expandChevron} aria-hidden>
                             {isExpanded ? '▲' : '▾'}
                           </span>
                         </button>
                         <Link
                           className={styles.viewDatasetLink}
-                          to={`/datasets?dataset=${encodeURIComponent(detail.dataset)}`}
+                          to={`/datasets?dataset=${encodeURIComponent(datasetDetail.dataset)}`}
                           onClick={(event) => event.stopPropagation()}
                         >
                           view dataset
@@ -176,19 +143,19 @@ export function LeaderboardDetailModal({
                       </div>
                     </span>
                     <span role="cell" className={styles.simpleCell}>
-                      {detail.splitBreakdown ?? '—'}
+                      {datasetDetail.splitBreakdown ?? '—'}
                     </span>
                     <span role="cell" className={styles.simpleCell}>
-                      {detail.datasetConfig ?? '—'}
+                      {datasetDetail.datasetConfig ?? '—'}
                     </span>
                     <span role="cell">
                       {scoreLines.length === 0 ? (
                         '—'
                       ) : (
                         <div className={styles.scoresCell}>
-                          {scoreLines.map(([label, score, pctl]) => (
+                          {scoreLines.map(([label, score, rank]) => (
                             <span key={label}>
-                              {label} {score} <span>({pctl})</span>
+                              {label} {score} {rank && <span>({rank})</span>}
                             </span>
                           ))}
                         </div>
@@ -197,7 +164,10 @@ export function LeaderboardDetailModal({
                   </div>
                   {isExpanded && (
                     <div className={styles.notesRow}>
-                      {formatResultType(detail)} · {detail.platform ?? 'unknown platform'}
+                      <p className={styles.notesMeta}>
+                        {formatResultType(datasetDetail)} · {datasetDetail.platform ?? 'unknown platform'}
+                      </p>
+                      <p className={styles.notesText}>{datasetDetail.notes ?? 'No additional notes for this result.'}</p>
                     </div>
                   )}
                 </Fragment>
diff --git a/src/lib/performance.ts b/src/lib/performance.ts
index 6c1379f..110ab9b 100644
--- a/src/lib/performance.ts
+++ b/src/lib/performance.ts
@@ -83,8 +83,8 @@ function normalizeEntry(raw: unknown): PerformanceEntry | null {
     date: toText(raw.date ?? raw.submitted_at),
     link: toText(raw.link ?? raw.url ?? raw.source_link),
     notes: toText(raw.notes),
-    variant: null,
-    optimized: Boolean(raw.optimized),
+    variant: raw.variant === 'zero-shot' || raw.variant === 'fine-tuned' ? raw.variant : null,
+    optimized: isOptimized(raw.optimized),
     splitBreakdown: toText(raw.split_breakdown),
     trainPercentage: toNumber(raw.train_percentage),
     datasetConfig: toText(raw.dataset_config ?? raw.config) ?? 'raw',
@@ -158,6 +158,13 @@ export interface CategoryScores {
   recall: number | null;
 }
 
+export const METRIC_LABELS: Record<keyof CategoryScores, string> = {
+  f1: 'F1',
+  map: 'mAP',
+  precision: 'Precision',
+  recall: 'Recall',
+};
+
 // Every run can report several metric families at once (F1, mAP, precision, recall),
 // independent of the single metric resolveMetricKey picks for the legacy per-dataset rank.
 // These four category scores back the leaderboard's four sortable global columns — precision
@@ -412,17 +419,7 @@ export interface GlobalPerformanceRecord {
   platform: string | null;
   splitBreakdown: string | null;
   datasetConfig: string | null;
-}
-
-export interface GlobalLeaderboardDatasetDetail {
-  dataset: string;
-  percentiles: CategoryPercentiles;
-  scores: CategoryScores;
-  variant: 'zero-shot' | 'fine-tuned' | null;
-  optimized: boolean;
-  platform: string | null;
-  splitBreakdown: string | null;
-  datasetConfig: string | null;
+  notes: string | null;
 }
 
 export function globalResultTypeKey(record: { variant: 'zero-shot' | 'fine-tuned' | null; optimized: boolean }): string | null {
@@ -437,21 +434,6 @@ export function formatGlobalResultTypeKey(key: string) {
   return optimized ? `${label} (optimized)` : label;
 }
 
-export interface GlobalLeaderboardEntry {
-  model: string;
-  machineLearningTask: string | null;
-  avgF1Percentile: number | null;
-  avgMapPercentile: number | null;
-  avgPrecisionPercentile: number | null;
-  avgRecallPercentile: number | null;
-  appearances: number;
-  datasets: string[];
-  fineTunedDatasets: string[];
-  resultType: string;
-  optimized: boolean;
-  datasetDetails: GlobalLeaderboardDatasetDetail[];
-}
-
 function normalizeCategoryPercentiles(raw: unknown): CategoryPercentiles {
   const record = isRecord(raw) ? raw : {};
   return {
@@ -499,32 +481,24 @@ function normalizeGlobalPerformanceRecord(raw: unknown): GlobalPerformanceRecord
     platform: toText(raw.platform),
     splitBreakdown: toText(raw.splitBreakdown),
     datasetConfig: toText(raw.datasetConfig),
+    notes: toText(raw.notes),
   };
 }
 
-function formatResultTypeLabel(variants: Set<'zero-shot' | 'fine-tuned'>, optimized: boolean) {
-  let base: string;
-  if (variants.size === 0) base = '—';
-  else if (variants.size > 1) base = 'Mixed';
-  else base = variants.has('fine-tuned') ? 'Fine-tuned' : 'Zero-shot';
-
-  if (base === '—') return base;
-  return optimized ? `${base} (optimized)` : base;
+export interface GlobalLeaderboardFilterOptions {
+  cropTypes?: string[];
+  mlTasks?: string[];
+  resultTypes?: string[];
+  tuned?: ('tuned' | 'not-tuned')[];
+  optimizedValues?: ('optimized' | 'not-optimized')[];
+  platforms?: string[];
+  datasets?: string[];
 }
 
-export function computeGlobalLeaderboard(
+function filterGlobalRecords(
   records: GlobalPerformanceRecord[],
-  options: {
-    cropTypes?: string[];
-    mlTasks?: string[];
-    resultTypes?: string[];
-    tuned?: ('tuned' | 'not-tuned')[];
-    optimizedValues?: ('optimized' | 'not-optimized')[];
-    platforms?: string[];
-    datasets?: string[];
-    minAppearances?: number;
-  } = {}
-): GlobalLeaderboardEntry[] {
+  options: GlobalLeaderboardFilterOptions
+): GlobalPerformanceRecord[] {
   const {
     cropTypes = [],
     mlTasks = [],
@@ -533,100 +507,302 @@ export function computeGlobalLeaderboard(
     optimizedValues = [],
     platforms = [],
     datasets = [],
-    minAppearances = 3,
   } = options;
-  const stats = new Map<
-    string,
-    {
-      model: string;
-      machineLearningTask: string | null;
-      categoryTotals: Record<keyof CategoryPercentiles, number>;
-      categoryCounts: Record<keyof CategoryPercentiles, number>;
-      appearances: number;
-      datasets: Set<string>;
-      fineTunedDatasets: Set<string>;
-      variants: Set<'zero-shot' | 'fine-tuned'>;
-      optimized: boolean;
-      datasetDetails: GlobalLeaderboardDatasetDetail[];
-    }
-  >();
 
-  for (const record of records) {
-    if (cropTypes.length && !record.crop_types?.some((crop) => cropTypes.includes(crop))) continue;
-    if (mlTasks.length && !(record.machine_learning_task && mlTasks.includes(record.machine_learning_task))) continue;
+  return records.filter((record) => {
+    if (cropTypes.length && !record.crop_types?.some((crop) => cropTypes.includes(crop))) return false;
+    if (mlTasks.length && !(record.machine_learning_task && mlTasks.includes(record.machine_learning_task))) return false;
     if (resultTypes.length) {
       const resultTypeKey = globalResultTypeKey(record);
-      if (!resultTypeKey || !resultTypes.includes(resultTypeKey)) continue;
+      if (!resultTypeKey || !resultTypes.includes(resultTypeKey)) return false;
     }
     if (tuned.length) {
       const tunedKey = record.variant === 'fine-tuned' ? 'tuned' : 'not-tuned';
-      if (!tuned.includes(tunedKey)) continue;
+      if (!tuned.includes(tunedKey)) return false;
     }
     if (optimizedValues.length) {
       const optimizedKey = record.optimized ? 'optimized' : 'not-optimized';
-      if (!optimizedValues.includes(optimizedKey)) continue;
+      if (!optimizedValues.includes(optimizedKey)) return false;
     }
-    if (platforms.length && !(record.platform && platforms.includes(record.platform))) continue;
-    if (datasets.length && !datasets.includes(record.dataset)) continue;
-
-    const key = `${record.model}|||${record.machine_learning_task ?? ''}`;
-    const entryStats =
-      stats.get(key) ??
-      {
-        model: record.model,
-        machineLearningTask: record.machine_learning_task,
-        categoryTotals: { f1: 0, map: 0, precision: 0, recall: 0 },
-        categoryCounts: { f1: 0, map: 0, precision: 0, recall: 0 },
-        appearances: 0,
-        datasets: new Set<string>(),
-        fineTunedDatasets: new Set<string>(),
-        variants: new Set<'zero-shot' | 'fine-tuned'>(),
-        optimized: false,
-        datasetDetails: [] as GlobalLeaderboardDatasetDetail[],
-      };
-    (Object.keys(record.percentiles) as (keyof CategoryPercentiles)[]).forEach((categoryKey) => {
-      const value = record.percentiles[categoryKey];
-      if (value == null) return;
-      entryStats.categoryTotals[categoryKey] += value;
-      entryStats.categoryCounts[categoryKey] += 1;
-    });
-    entryStats.appearances += 1;
-    entryStats.datasets.add(record.dataset);
-    if (record.variant === 'fine-tuned') entryStats.fineTunedDatasets.add(record.dataset);
-    if (record.variant) entryStats.variants.add(record.variant);
-    if (record.optimized) entryStats.optimized = true;
-    entryStats.datasetDetails.push({
-      dataset: record.dataset,
-      percentiles: record.percentiles,
-      scores: record.scores,
-      variant: record.variant,
-      optimized: record.optimized,
-      platform: record.platform,
-      splitBreakdown: record.splitBreakdown,
-      datasetConfig: record.datasetConfig,
+    if (platforms.length && !(record.platform && platforms.includes(record.platform))) return false;
+    if (datasets.length && !datasets.includes(record.dataset)) return false;
+    return true;
+  });
+}
+
+// A model name alone isn't a stable comparison unit — the same model can appear under multiple
+// ML tasks (e.g. a VLM benchmarked on both classification and detection), and averaging ranks
+// across unrelated tasks would be meaningless. Every "model" selectable for comparison is really
+// a (model, task) pair, identified by this composite id.
+function modelOptionId(model: string, task: string | null): string {
+  return `${model}|||${task ?? ''}`;
+}
+
+// One row per distinct (model, ML task) pair, used to populate the "select models to compare"
+// list. No minimum-appearances gate here — unlike the averaged-percentile table this replaces, a
+// model with a single dataset result is still a valid (if narrow) thing to compare.
+export interface ModelOption {
+  id: string;
+  model: string;
+  task: string | null;
+  datasetCount: number;
+}
+
+export function computeModelOptions(
+  records: GlobalPerformanceRecord[],
+  options: GlobalLeaderboardFilterOptions = {}
+): ModelOption[] {
+  const byId = new Map<string, { model: string; task: string | null; datasets: Set<string> }>();
+  for (const record of filterGlobalRecords(records, options)) {
+    const task = record.machine_learning_task;
+    const id = modelOptionId(record.model, task);
+    const entry = byId.get(id) ?? { model: record.model, task, datasets: new Set<string>() };
+    entry.datasets.add(record.dataset);
+    byId.set(id, entry);
+  }
+  return Array.from(byId.entries())
+    .map(([id, data]) => ({
+      id,
+      model: data.model,
+      task: data.task,
+      datasetCount: data.datasets.size,
+    }))
+    .sort((a, b) => b.datasetCount - a.datasetCount || a.model.localeCompare(b.model) || (a.task ?? '').localeCompare(b.task ?? ''));
+}
+
+export interface ModelComparisonDatasetRank {
+  dataset: string;
+  score: number;
+  rank: number;
+}
+
+export interface ModelComparisonEntry {
+  id: string;
+  model: string;
+  task: string | null;
+  avgRank: number;
+  datasetRanks: ModelComparisonDatasetRank[];
+}
+
+export interface ModelComparisonResult {
+  datasets: string[];
+  entries: ModelComparisonEntry[];
+}
+
+// Ranks the selected (model, task) pairs against each other (not against the whole leaderboard),
+// using only the datasets every selection has a score for on the chosen metric — the
+// "intersection of all datasets that they have results on". Each dataset contributes one rank
+// (1 = best, ties share a rank, competition-style: 1,2,2,4) which is then averaged per selection.
+// `filtered` must already have GlobalLeaderboardFilterOptions applied — callers that need to
+// compute this for several metrics at once (computeModelComparisons) filter records once and
+// reuse the result instead of re-scanning the full record set per metric.
+function computeModelComparisonFromFiltered(
+  filtered: GlobalPerformanceRecord[],
+  selectedIds: string[],
+  metric: keyof CategoryScores
+): ModelComparisonResult {
+  if (selectedIds.length < 2) return { datasets: [], entries: [] };
+
+  const idSet = new Set(selectedIds);
+  const infoById = new Map<string, { model: string; task: string | null }>();
+  const scoreByIdDataset = new Map<string, Map<string, number>>(selectedIds.map((id) => [id, new Map()]));
+  for (const record of filtered) {
+    const id = modelOptionId(record.model, record.machine_learning_task);
+    if (!idSet.has(id)) continue;
+    infoById.set(id, { model: record.model, task: record.machine_learning_task });
+    const byDataset = scoreByIdDataset.get(id)!;
+    const score = record.scores[metric];
+    if (score == null) continue;
+    const existing = byDataset.get(record.dataset);
+    if (existing == null || score > existing) byDataset.set(record.dataset, score);
+  }
+
+  const perIdDatasets: Set<string>[] = selectedIds.map((id) => new Set(scoreByIdDataset.get(id)!.keys()));
+  const [firstIdDatasets, ...restIdDatasets] = perIdDatasets;
+  const datasets: string[] = Array.from(firstIdDatasets)
+    .filter((dataset) => restIdDatasets.every((idDatasets) => idDatasets.has(dataset)))
+    .sort();
+  if (!datasets.length) return { datasets: [], entries: [] };
+
+  const rankTotals = new Map<string, number>(selectedIds.map((id) => [id, 0]));
+  const datasetRanksById = new Map<string, ModelComparisonDatasetRank[]>(selectedIds.map((id) => [id, []]));
+
+  for (const dataset of datasets) {
+    const scored = selectedIds
+      .map((id) => ({ id, score: scoreByIdDataset.get(id)!.get(dataset)! }))
+      .sort((a, b) => b.score - a.score);
+
+    let rank = 1;
+    scored.forEach((item, index) => {
+      if (index > 0 && item.score < scored[index - 1].score) rank = index + 1;
+      rankTotals.set(item.id, rankTotals.get(item.id)! + rank);
+      datasetRanksById.get(item.id)!.push({ dataset, score: item.score, rank });
     });
-    stats.set(key, entryStats);
   }
 
-  const average = (total: number, count: number) => (count > 0 ? total / count : null);
-
-  return Array.from(stats.values())
-    .filter((entryStats) => entryStats.appearances >= minAppearances)
-    .map((entryStats) => ({
-      model: entryStats.model,
-      machineLearningTask: entryStats.machineLearningTask,
-      avgF1Percentile: average(entryStats.categoryTotals.f1, entryStats.categoryCounts.f1),
-      avgMapPercentile: average(entryStats.categoryTotals.map, entryStats.categoryCounts.map),
-      avgPrecisionPercentile: average(entryStats.categoryTotals.precision, entryStats.categoryCounts.precision),
-      avgRecallPercentile: average(entryStats.categoryTotals.recall, entryStats.categoryCounts.recall),
-      appearances: entryStats.appearances,
-      datasets: Array.from(entryStats.datasets).sort(),
-      fineTunedDatasets: Array.from(entryStats.fineTunedDatasets).sort(),
-      resultType: formatResultTypeLabel(entryStats.variants, entryStats.optimized),
-      optimized: entryStats.optimized,
-      datasetDetails: entryStats.datasetDetails.sort((a, b) => a.dataset.localeCompare(b.dataset)),
-    }))
-    .sort((a, b) => (b.avgMapPercentile ?? b.avgF1Percentile ?? 0) - (a.avgMapPercentile ?? a.avgF1Percentile ?? 0));
+  const entries: ModelComparisonEntry[] = selectedIds
+    .map((id) => {
+      const info = infoById.get(id)!;
+      return {
+        id,
+        model: info.model,
+        task: info.task,
+        avgRank: rankTotals.get(id)! / datasets.length,
+        datasetRanks: datasetRanksById.get(id)!,
+      };
+    })
+    .sort((a, b) => a.avgRank - b.avgRank);
+
+  return { datasets, entries };
+}
+
+export function computeModelComparison(
+  records: GlobalPerformanceRecord[],
+  selectedIds: string[],
+  metric: keyof CategoryScores,
+  options: GlobalLeaderboardFilterOptions = {}
+): ModelComparisonResult {
+  return computeModelComparisonFromFiltered(filterGlobalRecords(records, options), selectedIds, metric);
+}
+
+const COMPARISON_CATEGORY_KEYS: (keyof CategoryScores)[] = ['f1', 'map', 'precision', 'recall'];
+
+export type CategoryComparisons = Record<keyof CategoryScores, ModelComparisonResult>;
+
+// All four category rankings for the selected (model, task) pairs in one call — the leaderboard
+// table shows f1/precision/recall as sortable columns, and the detail modal needs all four
+// (including map) to annotate a model's per-dataset rows. Records are filtered once and reused
+// across all four metrics rather than re-scanning the full record set per metric.
+export function computeModelComparisons(
+  records: GlobalPerformanceRecord[],
+  selectedIds: string[],
+  options: GlobalLeaderboardFilterOptions = {}
+): CategoryComparisons {
+  const filtered = filterGlobalRecords(records, options);
+  const entries = COMPARISON_CATEGORY_KEYS.map(
+    (key) => [key, computeModelComparisonFromFiltered(filtered, selectedIds, key)] as const
+  );
+  return Object.fromEntries(entries) as CategoryComparisons;
+}
+
+export interface ModelComparisonDatasetDetail {
+  dataset: string;
+  scores: CategoryScores;
+  ranks: CategoryScores;
+  variant: 'zero-shot' | 'fine-tuned' | null;
+  optimized: boolean;
+  platform: string | null;
+  splitBreakdown: string | null;
+  datasetConfig: string | null;
+  notes: string | null;
+}
+
+export interface ModelComparisonDetail {
+  id: string;
+  model: string;
+  task: string | null;
+  totalModels: number;
+  metric: keyof CategoryScores;
+  datasets: ModelComparisonDatasetDetail[];
+}
+
+// Detail behind the "view details" modal: only the datasets that actually fed the selection's avg
+// rank for `metric` (the currently active/sorted column) — not every dataset the model has ever
+// reported a result on. Each dataset's other category scores are still shown, ranked against the
+// same selected group, when that category also happens to be available.
+//
+// The score shown for every category always comes from the same underlying record — the "winner"
+// for the active metric — and each category's rank is computed against that record's own score,
+// not against a different (possibly higher-scoring) record the model happens to have for that
+// category. Otherwise a zero-shot run's score could be displayed next to a rank a fine-tuned run
+// actually earned.
+export function computeModelComparisonDetail(
+  records: GlobalPerformanceRecord[],
+  id: string,
+  selectedIds: string[],
+  metric: keyof CategoryScores,
+  comparisons: CategoryComparisons,
+  options: GlobalLeaderboardFilterOptions = {}
+): ModelComparisonDetail {
+  const currentEntry = comparisons[metric].entries.find((entry) => entry.id === id);
+  const contributingDatasets = currentEntry ? currentEntry.datasetRanks.map((d) => d.dataset) : [];
+  const contributingSet = new Set(contributingDatasets);
+
+  const model = currentEntry?.model ?? id;
+  const task = currentEntry?.task ?? null;
+
+  const recordsByDataset = new Map<string, GlobalPerformanceRecord[]>();
+  for (const record of filterGlobalRecords(records, options)) {
+    if (modelOptionId(record.model, record.machine_learning_task) !== id || !contributingSet.has(record.dataset)) continue;
+    const list = recordsByDataset.get(record.dataset) ?? [];
+    list.push(record);
+    recordsByDataset.set(record.dataset, list);
+  }
+
+  // Per-category, per-id, per-dataset score lookup built once instead of re-deriving it (via
+  // nested .find() calls) inside the per-dataset loop below.
+  const scoreLookupByCategory = new Map<keyof CategoryScores, Map<string, Map<string, number>>>(
+    COMPARISON_CATEGORY_KEYS.map((key) => [
+      key,
+      new Map(
+        comparisons[key].entries.map((entry) => [entry.id, new Map(entry.datasetRanks.map((d) => [d.dataset, d.score]))])
+      ),
+    ])
+  );
+
+  const datasets: ModelComparisonDatasetDetail[] = contributingDatasets
+    .map((dataset) => {
+      const candidates = recordsByDataset.get(dataset) ?? [];
+      const winner = candidates.reduce<GlobalPerformanceRecord | null>((best, record) => {
+        const score = record.scores[metric];
+        if (score == null) return best;
+        if (!best || (best.scores[metric] ?? -Infinity) < score) return record;
+        return best;
+      }, null);
+      if (!winner) return null;
+
+      // Rank each of the winner's own category scores against the other selected ids' best score
+      // for that category on this dataset — substituting the winner's score in place of this id's
+      // own (possibly different-record) entry in comparisons[key], so the rank always matches the
+      // score displayed alongside it.
+      const ranks: CategoryScores = { f1: null, map: null, precision: null, recall: null };
+      COMPARISON_CATEGORY_KEYS.forEach((key) => {
+        const winnerScore = winner.scores[key];
+        if (winnerScore == null) return;
+
+        const byId = scoreLookupByCategory.get(key)!;
+        const scored = selectedIds
+          .map((otherId) => {
+            if (otherId === id) return { id, score: winnerScore };
+            const score = byId.get(otherId)?.get(dataset);
+            return score == null ? null : { id: otherId, score };
+          })
+          .filter((item): item is { id: string; score: number } => item != null)
+          .sort((a, b) => b.score - a.score);
+
+        let rank = 1;
+        scored.forEach((item, index) => {
+          if (index > 0 && item.score < scored[index - 1].score) rank = index + 1;
+          if (item.id === id) ranks[key] = rank;
+        });
+      });
+
+      return {
+        dataset,
+        scores: winner.scores,
+        ranks,
+        variant: winner.variant,
+        optimized: winner.optimized,
+        platform: winner.platform,
+        splitBreakdown: winner.splitBreakdown,
+        datasetConfig: winner.datasetConfig,
+        notes: winner.notes,
+      };
+    })
+    .filter((row): row is ModelComparisonDatasetDetail => row != null)
+    .sort((a, b) => a.dataset.localeCompare(b.dataset));
+
+  return { id, model, task, totalModels: selectedIds.length, metric, datasets };
 }
 
 export function useGlobalPerformance(): {
diff --git a/src/pages/leaderboard/index.module.css b/src/pages/leaderboard/index.module.css
index 9d1bef1..7879b13 100644
--- a/src/pages/leaderboard/index.module.css
+++ b/src/pages/leaderboard/index.module.css
@@ -153,10 +153,6 @@
   color: var(--agml-muted);
 }
 
-.benchmarkHeader {
-  margin-bottom: 1.5rem;
-}
-
 .tableHeading {
   font-size: 0.95rem;
   font-weight: 600;
@@ -164,42 +160,12 @@
   margin: 0;
 }
 
-.tableSection {
-  margin-bottom: 2rem;
-}
-
-.tableSection:last-child {
-  margin-bottom: 0;
-}
-
 .tableWrap {
   border: 1px solid var(--agml-border);
   border-radius: 8px;
   overflow-x: auto;
 }
 
-.tableCaption {
-  padding: 0.65rem 1rem;
-  border-bottom: 1px solid var(--agml-border);
-  background: var(--agml-surface-soft);
-  border-radius: 8px 8px 0 0;
-}
-
-.sectionHeading {
-  font-size: 0.8rem;
-  font-weight: 600;
-  text-transform: uppercase;
-  letter-spacing: 0.04em;
-  color: var(--agml-text);
-  margin: 0;
-}
-
-.sectionDescription {
-  font-size: 0.78rem;
-  color: var(--agml-muted);
-  margin: 0.35rem 0 0;
-}
-
 .leaderboardTable {
   width: 100%;
   min-width: 760px;
@@ -276,66 +242,212 @@
   text-underline-offset: 3px;
 }
 
-.metricCell {
+.metricEmpty {
+  color: var(--agml-muted);
+}
+
+.pagination {
   display: flex;
   align-items: center;
+  justify-content: center;
+  gap: 1rem;
+  margin-top: 1.5rem;
+}
+
+.paginationButton {
+  border: 1px solid var(--agml-border-strong);
+  background: var(--agml-surface-strong);
+  border-radius: 999px;
+  padding: 0.5rem 1.1rem;
+  font-size: 0.85rem;
+  color: var(--agml-text);
+  cursor: pointer;
+}
+
+.paginationButton:disabled {
+  opacity: 0.5;
+  cursor: not-allowed;
+}
+
+.paginationStatus {
+  font-size: 0.82rem;
+  color: var(--agml-muted);
+}
+
+.compareLayout {
+  display: flex;
+  gap: 1.5rem;
+  align-items: flex-start;
+}
+
+.selectorPanel {
+  width: 320px;
+  flex-shrink: 0;
+  border: 1px solid var(--agml-border);
+  border-radius: 8px;
+  overflow: hidden;
+}
+
+.selectorHeader {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
   gap: 0.5rem;
-  min-width: 110px;
+  padding: 0.65rem 1rem;
+  border-bottom: 1px solid var(--agml-border);
+  background: var(--agml-surface-soft);
 }
 
-.metricBarTrack {
-  flex: 1;
-  height: 6px;
-  border-radius: 3px;
+.selectorHeader .filterGroupLabel {
+  margin: 0;
+}
+
+.selectorHeaderRight {
+  display: flex;
+  align-items: center;
+  gap: 0.6rem;
+}
+
+.modelList {
+  max-height: 640px;
+  overflow-y: auto;
+}
+
+.modelSectionHeading {
+  margin: 0;
+  padding: 0.5rem 1rem 0.3rem;
+  font-size: 0.68rem;
+  font-weight: 600;
+  text-transform: uppercase;
+  letter-spacing: 0.04em;
+  color: var(--agml-muted);
   background: var(--agml-surface-soft);
-  overflow: hidden;
+  border-top: 1px solid var(--agml-border);
 }
 
-.metricBarFill {
-  height: 100%;
-  background: var(--ifm-color-primary);
+.modelSectionHeading:first-child {
+  border-top: none;
+}
+
+.modelRow {
+  display: flex;
+  align-items: center;
+  gap: 0.6rem;
+  padding: 0.55rem 1rem;
+  border-top: 1px solid var(--agml-border);
+  cursor: pointer;
+  font-size: 0.85rem;
+}
+
+.modelRow:first-child {
+  border-top: none;
+}
+
+.modelRow:hover {
+  background: var(--agml-accent-soft);
 }
 
-.metricLabel {
+.modelRowSelected {
+  background: var(--agml-accent-soft);
+}
+
+.modelRowName {
+  flex: 1;
   font-family: var(--ifm-code-font-family);
+  color: var(--agml-text);
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
+.modelRowMeta {
   font-size: 0.72rem;
   color: var(--agml-muted);
+  white-space: nowrap;
   flex-shrink: 0;
 }
 
-.metricEmpty {
-  color: var(--agml-muted);
+.comparePanel {
+  flex: 1;
+  min-width: 0;
 }
 
-.pagination {
+.compareHeader {
   display: flex;
   align-items: center;
-  justify-content: center;
+  justify-content: space-between;
   gap: 1rem;
+  flex-wrap: wrap;
+  margin-bottom: 0.75rem;
+}
+
+.compareTable .tableRow {
+  grid-template-columns: minmax(40px, 0.3fr) minmax(180px, 1.8fr) minmax(110px, 1fr) minmax(110px, 1fr) minmax(110px, 1fr);
+}
+
+.rankPosition {
+  font-family: var(--ifm-code-font-family);
+  color: var(--agml-muted);
+}
+
+.avgRankValue {
+  font-family: var(--ifm-code-font-family);
+  font-weight: 600;
+  color: var(--agml-text);
+}
+
+.matrixSection {
   margin-top: 1.5rem;
 }
 
-.paginationButton {
+.matrixHeader {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  gap: 1rem;
+  flex-wrap: wrap;
+  margin-bottom: 0.75rem;
+}
+
+.matrixHeader .filterGroupLabel {
+  margin: 0;
+}
+
+.breakdownMetricTabs {
+  display: flex;
+  gap: 0.4rem;
+}
+
+.breakdownMetricButton {
   border: 1px solid var(--agml-border-strong);
   background: var(--agml-surface-strong);
   border-radius: 999px;
-  padding: 0.5rem 1.1rem;
-  font-size: 0.85rem;
+  padding: 0.3rem 0.75rem;
+  font-size: 0.72rem;
   color: var(--agml-text);
   cursor: pointer;
 }
 
-.paginationButton:disabled {
-  opacity: 0.5;
-  cursor: not-allowed;
+.breakdownMetricButtonActive {
+  background: var(--ifm-color-primary);
+  border-color: var(--ifm-color-primary);
+  color: var(--agml-on-accent, #fff);
 }
 
-.paginationStatus {
-  font-size: 0.82rem;
+.matrixRank {
   color: var(--agml-muted);
 }
 
 @media (max-width: 996px) {
+  .compareLayout {
+    flex-direction: column;
+  }
+
+  .selectorPanel {
+    width: 100%;
+  }
+
+
   .body {
     flex-direction: column;
   }
diff --git a/src/pages/leaderboard/index.tsx b/src/pages/leaderboard/index.tsx
index a4a940d..4a30c0e 100644
--- a/src/pages/leaderboard/index.tsx
+++ b/src/pages/leaderboard/index.tsx
@@ -1,61 +1,40 @@
 import { useDeferredValue, useEffect, useMemo, useState } from 'react';
 import Layout from '@theme/Layout';
 import {
-  computeGlobalLeaderboard,
+  computeModelComparisonDetail,
+  computeModelComparisons,
+  computeModelOptions,
+  METRIC_LABELS,
   useGlobalPerformance,
 } from '../../lib/performance';
-import type { GlobalLeaderboardEntry } from '../../lib/performance';
+import type { CategoryComparisons, ModelOption } from '../../lib/performance';
 import { MultiSelectDropdown } from '../../components/MultiSelectDropdown';
 import { LeaderboardDetailModal } from '../../components/LeaderboardDetailModal';
 import { useDatasets } from '../../lib/datasets';
+import { toDisplayLabel } from '../../lib/labelOverrides';
 import styles from './index.module.css';
 
-const MIN_APPEARANCES = 3;
-const PAGE_SIZE = 25;
-
-// The global leaderboard only ever sorts/displays F1, precision, and recall — mAP stays fully
-// computed (GlobalLeaderboardEntry.avgMapPercentile, computeGlobalLeaderboard, etc.) since
-// per-dataset detail views and future benchmarks still use it, it's just not a table column here.
 type SortField = 'f1' | 'precision' | 'recall';
 
-function toLabel(value: string) {
-  return value.replace(/_/g, ' ');
-}
+const SORT_FIELDS: { value: SortField; label: string }[] = [
+  { value: 'f1', label: 'Avg F1 rank' },
+  { value: 'precision', label: 'Avg Precision rank' },
+  { value: 'recall', label: 'Avg Recall rank' },
+];
 
-// 1st, 2nd, 3rd, 4th, ..., 11th-13th stay "th" (the exception the mod-10 rule alone gets wrong).
-function ordinal(value: number): string {
-  const rounded = Math.round(value);
-  const mod100 = rounded % 100;
-  if (mod100 >= 11 && mod100 <= 13) return `${rounded}th`;
-  switch (rounded % 10) {
-    case 1:
-      return `${rounded}st`;
-    case 2:
-      return `${rounded}nd`;
-    case 3:
-      return `${rounded}rd`;
-    default:
-      return `${rounded}th`;
-  }
-}
+const MATRIX_PAGE_SIZE = 15;
+const MODEL_LIST_PAGE_SIZE = 15;
 
 function shortTaskLabel(value: string) {
   const lower = value.toLowerCase();
   if (lower.includes('classif')) return 'Classification';
   if (lower.includes('detect')) return 'Detection';
   if (lower.includes('segment')) return 'Segmentation';
-  return toLabel(value);
+  return toDisplayLabel(value);
 }
 
-function percentileValue(entry: GlobalLeaderboardEntry, field: SortField) {
-  switch (field) {
-    case 'f1':
-      return entry.avgF1Percentile;
-    case 'precision':
-      return entry.avgPrecisionPercentile;
-    case 'recall':
-      return entry.avgRecallPercentile;
-  }
+function formatAvgRank(value: number): string {
+  return Number.isInteger(value) ? String(value) : value.toFixed(2);
 }
 
 function CheckboxFilterGroup({
@@ -91,141 +70,6 @@ function CheckboxFilterGroup({
   );
 }
 
-function PercentileCell({ value }: { value: number | null }) {
-  if (value == null) {
-    return <span className={styles.metricEmpty}>—</span>;
-  }
-  return (
-    <div className={styles.metricCell}>
-      <div className={styles.metricBarTrack}>
-        <div className={styles.metricBarFill} style={{ width: `${value}%` }} />
-      </div>
-      <span className={styles.metricLabel}>{ordinal(value)}</span>
-    </div>
-  );
-}
-
-function LeaderboardSection({
-  title,
-  entries,
-  sortBy,
-  setSortBy,
-  page,
-  setPage,
-  onSelect,
-  description,
-}: {
-  title: string;
-  entries: GlobalLeaderboardEntry[];
-  sortBy: SortField;
-  setSortBy: (field: SortField) => void;
-  page: number;
-  setPage: (updater: (current: number) => number) => void;
-  onSelect: (key: string) => void;
-  description?: string;
-}) {
-  const pageCount = Math.max(1, Math.ceil(entries.length / PAGE_SIZE));
-  const currentPage = Math.min(page, pageCount);
-  const paged = entries.slice((currentPage - 1) * PAGE_SIZE, currentPage * PAGE_SIZE);
-  const sortHeaderClass = (field: SortField) => `${styles.sortHeader} ${sortBy === field ? styles.sortHeaderActive : ''}`;
-
-  return (
-    <div className={styles.tableSection}>
-      <div className={styles.tableWrap}>
-        <div className={styles.tableCaption}>
-          <h3 className={styles.sectionHeading}>{title}</h3>
-          {description && <p className={styles.sectionDescription}>{description}</p>}
-        </div>
-        {entries.length === 0 ? (
-          <p className={styles.status}>No models have at least {MIN_APPEARANCES} dataset appearances for the current filters.</p>
-        ) : (
-          <div className={styles.leaderboardTable} role="table">
-            <div className={styles.tableRow} role="row">
-              <span role="columnheader">Model</span>
-              <span role="columnheader">
-                <button type="button" className={sortHeaderClass('f1')} onClick={() => setSortBy('f1')}>
-                  <span>Avg F1 pctl</span>
-                  <span>{sortBy === 'f1' ? '▼' : '▾'}</span>
-                </button>
-              </span>
-              <span role="columnheader">
-                <button type="button" className={sortHeaderClass('precision')} onClick={() => setSortBy('precision')}>
-                  <span>Avg Precision pctl</span>
-                  <span>{sortBy === 'precision' ? '▼' : '▾'}</span>
-                </button>
-              </span>
-              <span role="columnheader">
-                <button type="button" className={sortHeaderClass('recall')} onClick={() => setSortBy('recall')}>
-                  <span>Avg Recall pctl</span>
-                  <span>{sortBy === 'recall' ? '▼' : '▾'}</span>
-                </button>
-              </span>
-              <span role="columnheader">Result type</span>
-              <span role="columnheader"># Results</span>
-            </div>
-            {paged.map((entry) => {
-              const key = `${entry.model}|||${entry.machineLearningTask ?? ''}`;
-              return (
-                <div
-                  key={key}
-                  role="row"
-                  tabIndex={0}
-                  className={`${styles.tableRow} ${styles.clickableRow}`}
-                  onClick={() => onSelect(key)}
-                  onKeyDown={(event) => {
-                    if (event.key === 'Enter' || event.key === ' ') {
-                      event.preventDefault();
-                      onSelect(key);
-                    }
-                  }}
-                >
-                  <span role="cell">
-                    <span className={styles.modelName}>{entry.model}</span>
-                  </span>
-                  <span role="cell">
-                    <PercentileCell value={entry.avgF1Percentile} />
-                  </span>
-                  <span role="cell">
-                    <PercentileCell value={entry.avgPrecisionPercentile} />
-                  </span>
-                  <span role="cell">
-                    <PercentileCell value={entry.avgRecallPercentile} />
-                  </span>
-                  <span role="cell">{entry.resultType}</span>
-                  <span role="cell">{entry.appearances}</span>
-                </div>
-              );
-            })}
-          </div>
-        )}
-      </div>
-      {entries.length > 0 && pageCount > 1 && (
-        <div className={styles.pagination}>
-          <button
-            type="button"
-            className={styles.paginationButton}
-            onClick={() => setPage((p) => Math.max(1, p - 1))}
-            disabled={currentPage === 1}
-          >
-            Previous
-          </button>
-          <span className={styles.paginationStatus}>
-            Page {currentPage} of {pageCount}
-          </span>
-          <button
-            type="button"
-            className={styles.paginationButton}
-            onClick={() => setPage((p) => Math.min(pageCount, p + 1))}
-            disabled={currentPage === pageCount}
-          >
-            Next
-          </button>
-        </div>
-      )}
-    </div>
-  );
-}
-
 export default function GlobalLeaderboardPage() {
   const { data: records, loading, error } = useGlobalPerformance();
   const { data: datasets } = useDatasets();
@@ -239,6 +83,9 @@ export default function GlobalLeaderboardPage() {
   const [optimizedValues, setOptimizedValues] = useState<('optimized' | 'not-optimized')[]>([]);
   const [platforms, setPlatforms] = useState<string[]>([]);
   const [sortBy, setSortBy] = useState<SortField>('f1');
+  const [breakdownMetric, setBreakdownMetric] = useState<SortField>('f1');
+  const [selectedIds, setSelectedIds] = useState<string[]>([]);
+  const [detailId, setDetailId] = useState<string | null>(null);
 
   const { cropTypeOptions, mlTaskOptions, platformOptions } = useMemo(() => {
     const cropSet = new Set<string>();
@@ -297,74 +144,173 @@ export default function GlobalLeaderboardPage() {
     setPlatforms([]);
   };
 
-  const leaderboard = useMemo(
-    () =>
-      computeGlobalLeaderboard(records, {
-        cropTypes,
-        mlTasks,
-        tuned,
-        optimizedValues,
-        platforms,
-        datasets: agTaskDatasets,
-        minAppearances: MIN_APPEARANCES,
-      }),
-    [records, cropTypes, mlTasks, tuned, optimizedValues, platforms, agTaskDatasets]
+  const filterOptions = useMemo(
+    () => ({
+      cropTypes,
+      mlTasks,
+      tuned,
+      optimizedValues,
+      platforms,
+      datasets: agTaskDatasets,
+    }),
+    [cropTypes, mlTasks, tuned, optimizedValues, platforms, agTaskDatasets]
   );
 
-  const searched = useMemo(() => {
+  const modelOptions = useMemo(() => computeModelOptions(records, filterOptions), [records, filterOptions]);
+
+  // Filters can shrink the eligible model pool out from under an existing selection — drop any
+  // selected model that no longer has a matching result rather than silently comparing stale data.
+  useEffect(() => {
+    setSelectedIds((current) => {
+      const available = new Set(modelOptions.map((option) => option.id));
+      const next = current.filter((id) => available.has(id));
+      return next.length === current.length ? current : next;
+    });
+  }, [modelOptions]);
+
+  const visibleModelOptions = useMemo(() => {
     const q = searchDeferred.trim().toLowerCase();
-    if (!q) return leaderboard;
-    return leaderboard.filter(
-      (entry) =>
-        entry.model.toLowerCase().includes(q) ||
-        (entry.machineLearningTask ?? '').toLowerCase().includes(q) ||
-        entry.datasets.some((dataset) => dataset.toLowerCase().includes(q))
-    );
-  }, [leaderboard, searchDeferred]);
-
-  const sorted = useMemo(
-    () =>
-      [...searched].sort((a, b) => (percentileValue(b, sortBy) ?? -1) - (percentileValue(a, sortBy) ?? -1)),
-    [searched, sortBy]
-  );
+    if (!q) return modelOptions;
+    return modelOptions.filter((option) => option.model.toLowerCase().includes(q));
+  }, [modelOptions, searchDeferred]);
+
+  // Group the selector list by CV task instead of labeling each row individually — within a
+  // group, datasetCount-desc / name order from computeModelOptions is preserved.
+  const modelSections = useMemo(() => {
+    const groups = new Map<string, { task: string | null; options: typeof visibleModelOptions }>();
+    for (const option of visibleModelOptions) {
+      const key = option.task ?? '';
+      const group = groups.get(key) ?? { task: option.task, options: [] };
+      group.options.push(option);
+      groups.set(key, group);
+    }
+    return Array.from(groups.values()).sort((a, b) => {
+      if (a.task == null) return 1;
+      if (b.task == null) return -1;
+      return shortTaskLabel(a.task).localeCompare(shortTaskLabel(b.task));
+    });
+  }, [visibleModelOptions]);
+
+  // Flatten sections into a single row list (headers + models) so the whole selector can be
+  // paginated without an unbounded DOM — the model list has no other cap now that the old
+  // leaderboard's PAGE_SIZE-based pagination is gone.
+  const modelListRows = useMemo(() => {
+    const rows: ({ kind: 'header'; key: string; task: string | null } | { kind: 'option'; key: string; option: ModelOption })[] = [];
+    for (const section of modelSections) {
+      rows.push({ kind: 'header', key: `header-${section.task ?? '__none__'}`, task: section.task });
+      for (const option of section.options) rows.push({ kind: 'option', key: option.id, option });
+    }
+    return rows;
+  }, [modelSections]);
+
+  const [modelListPage, setModelListPage] = useState(0);
+  useEffect(() => {
+    setModelListPage(0);
+  }, [modelListRows]);
+
+  const modelListTotalPages = Math.max(1, Math.ceil(modelListRows.length / MODEL_LIST_PAGE_SIZE));
+  const modelListPageStart = modelListPage * MODEL_LIST_PAGE_SIZE;
+  const modelListPageRows = useMemo(() => {
+    const slice = modelListRows.slice(modelListPageStart, modelListPageStart + MODEL_LIST_PAGE_SIZE);
+    // A section can span a page boundary — if this page starts mid-section, repeat that
+    // section's header so the task grouping is never lost.
+    if (slice.length && slice[0].kind === 'option') {
+      for (let i = modelListPageStart - 1; i >= 0; i--) {
+        const prior = modelListRows[i];
+        if (prior.kind === 'header') return [prior, ...slice];
+      }
+    }
+    return slice;
+  }, [modelListRows, modelListPageStart]);
+
+  const toggleModel = (id: string) => {
+    setSelectedIds((current) => (current.includes(id) ? current.filter((v) => v !== id) : [...current, id]));
+  };
 
-  const classificationEntries = useMemo(
-    () => sorted.filter((entry) => shortTaskLabel(entry.machineLearningTask ?? '') === 'Classification'),
-    [sorted]
+  const comparisons: CategoryComparisons = useMemo(
+    () => computeModelComparisons(records, selectedIds, filterOptions),
+    [records, selectedIds, filterOptions]
   );
-  const detectionEntries = useMemo(
-    () => sorted.filter((entry) => shortTaskLabel(entry.machineLearningTask ?? '') === 'Detection'),
-    [sorted]
+
+  const modelOptionById = useMemo(() => new Map(modelOptions.map((option) => [option.id, option])), [modelOptions]);
+
+  const comparisonRows = useMemo(() => {
+    const avgRankById = (field: SortField) => new Map(comparisons[field].entries.map((entry) => [entry.id, entry.avgRank]));
+    const f1ById = avgRankById('f1');
+    const precisionById = avgRankById('precision');
+    const recallById = avgRankById('recall');
+
+    return selectedIds
+      .map((id) => {
+        const option = modelOptionById.get(id);
+        return {
+          id,
+          model: option?.model ?? id,
+          task: option?.task ?? null,
+          f1: f1ById.get(id) ?? null,
+          precision: precisionById.get(id) ?? null,
+          recall: recallById.get(id) ?? null,
+        };
+      })
+      .sort((a, b) => {
+        const rankA = a[sortBy];
+        const rankB = b[sortBy];
+        if (rankA == null && rankB == null) return 0;
+        if (rankA == null) return 1;
+        if (rankB == null) return -1;
+        return rankA - rankB;
+      });
+  }, [selectedIds, comparisons, sortBy, modelOptionById]);
+
+  const hasAnyComparison = SORT_FIELDS.some((field) => comparisons[field.value].datasets.length > 0);
+  const currentDatasetCount = comparisons[sortBy].datasets.length;
+
+  const breakdownComparison = comparisons[breakdownMetric];
+  // Column order stays fixed (alphabetical by model) regardless of which metric is selected —
+  // breakdownComparison.entries is sorted by avgRank, which would otherwise reshuffle columns
+  // every time the metric changes.
+  const breakdownEntries = useMemo(
+    () => [...breakdownComparison.entries].sort((a, b) => a.model.localeCompare(b.model)),
+    [breakdownComparison]
   );
+  const matrixGridStyle = {
+    gridTemplateColumns: `minmax(160px, 1.6fr) repeat(${breakdownEntries.length}, minmax(100px, 1fr))`,
+  };
 
-  const [classificationPage, setClassificationPage] = useState(1);
-  const [detectionPage, setDetectionPage] = useState(1);
+  const [matrixPage, setMatrixPage] = useState(0);
+  // A metric switch or a change to the selected/filtered models can shrink or reorder the dataset
+  // list out from under the current page — go back to the first page rather than risk stranding
+  // the user on a now out-of-range or unrelated page.
   useEffect(() => {
-    setClassificationPage(1);
-    setDetectionPage(1);
-  }, [cropTypes, mlTasks, agTasks, tuned, optimizedValues, platforms, searchDeferred]);
-
-  const [selectedKey, setSelectedKey] = useState<string | null>(null);
-  const selectedEntry = useMemo(
-    () => sorted.find((entry) => `${entry.model}|||${entry.machineLearningTask ?? ''}` === selectedKey) ?? null,
-    [sorted, selectedKey]
+    setMatrixPage(0);
+  }, [breakdownComparison]);
+  const matrixTotalPages = Math.max(1, Math.ceil(breakdownComparison.datasets.length / MATRIX_PAGE_SIZE));
+  const matrixPageStart = matrixPage * MATRIX_PAGE_SIZE;
+  const matrixPageDatasets = breakdownComparison.datasets.slice(matrixPageStart, matrixPageStart + MATRIX_PAGE_SIZE);
+
+  const detail = useMemo(
+    () =>
+      detailId && selectedIds.includes(detailId)
+        ? computeModelComparisonDetail(records, detailId, selectedIds, sortBy, comparisons, filterOptions)
+        : null,
+    [records, detailId, selectedIds, sortBy, comparisons, filterOptions]
   );
 
   return (
-    <Layout title="Model Leaderboard" description="Global model leaderboard aggregated across AgML dataset benchmarks.">
+    <Layout title="Model Leaderboard" description="Compare model rankings across shared AgML dataset benchmarks.">
       <div className={styles.page}>
-        <LeaderboardDetailModal entry={selectedEntry} open={selectedEntry != null} onClose={() => setSelectedKey(null)} />
+        <LeaderboardDetailModal detail={detail} open={detail != null} onClose={() => setDetailId(null)} />
 
         <div className={styles.toolbar}>
           <input
             type="search"
-            placeholder="search models, datasets, tasks..."
+            placeholder="search models..."
             value={search}
             onChange={(event) => setSearch(event.target.value)}
             className={styles.searchInput}
           />
           <div className={styles.toolbarRight}>
-            <span className={styles.resultCount}>{sorted.length.toLocaleString()} models</span>
+            <span className={styles.resultCount}>{modelOptions.length.toLocaleString()} models</span>
             {hasActiveFilters && (
               <button type="button" className={styles.clearButton} onClick={clearFilters}>
                 Clear filters
@@ -377,10 +323,10 @@ export default function GlobalLeaderboardPage() {
           <aside className={styles.sidebar}>
             <CheckboxFilterGroup label="CV Task" options={mlTaskOptions} selected={mlTasks} onToggle={(value) => toggleValue(setMlTasks, value)} formatOption={shortTaskLabel} />
             <div className={styles.filterGroup}>
-              <MultiSelectDropdown label="Agricultural Task" options={agTaskOptions} selected={agTasks} onToggle={(value) => toggleValue(setAgTasks, value)} formatOption={toLabel} />
+              <MultiSelectDropdown label="Agricultural Task" options={agTaskOptions} selected={agTasks} onToggle={(value) => toggleValue(setAgTasks, value)} formatOption={toDisplayLabel} />
             </div>
             <div className={styles.filterGroup}>
-              <MultiSelectDropdown label="Crop" options={cropTypeOptions} selected={cropTypes} onToggle={(value) => toggleValue(setCropTypes, value)} formatOption={toLabel} />
+              <MultiSelectDropdown label="Crop" options={cropTypeOptions} selected={cropTypes} onToggle={(value) => toggleValue(setCropTypes, value)} formatOption={toDisplayLabel} />
             </div>
             <CheckboxFilterGroup
               label="Tuned"
@@ -396,45 +342,240 @@ export default function GlobalLeaderboardPage() {
               onToggle={(value) => toggleValue(setOptimizedValues, value as 'optimized' | 'not-optimized')}
               formatOption={(value) => (value === 'optimized' ? 'Optimized' : 'Not optimized')}
             />
-            <CheckboxFilterGroup label="Platform" options={platformOptions} selected={platforms} onToggle={(value) => toggleValue(setPlatforms, value)} formatOption={toLabel} />
+            <CheckboxFilterGroup label="Platform" options={platformOptions} selected={platforms} onToggle={(value) => toggleValue(setPlatforms, value)} formatOption={toDisplayLabel} />
           </aside>
 
           <section className={styles.results}>
             {loading && <p className={styles.status}>Loading leaderboard…</p>}
             {error && <p className={styles.status}>Error: {error.message}</p>}
-            {!loading && !error && sorted.length === 0 && (
-              <p className={styles.status}>
-                No models have at least {MIN_APPEARANCES} dataset appearances for the current filters.
-              </p>
-            )}
-            {!loading && !error && sorted.length > 0 && (
-              <>
-                <div className={styles.benchmarkHeader}>
-                  <h2 className={styles.tableHeading}>Benchmark 1: Naive approach</h2>
+            {!loading && !error && (
+              <div className={styles.compareLayout}>
+                <div className={styles.selectorPanel}>
+                  <div className={styles.selectorHeader}>
+                    <p className={styles.filterGroupLabel}>Select models to compare</p>
+                    <div className={styles.selectorHeaderRight}>
+                      <span className={styles.resultCount}>{selectedIds.length} selected</span>
+                      {selectedIds.length > 0 && (
+                        <button type="button" className={styles.clearButton} onClick={() => setSelectedIds([])}>
+                          Clear
+                        </button>
+                      )}
+                    </div>
+                  </div>
+                  <div className={styles.modelList}>
+                    {visibleModelOptions.length === 0 ? (
+                      <p className={styles.status}>No models match the current filters.</p>
+                    ) : (
+                      modelListPageRows.map((row) =>
+                        row.kind === 'header' ? (
+                          <p key={row.key} className={styles.modelSectionHeading}>
+                            {row.task ? shortTaskLabel(row.task) : 'Other'}
+                          </p>
+                        ) : (
+                          <label
+                            key={row.key}
+                            className={`${styles.modelRow} ${selectedIds.includes(row.option.id) ? styles.modelRowSelected : ''}`}
+                          >
+                            <input
+                              type="checkbox"
+                              className={styles.checkbox}
+                              checked={selectedIds.includes(row.option.id)}
+                              onChange={() => toggleModel(row.option.id)}
+                            />
+                            <span className={styles.modelRowName}>{row.option.model}</span>
+                            <span className={styles.modelRowMeta}>
+                              {row.option.datasetCount} dataset{row.option.datasetCount === 1 ? '' : 's'}
+                            </span>
+                          </label>
+                        )
+                      )
+                    )}
+                  </div>
+                  {modelListTotalPages > 1 && (
+                    <div className={styles.pagination}>
+                      <button
+                        type="button"
+                        className={styles.paginationButton}
+                        onClick={() => setModelListPage((page) => Math.max(0, page - 1))}
+                        disabled={modelListPage === 0}
+                      >
+                        Previous
+                      </button>
+                      <span className={styles.paginationStatus}>
+                        Page {modelListPage + 1} of {modelListTotalPages} · {visibleModelOptions.length} models
+                      </span>
+                      <button
+                        type="button"
+                        className={styles.paginationButton}
+                        onClick={() => setModelListPage((page) => Math.min(modelListTotalPages - 1, page + 1))}
+                        disabled={modelListPage >= modelListTotalPages - 1}
+                      >
+                        Next
+                      </button>
+                    </div>
+                  )}
                 </div>
 
-                <LeaderboardSection
-                  title="Classification"
-                  description="Models are prompted with the class names and asked to return the best-matching label as JSON."
-                  entries={classificationEntries}
-                  sortBy={sortBy}
-                  setSortBy={setSortBy}
-                  page={classificationPage}
-                  setPage={setClassificationPage}
-                  onSelect={setSelectedKey}
-                />
-
-                <LeaderboardSection
-                  title="Detection"
-                  description="Models return bounding boxes as JSON, matched to ground truth at IoU ≥ 0.5; F1/Precision/Recall are reported at that threshold since no model gives a confidence score to rank by."
-                  entries={detectionEntries}
-                  sortBy={sortBy}
-                  setSortBy={setSortBy}
-                  page={detectionPage}
-                  setPage={setDetectionPage}
-                  onSelect={setSelectedKey}
-                />
-              </>
+                <div className={styles.comparePanel}>
+                  {selectedIds.length < 2 ? (
+                    <p className={styles.status}>Select at least 2 models on the left to compare their average ranking.</p>
+                  ) : !hasAnyComparison ? (
+                    <p className={styles.status}>
+                      The selected models have no F1, Precision, or Recall results on a shared dataset under the current
+                      filters.
+                    </p>
+                  ) : (
+                    <>
+                      <div className={styles.compareHeader}>
+                        <h2 className={styles.tableHeading}>Average ranking</h2>
+                        <span className={styles.resultCount}>
+                          {currentDatasetCount} shared dataset{currentDatasetCount === 1 ? '' : 's'}
+                        </span>
+                      </div>
+
+                      <div className={styles.tableWrap}>
+                        <div className={`${styles.leaderboardTable} ${styles.compareTable}`} role="table">
+                          <div className={styles.tableRow} role="row">
+                            <span role="columnheader">#</span>
+                            <span role="columnheader">Model</span>
+                            {SORT_FIELDS.map((field) => (
+                              <span role="columnheader" key={field.value}>
+                                <button
+                                  type="button"
+                                  className={`${styles.sortHeader} ${sortBy === field.value ? styles.sortHeaderActive : ''}`}
+                                  onClick={() => setSortBy(field.value)}
+                                >
+                                  <span>{field.label}</span>
+                                  <span>{sortBy === field.value ? '▲' : '▾'}</span>
+                                </button>
+                              </span>
+                            ))}
+                          </div>
+                          {comparisonRows.map((row, index) => {
+                            const hasDetail = row[sortBy] != null;
+                            return (
+                            <div
+                              key={row.id}
+                              role="row"
+                              tabIndex={hasDetail ? 0 : -1}
+                              className={`${styles.tableRow} ${hasDetail ? styles.clickableRow : ''}`}
+                              onClick={hasDetail ? () => setDetailId(row.id) : undefined}
+                              onKeyDown={
+                                hasDetail
+                                  ? (event) => {
+                                      if (event.key === 'Enter' || event.key === ' ') {
+                                        event.preventDefault();
+                                        setDetailId(row.id);
+                                      }
+                                    }
+                                  : undefined
+                              }
+                            >
+                              <span role="cell" className={styles.rankPosition}>
+                                {index + 1}
+                              </span>
+                              <span role="cell">
+                                <span className={styles.modelName}>
+                                  {row.model}
+                                  {row.task ? ` · ${shortTaskLabel(row.task)}` : ''}
+                                </span>
+                              </span>
+                              {SORT_FIELDS.map((field) => (
+                                <span role="cell" className={styles.avgRankValue} key={field.value}>
+                                  {row[field.value] == null ? <span className={styles.metricEmpty}>—</span> : formatAvgRank(row[field.value]!)}
+                                </span>
+                              ))}
+                            </div>
+                            );
+                          })}
+                        </div>
+                      </div>
+
+                      <div className={styles.matrixSection}>
+                        <div className={styles.matrixHeader}>
+                          <p className={styles.filterGroupLabel}>Per-dataset breakdown</p>
+                          <div className={styles.breakdownMetricTabs} role="tablist" aria-label="Breakdown score">
+                            {SORT_FIELDS.map((field) => (
+                              <button
+                                key={field.value}
+                                type="button"
+                                role="tab"
+                                aria-selected={breakdownMetric === field.value}
+                                className={`${styles.breakdownMetricButton} ${breakdownMetric === field.value ? styles.breakdownMetricButtonActive : ''}`}
+                                onClick={() => setBreakdownMetric(field.value)}
+                              >
+                                {METRIC_LABELS[field.value]}
+                              </button>
+                            ))}
+                          </div>
+                        </div>
+
+                        {breakdownComparison.datasets.length === 0 ? (
+                          <p className={styles.status}>
+                            The selected models have no shared {METRIC_LABELS[breakdownMetric]} results to break down.
+                          </p>
+                        ) : (
+                          <>
+                            <div className={styles.tableWrap}>
+                              <div className={styles.leaderboardTable} role="table">
+                                <div className={styles.tableRow} role="row" style={matrixGridStyle}>
+                                  <span role="columnheader">Dataset</span>
+                                  {breakdownEntries.map((entry) => (
+                                    <span role="columnheader" key={entry.id}>
+                                      {entry.model}
+                                      {entry.task ? ` · ${shortTaskLabel(entry.task)}` : ''}
+                                    </span>
+                                  ))}
+                                </div>
+                                {matrixPageDatasets.map((dataset, pageIndex) => {
+                                  const datasetIndex = matrixPageStart + pageIndex;
+                                  return (
+                                    <div className={styles.tableRow} role="row" style={matrixGridStyle} key={dataset}>
+                                      <span role="cell">{toDisplayLabel(dataset)}</span>
+                                      {breakdownEntries.map((entry) => {
+                                        const cell = entry.datasetRanks[datasetIndex];
+                                        return (
+                                          <span role="cell" key={entry.id}>
+                                            {cell.score.toFixed(3)} <span className={styles.matrixRank}>(#{cell.rank})</span>
+                                          </span>
+                                        );
+                                      })}
+                                    </div>
+                                  );
+                                })}
+                              </div>
+                            </div>
+                            {matrixTotalPages > 1 && (
+                              <div className={styles.pagination}>
+                                <button
+                                  type="button"
+                                  className={styles.paginationButton}
+                                  onClick={() => setMatrixPage((page) => Math.max(0, page - 1))}
+                                  disabled={matrixPage === 0}
+                                >
+                                  Previous
+                                </button>
+                                <span className={styles.paginationStatus}>
+                                  Datasets {matrixPageStart + 1}–{Math.min(matrixPageStart + MATRIX_PAGE_SIZE, breakdownComparison.datasets.length)} of{' '}
+                                  {breakdownComparison.datasets.length}
+                                </span>
+                                <button
+                                  type="button"
+                                  className={styles.paginationButton}
+                                  onClick={() => setMatrixPage((page) => Math.min(matrixTotalPages - 1, page + 1))}
+                                  disabled={matrixPage >= matrixTotalPages - 1}
+                                >
+                                  Next
+                                </button>
+                              </div>
+                            )}
+                          </>
+                        )}
+                      </div>
+                    </>
+                  )}
+                </div>
+              </div>
             )}
           </section>
         </div>
diff --git a/static/data/datasets.json b/static/data/datasets.json
index 4f90191..60cfc5f 100644
--- a/static/data/datasets.json
+++ b/static/data/datasets.json
@@ -25,8 +25,8 @@
   {
     "name": "iNatAg",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46,8 +46,8 @@
   {
     "name": "iNatAg-mini",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67,8 +67,8 @@
   {
     "name": "iNatAg-mini/abelmoschus_esculentus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88,8 +88,8 @@
   {
     "name": "iNatAg-mini/abelmoschus_manihot",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109,8 +109,8 @@
   {
     "name": "iNatAg-mini/abelmoschus_moschatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -130,8 +130,8 @@
   {
     "name": "iNatAg-mini/abies_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -151,8 +151,8 @@
   {
     "name": "iNatAg-mini/abies_amabilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -172,8 +172,8 @@
   {
     "name": "iNatAg-mini/abies_balsamea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -193,8 +193,8 @@
   {
     "name": "iNatAg-mini/abies_concolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -214,8 +214,8 @@
   {
     "name": "iNatAg-mini/abies_pindrow",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -235,8 +235,8 @@
   {
     "name": "iNatAg-mini/abroma_augustum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -256,8 +256,8 @@
   {
     "name": "iNatAg-mini/abrus_pecatorius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -277,8 +277,8 @@
   {
     "name": "iNatAg-mini/abrus_precatorius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -298,8 +298,8 @@
   {
     "name": "iNatAg-mini/abutilon_theophrasti",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -319,8 +319,8 @@
   {
     "name": "iNatAg-mini/acacia_abyssinica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -340,8 +340,8 @@
   {
     "name": "iNatAg-mini/acacia_acradenia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -361,8 +361,8 @@
   {
     "name": "iNatAg-mini/acacia_acuminata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -382,8 +382,8 @@
   {
     "name": "iNatAg-mini/acacia_ampliceps",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -403,8 +403,8 @@
   {
     "name": "iNatAg-mini/acacia_anceps",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -424,8 +424,8 @@
   {
     "name": "iNatAg-mini/acacia_ancistrocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -445,8 +445,8 @@
   {
     "name": "iNatAg-mini/acacia_aneura",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -466,8 +466,8 @@
   {
     "name": "iNatAg-mini/acacia_angustissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -487,8 +487,8 @@
   {
     "name": "iNatAg-mini/acacia_ataxacantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -508,8 +508,8 @@
   {
     "name": "iNatAg-mini/acacia_aulacocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -529,8 +529,8 @@
   {
     "name": "iNatAg-mini/acacia_auriculiformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -550,8 +550,8 @@
   {
     "name": "iNatAg-mini/acacia_bidwillii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -571,8 +571,8 @@
   {
     "name": "iNatAg-mini/acacia_brachystachya",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -592,8 +592,8 @@
   {
     "name": "iNatAg-mini/acacia_brevispica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -613,8 +613,8 @@
   {
     "name": "iNatAg-mini/acacia_burkei",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -634,8 +634,8 @@
   {
     "name": "iNatAg-mini/acacia_caffra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -655,8 +655,8 @@
   {
     "name": "iNatAg-mini/acacia_cambagei",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -676,8 +676,8 @@
   {
     "name": "iNatAg-mini/acacia_catechu",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -697,8 +697,8 @@
   {
     "name": "iNatAg-mini/acacia_catenulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -718,8 +718,8 @@
   {
     "name": "iNatAg-mini/acacia_caven",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -739,8 +739,8 @@
   {
     "name": "iNatAg-mini/acacia_cincinnata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -760,8 +760,8 @@
   {
     "name": "iNatAg-mini/acacia_coriacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -781,8 +781,8 @@
   {
     "name": "iNatAg-mini/acacia_cowleana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -802,8 +802,8 @@
   {
     "name": "iNatAg-mini/acacia_crassicarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -823,8 +823,8 @@
   {
     "name": "iNatAg-mini/acacia_cyclops",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -844,8 +844,8 @@
   {
     "name": "iNatAg-mini/acacia_cyperophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -865,8 +865,8 @@
   {
     "name": "iNatAg-mini/acacia_dealbata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -886,8 +886,8 @@
   {
     "name": "iNatAg-mini/acacia_deanei",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -907,8 +907,8 @@
   {
     "name": "iNatAg-mini/acacia_decurrens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -928,8 +928,8 @@
   {
     "name": "iNatAg-mini/acacia_difficilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -949,8 +949,8 @@
   {
     "name": "iNatAg-mini/acacia_doratoxylon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -970,8 +970,8 @@
   {
     "name": "iNatAg-mini/acacia_ehrenbergiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -991,8 +991,8 @@
   {
     "name": "iNatAg-mini/acacia_erioloba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1012,8 +1012,8 @@
   {
     "name": "iNatAg-mini/acacia_estrophiolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1033,8 +1033,8 @@
   {
     "name": "iNatAg-mini/acacia_excelsa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1054,8 +1054,8 @@
   {
     "name": "iNatAg-mini/acacia_falciformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1075,8 +1075,8 @@
   {
     "name": "iNatAg-mini/acacia_farnesiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1096,8 +1096,8 @@
   {
     "name": "iNatAg-mini/acacia_fasciculifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1117,8 +1117,8 @@
   {
     "name": "iNatAg-mini/acacia_flavescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1138,8 +1138,8 @@
   {
     "name": "iNatAg-mini/acacia_georginae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1159,8 +1159,8 @@
   {
     "name": "iNatAg-mini/acacia_gerrardii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1180,8 +1180,8 @@
   {
     "name": "iNatAg-mini/acacia_glaucocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1201,8 +1201,8 @@
   {
     "name": "iNatAg-mini/acacia_gourmaensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1222,8 +1222,8 @@
   {
     "name": "iNatAg-mini/acacia_harpophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1243,8 +1243,8 @@
   {
     "name": "iNatAg-mini/acacia_holosericea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1264,8 +1264,8 @@
   {
     "name": "iNatAg-mini/acacia_irrorata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1285,8 +1285,8 @@
   {
     "name": "iNatAg-mini/acacia_ixiophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1306,8 +1306,8 @@
   {
     "name": "iNatAg-mini/acacia_karroo",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1327,8 +1327,8 @@
   {
     "name": "iNatAg-mini/acacia_koa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1348,8 +1348,8 @@
   {
     "name": "iNatAg-mini/acacia_leptocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1369,8 +1369,8 @@
   {
     "name": "iNatAg-mini/acacia_leucophloea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1390,8 +1390,8 @@
   {
     "name": "iNatAg-mini/acacia_ligulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1411,8 +1411,8 @@
   {
     "name": "iNatAg-mini/acacia_maidenii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1432,8 +1432,8 @@
   {
     "name": "iNatAg-mini/acacia_mangium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1453,8 +1453,8 @@
   {
     "name": "iNatAg-mini/acacia_mearnsii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1474,8 +1474,8 @@
   {
     "name": "iNatAg-mini/acacia_melanoxylon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1495,8 +1495,8 @@
   {
     "name": "iNatAg-mini/acacia_mellifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1516,8 +1516,8 @@
   {
     "name": "iNatAg-mini/acacia_murrayana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1537,8 +1537,8 @@
   {
     "name": "iNatAg-mini/acacia_neriifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1558,8 +1558,8 @@
   {
     "name": "iNatAg-mini/acacia_nigrescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1579,8 +1579,8 @@
   {
     "name": "iNatAg-mini/acacia_nilotica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1600,8 +1600,8 @@
   {
     "name": "iNatAg-mini/acacia_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1621,8 +1621,8 @@
   {
     "name": "iNatAg-mini/acacia_oraria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1642,8 +1642,8 @@
   {
     "name": "iNatAg-mini/acacia_oswaldii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1663,8 +1663,8 @@
   {
     "name": "iNatAg-mini/acacia_pachycarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1684,8 +1684,8 @@
   {
     "name": "iNatAg-mini/acacia_papyrocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1705,8 +1705,8 @@
   {
     "name": "iNatAg-mini/acacia_paradoxa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1726,8 +1726,8 @@
   {
     "name": "iNatAg-mini/acacia_pendula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1747,8 +1747,8 @@
   {
     "name": "iNatAg-mini/acacia_peuce",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1768,8 +1768,8 @@
   {
     "name": "iNatAg-mini/acacia_podalyriifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1789,8 +1789,8 @@
   {
     "name": "iNatAg-mini/acacia_polyacantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1810,8 +1810,8 @@
   {
     "name": "iNatAg-mini/acacia_polystachya",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1831,8 +1831,8 @@
   {
     "name": "iNatAg-mini/acacia_pruinocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1852,8 +1852,8 @@
   {
     "name": "iNatAg-mini/acacia_pycnantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1873,8 +1873,8 @@
   {
     "name": "iNatAg-mini/acacia_salicina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1894,8 +1894,8 @@
   {
     "name": "iNatAg-mini/acacia_saligna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1915,8 +1915,8 @@
   {
     "name": "iNatAg-mini/acacia_sclerosperma",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1936,8 +1936,8 @@
   {
     "name": "iNatAg-mini/acacia_senegal",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1957,8 +1957,8 @@
   {
     "name": "iNatAg-mini/acacia_seyal",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1978,8 +1978,8 @@
   {
     "name": "iNatAg-mini/acacia_shirleyi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -1999,8 +1999,8 @@
   {
     "name": "iNatAg-mini/acacia_sieberiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2020,8 +2020,8 @@
   {
     "name": "iNatAg-mini/acacia_silvestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2041,8 +2041,8 @@
   {
     "name": "iNatAg-mini/acacia_simsii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2062,8 +2062,8 @@
   {
     "name": "iNatAg-mini/acacia_stenophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2083,8 +2083,8 @@
   {
     "name": "iNatAg-mini/acacia_tetragonophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2104,8 +2104,8 @@
   {
     "name": "iNatAg-mini/acacia_tortilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2125,8 +2125,8 @@
   {
     "name": "iNatAg-mini/acacia_torulosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2146,8 +2146,8 @@
   {
     "name": "iNatAg-mini/acacia_trachycarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2167,8 +2167,8 @@
   {
     "name": "iNatAg-mini/acacia_victoriae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2188,8 +2188,8 @@
   {
     "name": "iNatAg-mini/acaena_novae-zelandiae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2209,8 +2209,8 @@
   {
     "name": "iNatAg-mini/acalypha_rhomboidea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2230,8 +2230,8 @@
   {
     "name": "iNatAg-mini/acalypha_virginica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2251,8 +2251,8 @@
   {
     "name": "iNatAg-mini/acanthosicyos_horridus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2272,8 +2272,8 @@
   {
     "name": "iNatAg-mini/acanthosicyos_naudinianus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2293,8 +2293,8 @@
   {
     "name": "iNatAg-mini/acanthospermum_hispidum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2314,8 +2314,8 @@
   {
     "name": "iNatAg-mini/acanthus_ilicifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2335,8 +2335,8 @@
   {
     "name": "iNatAg-mini/acanthus_mollis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2356,8 +2356,8 @@
   {
     "name": "iNatAg-mini/acca_sellowiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2377,8 +2377,8 @@
   {
     "name": "iNatAg-mini/acer_caesium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2398,8 +2398,8 @@
   {
     "name": "iNatAg-mini/acer_campestre",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2419,8 +2419,8 @@
   {
     "name": "iNatAg-mini/acer_platanoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2440,8 +2440,8 @@
   {
     "name": "iNatAg-mini/acer_pseudoplatanus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2461,8 +2461,8 @@
   {
     "name": "iNatAg-mini/acer_saccharum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2482,8 +2482,8 @@
   {
     "name": "iNatAg-mini/achillea_fragrantissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2503,8 +2503,8 @@
   {
     "name": "iNatAg-mini/achillea_millefolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2524,8 +2524,8 @@
   {
     "name": "iNatAg-mini/achillea_ptarmica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2545,8 +2545,8 @@
   {
     "name": "iNatAg-mini/achnatherum_pekinense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2566,8 +2566,8 @@
   {
     "name": "iNatAg-mini/achyranthes_aspera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2587,8 +2587,8 @@
   {
     "name": "iNatAg-mini/acmena_smithii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2608,8 +2608,8 @@
   {
     "name": "iNatAg-mini/aconitum_napellus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2629,8 +2629,8 @@
   {
     "name": "iNatAg-mini/acorus_calamus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2650,8 +2650,8 @@
   {
     "name": "iNatAg-mini/acrocarpus_fraxinifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2671,8 +2671,8 @@
   {
     "name": "iNatAg-mini/acrocomia_aculeata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2692,8 +2692,8 @@
   {
     "name": "iNatAg-mini/acrocomia_totai",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2713,8 +2713,8 @@
   {
     "name": "iNatAg-mini/actaea_racemosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2734,8 +2734,8 @@
   {
     "name": "iNatAg-mini/actinidia_arguta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2755,8 +2755,8 @@
   {
     "name": "iNatAg-mini/actinidia_chinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2776,8 +2776,8 @@
   {
     "name": "iNatAg-mini/adansonia_digitata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2797,8 +2797,8 @@
   {
     "name": "iNatAg-mini/adansonia_grandidieri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2818,8 +2818,8 @@
   {
     "name": "iNatAg-mini/adansonia_gregorii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2839,8 +2839,8 @@
   {
     "name": "iNatAg-mini/adenanthera_pavonina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2860,8 +2860,8 @@
   {
     "name": "iNatAg-mini/adesmia_bicolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2881,8 +2881,8 @@
   {
     "name": "iNatAg-mini/adesmia_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2902,8 +2902,8 @@
   {
     "name": "iNatAg-mini/adesmia_punctata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2923,8 +2923,8 @@
   {
     "name": "iNatAg-mini/adesmia_securigerifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2944,8 +2944,8 @@
   {
     "name": "iNatAg-mini/adiantum_capillus-veneris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2965,8 +2965,8 @@
   {
     "name": "iNatAg-mini/adina_cordifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -2986,8 +2986,8 @@
   {
     "name": "iNatAg-mini/adonis_annua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3007,8 +3007,8 @@
   {
     "name": "iNatAg-mini/adonis_vernalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3028,8 +3028,8 @@
   {
     "name": "iNatAg-mini/aechmea_magdalenae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3049,8 +3049,8 @@
   {
     "name": "iNatAg-mini/aegiceras_corniculatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3070,8 +3070,8 @@
   {
     "name": "iNatAg-mini/aegilops_biuncialis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3091,8 +3091,8 @@
   {
     "name": "iNatAg-mini/aegilops_cylindrica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3112,8 +3112,8 @@
   {
     "name": "iNatAg-mini/aegilops_geniculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3133,8 +3133,8 @@
   {
     "name": "iNatAg-mini/aegilops_triuncialis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3154,8 +3154,8 @@
   {
     "name": "iNatAg-mini/aegle_marmelos",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3175,8 +3175,8 @@
   {
     "name": "iNatAg-mini/aegopodium_podagraria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3196,8 +3196,8 @@
   {
     "name": "iNatAg-mini/aeschynomene_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3217,8 +3217,8 @@
   {
     "name": "iNatAg-mini/aeschynomene_brasiliana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3238,8 +3238,8 @@
   {
     "name": "iNatAg-mini/aeschynomene_falcata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3259,8 +3259,8 @@
   {
     "name": "iNatAg-mini/aeschynomene_histrix",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3280,8 +3280,8 @@
   {
     "name": "iNatAg-mini/aeschynomene_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3301,8 +3301,8 @@
   {
     "name": "iNatAg-mini/aeschynomene_villosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3322,8 +3322,8 @@
   {
     "name": "iNatAg-mini/aesculus_hippocastanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3343,8 +3343,8 @@
   {
     "name": "iNatAg-mini/aesculus_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3364,8 +3364,8 @@
   {
     "name": "iNatAg-mini/aethusa_cynapium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3385,8 +3385,8 @@
   {
     "name": "iNatAg-mini/afzelia_africana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3406,8 +3406,8 @@
   {
     "name": "iNatAg-mini/afzelia_quanzensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3427,8 +3427,8 @@
   {
     "name": "iNatAg-mini/agathis_australis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3448,8 +3448,8 @@
   {
     "name": "iNatAg-mini/agathis_dammara",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3469,8 +3469,8 @@
   {
     "name": "iNatAg-mini/agathis_macrophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3490,8 +3490,8 @@
   {
     "name": "iNatAg-mini/agathis_microstachya",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3511,8 +3511,8 @@
   {
     "name": "iNatAg-mini/agathis_robusta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3532,8 +3532,8 @@
   {
     "name": "iNatAg-mini/agave_fourcroydes",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3553,8 +3553,8 @@
   {
     "name": "iNatAg-mini/agave_lecheguilla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3574,8 +3574,8 @@
   {
     "name": "iNatAg-mini/agave_sisalana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3595,8 +3595,8 @@
   {
     "name": "iNatAg-mini/ageratum_conyzoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3616,8 +3616,8 @@
   {
     "name": "iNatAg-mini/agrimonia_eupatoria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3637,8 +3637,8 @@
   {
     "name": "iNatAg-mini/agrimonia_gryposepala",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3658,8 +3658,8 @@
   {
     "name": "iNatAg-mini/agrimonia_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3679,8 +3679,8 @@
   {
     "name": "iNatAg-mini/agropyron_cristatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3700,8 +3700,8 @@
   {
     "name": "iNatAg-mini/agropyron_dasyanthum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3721,8 +3721,8 @@
   {
     "name": "iNatAg-mini/agropyron_desertorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3742,8 +3742,8 @@
   {
     "name": "iNatAg-mini/agropyron_scabrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3763,8 +3763,8 @@
   {
     "name": "iNatAg-mini/agrostemma_githago",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3784,8 +3784,8 @@
   {
     "name": "iNatAg-mini/agrostis_canina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3805,8 +3805,8 @@
   {
     "name": "iNatAg-mini/agrostis_capillaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3826,8 +3826,8 @@
   {
     "name": "iNatAg-mini/agrostis_gigantea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3847,8 +3847,8 @@
   {
     "name": "iNatAg-mini/agrostis_stolonifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3868,8 +3868,8 @@
   {
     "name": "iNatAg-mini/agrostis_tenuis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3889,8 +3889,8 @@
   {
     "name": "iNatAg-mini/ailanthus_altissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3910,8 +3910,8 @@
   {
     "name": "iNatAg-mini/ailanthus_excelsa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3931,8 +3931,8 @@
   {
     "name": "iNatAg-mini/aiphanes_aculeata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3952,8 +3952,8 @@
   {
     "name": "iNatAg-mini/aira_caryophyllea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3973,8 +3973,8 @@
   {
     "name": "iNatAg-mini/ajuga_genevensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3994,8 +3994,8 @@
   {
     "name": "iNatAg-mini/ajuga_reptans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4015,8 +4015,8 @@
   {
     "name": "iNatAg-mini/alania_cunninghamii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4036,8 +4036,8 @@
   {
     "name": "iNatAg-mini/albizia_adianthifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4057,8 +4057,8 @@
   {
     "name": "iNatAg-mini/albizia_amara",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4078,8 +4078,8 @@
   {
     "name": "iNatAg-mini/albizia_chinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4099,8 +4099,8 @@
   {
     "name": "iNatAg-mini/albizia_falcataria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4120,8 +4120,8 @@
   {
     "name": "iNatAg-mini/albizia_harveyi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4141,8 +4141,8 @@
   {
     "name": "iNatAg-mini/albizia_lebbeck",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4162,8 +4162,8 @@
   {
     "name": "iNatAg-mini/albizia_lophantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4183,8 +4183,8 @@
   {
     "name": "iNatAg-mini/albizia_lucida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4204,8 +4204,8 @@
   {
     "name": "iNatAg-mini/albizia_odoratissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4225,8 +4225,8 @@
   {
     "name": "iNatAg-mini/albizia_procera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4246,8 +4246,8 @@
   {
     "name": "iNatAg-mini/alcea_rosea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4267,8 +4267,8 @@
   {
     "name": "iNatAg-mini/alchemilla_monticola",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4288,8 +4288,8 @@
   {
     "name": "iNatAg-mini/alchemilla_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4309,8 +4309,8 @@
   {
     "name": "iNatAg-mini/alchemilla_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4330,8 +4330,8 @@
   {
     "name": "iNatAg-mini/alchemilla_xanthochlora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4351,8 +4351,8 @@
   {
     "name": "iNatAg-mini/aleurites_fordii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4372,8 +4372,8 @@
   {
     "name": "iNatAg-mini/aleurites_moluccana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4393,8 +4393,8 @@
   {
     "name": "iNatAg-mini/alisma_gramineum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4414,8 +4414,8 @@
   {
     "name": "iNatAg-mini/alisma_lanceolatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4435,8 +4435,8 @@
   {
     "name": "iNatAg-mini/alisma_plantago-aquatica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4456,8 +4456,8 @@
   {
     "name": "iNatAg-mini/alkanna_tinctoria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4477,8 +4477,8 @@
   {
     "name": "iNatAg-mini/alliaria_petiolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4498,8 +4498,8 @@
   {
     "name": "iNatAg-mini/allionia_incarnata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4519,8 +4519,8 @@
   {
     "name": "iNatAg-mini/allium_ampeloprasum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4540,8 +4540,8 @@
   {
     "name": "iNatAg-mini/allium_canadense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4561,8 +4561,8 @@
   {
     "name": "iNatAg-mini/allium_cepa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4582,8 +4582,8 @@
   {
     "name": "iNatAg-mini/allium_chinense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4603,8 +4603,8 @@
   {
     "name": "iNatAg-mini/allium_fistulosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4624,8 +4624,8 @@
   {
     "name": "iNatAg-mini/allium_paniculatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4645,8 +4645,8 @@
   {
     "name": "iNatAg-mini/allium_sativum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4666,8 +4666,8 @@
   {
     "name": "iNatAg-mini/allium_schoenoprasum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4687,8 +4687,8 @@
   {
     "name": "iNatAg-mini/allium_triquetrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4708,8 +4708,8 @@
   {
     "name": "iNatAg-mini/allium_tuberosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4729,8 +4729,8 @@
   {
     "name": "iNatAg-mini/allium_ursinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4750,8 +4750,8 @@
   {
     "name": "iNatAg-mini/allocasuarina_campestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4771,8 +4771,8 @@
   {
     "name": "iNatAg-mini/allocasuarina_decaisneana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4792,8 +4792,8 @@
   {
     "name": "iNatAg-mini/allocasuarina_fraseriana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4813,8 +4813,8 @@
   {
     "name": "iNatAg-mini/allocasuarina_huegeliana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4834,8 +4834,8 @@
   {
     "name": "iNatAg-mini/allocasuarina_littoralis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4855,8 +4855,8 @@
   {
     "name": "iNatAg-mini/allocasuarina_luehmannii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4876,8 +4876,8 @@
   {
     "name": "iNatAg-mini/allocasuarina_torulosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4897,8 +4897,8 @@
   {
     "name": "iNatAg-mini/alloteropsis_semialata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4918,8 +4918,8 @@
   {
     "name": "iNatAg-mini/alnus_acuminata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4939,8 +4939,8 @@
   {
     "name": "iNatAg-mini/alnus_glutinosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4960,8 +4960,8 @@
   {
     "name": "iNatAg-mini/alnus_japonica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -4981,8 +4981,8 @@
   {
     "name": "iNatAg-mini/alnus_maritima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5002,8 +5002,8 @@
   {
     "name": "iNatAg-mini/alnus_nepalensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5023,8 +5023,8 @@
   {
     "name": "iNatAg-mini/alnus_rubra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5044,8 +5044,8 @@
   {
     "name": "iNatAg-mini/alocasia_macrorrhizos",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5065,8 +5065,8 @@
   {
     "name": "iNatAg-mini/aloe_arborescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5086,8 +5086,8 @@
   {
     "name": "iNatAg-mini/aloe_barbadensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5107,8 +5107,8 @@
   {
     "name": "iNatAg-mini/aloe_ferox",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5128,8 +5128,8 @@
   {
     "name": "iNatAg-mini/aloe_perryi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5149,8 +5149,8 @@
   {
     "name": "iNatAg-mini/alopecurus_arundinaceus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5170,8 +5170,8 @@
   {
     "name": "iNatAg-mini/alopecurus_carolinianus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5191,8 +5191,8 @@
   {
     "name": "iNatAg-mini/alopecurus_geniculatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5212,8 +5212,8 @@
   {
     "name": "iNatAg-mini/alopecurus_myosuroides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5233,8 +5233,8 @@
   {
     "name": "iNatAg-mini/alopecurus_pratensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5254,8 +5254,8 @@
   {
     "name": "iNatAg-mini/alopecurus_rendlei",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5275,8 +5275,8 @@
   {
     "name": "iNatAg-mini/aloysia_triphylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5296,8 +5296,8 @@
   {
     "name": "iNatAg-mini/alphitonia_excelsa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5317,8 +5317,8 @@
   {
     "name": "iNatAg-mini/alpinia_galanga",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5338,8 +5338,8 @@
   {
     "name": "iNatAg-mini/alstonia_scholaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5359,8 +5359,8 @@
   {
     "name": "iNatAg-mini/alternanthera_pungens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5380,8 +5380,8 @@
   {
     "name": "iNatAg-mini/althaea_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5401,8 +5401,8 @@
   {
     "name": "iNatAg-mini/altingia_excelsa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5422,8 +5422,8 @@
   {
     "name": "iNatAg-mini/alysicarpus_monilifer",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5443,8 +5443,8 @@
   {
     "name": "iNatAg-mini/alysicarpus_ovalifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5464,8 +5464,8 @@
   {
     "name": "iNatAg-mini/alysicarpus_rugosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5485,8 +5485,8 @@
   {
     "name": "iNatAg-mini/alysicarpus_vaginalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5506,8 +5506,8 @@
   {
     "name": "iNatAg-mini/alyssum_desertorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5527,8 +5527,8 @@
   {
     "name": "iNatAg-mini/amaranthus_albus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5548,8 +5548,8 @@
   {
     "name": "iNatAg-mini/amaranthus_blitum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5569,8 +5569,8 @@
   {
     "name": "iNatAg-mini/amaranthus_caudatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5590,8 +5590,8 @@
   {
     "name": "iNatAg-mini/amaranthus_cruentus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5611,8 +5611,8 @@
   {
     "name": "iNatAg-mini/amaranthus_dubius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5632,8 +5632,8 @@
   {
     "name": "iNatAg-mini/amaranthus_hybridus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5653,8 +5653,8 @@
   {
     "name": "iNatAg-mini/amaranthus_hypochondriacus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5674,8 +5674,8 @@
   {
     "name": "iNatAg-mini/amaranthus_lividus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5695,8 +5695,8 @@
   {
     "name": "iNatAg-mini/amaranthus_retroflexus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5716,8 +5716,8 @@
   {
     "name": "iNatAg-mini/amaranthus_speciosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5737,8 +5737,8 @@
   {
     "name": "iNatAg-mini/amaranthus_spinosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5758,8 +5758,8 @@
   {
     "name": "iNatAg-mini/amaranthus_tricolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5779,8 +5779,8 @@
   {
     "name": "iNatAg-mini/amaranthus_viridis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5800,8 +5800,8 @@
   {
     "name": "iNatAg-mini/ambelania_acida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5821,8 +5821,8 @@
   {
     "name": "iNatAg-mini/ambrosia_acanthicarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5842,8 +5842,8 @@
   {
     "name": "iNatAg-mini/ambrosia_artemisiifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5863,8 +5863,8 @@
   {
     "name": "iNatAg-mini/ambrosia_confertiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5884,8 +5884,8 @@
   {
     "name": "iNatAg-mini/ambrosia_psilostachya",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5905,8 +5905,8 @@
   {
     "name": "iNatAg-mini/ambrosia_tomentosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5926,8 +5926,8 @@
   {
     "name": "iNatAg-mini/ambrosia_trifida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5947,8 +5947,8 @@
   {
     "name": "iNatAg-mini/ammannia_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5968,8 +5968,8 @@
   {
     "name": "iNatAg-mini/ammi_majus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5989,8 +5989,8 @@
   {
     "name": "iNatAg-mini/ammophila_arenaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6010,8 +6010,8 @@
   {
     "name": "iNatAg-mini/ammophila_breviligulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6031,8 +6031,8 @@
   {
     "name": "iNatAg-mini/amorpha_fruticosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6052,8 +6052,8 @@
   {
     "name": "iNatAg-mini/amorphophallus_paeoniifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6073,8 +6073,8 @@
   {
     "name": "iNatAg-mini/amsinckia_douglasiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6094,8 +6094,8 @@
   {
     "name": "iNatAg-mini/amsinckia_lycopsoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6115,8 +6115,8 @@
   {
     "name": "iNatAg-mini/anacardium_occidentale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6136,8 +6136,8 @@
   {
     "name": "iNatAg-mini/anagallis_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6157,8 +6157,8 @@
   {
     "name": "iNatAg-mini/ananas_comosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6178,8 +6178,8 @@
   {
     "name": "iNatAg-mini/anchusa_azurea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6199,8 +6199,8 @@
   {
     "name": "iNatAg-mini/andrographis_paniculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6220,8 +6220,8 @@
   {
     "name": "iNatAg-mini/andropogon_barbinodis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6241,8 +6241,8 @@
   {
     "name": "iNatAg-mini/andropogon_bicornis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6262,8 +6262,8 @@
   {
     "name": "iNatAg-mini/andropogon_brachystachyus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6283,8 +6283,8 @@
   {
     "name": "iNatAg-mini/andropogon_gayanus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6304,8 +6304,8 @@
   {
     "name": "iNatAg-mini/andropogon_gyrans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6325,8 +6325,8 @@
   {
     "name": "iNatAg-mini/andropogon_hallii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6346,8 +6346,8 @@
   {
     "name": "iNatAg-mini/andropogon_leucostachyus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6367,8 +6367,8 @@
   {
     "name": "iNatAg-mini/andropogon_ternarius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6388,8 +6388,8 @@
   {
     "name": "iNatAg-mini/androsace_septentrionalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6409,8 +6409,8 @@
   {
     "name": "iNatAg-mini/anemone_hepatica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6430,8 +6430,8 @@
   {
     "name": "iNatAg-mini/anemone_nemorosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6451,8 +6451,8 @@
   {
     "name": "iNatAg-mini/anethum_graveolens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6472,8 +6472,8 @@
   {
     "name": "iNatAg-mini/angelica_archangelica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6493,8 +6493,8 @@
   {
     "name": "iNatAg-mini/angelica_atropurpurea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6514,8 +6514,8 @@
   {
     "name": "iNatAg-mini/angelica_sylvestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6535,8 +6535,8 @@
   {
     "name": "iNatAg-mini/angophora_costata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6556,8 +6556,8 @@
   {
     "name": "iNatAg-mini/angophora_floribunda",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6577,8 +6577,8 @@
   {
     "name": "iNatAg-mini/annona_atemoya",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6598,8 +6598,8 @@
   {
     "name": "iNatAg-mini/annona_cherimola",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6619,8 +6619,8 @@
   {
     "name": "iNatAg-mini/annona_diversifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6640,8 +6640,8 @@
   {
     "name": "iNatAg-mini/annona_montana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6661,8 +6661,8 @@
   {
     "name": "iNatAg-mini/annona_muricata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6682,8 +6682,8 @@
   {
     "name": "iNatAg-mini/annona_purpurea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6703,8 +6703,8 @@
   {
     "name": "iNatAg-mini/annona_reticulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6724,8 +6724,8 @@
   {
     "name": "iNatAg-mini/annona_senegalensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6745,8 +6745,8 @@
   {
     "name": "iNatAg-mini/annona_squamosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6766,8 +6766,8 @@
   {
     "name": "iNatAg-mini/anogeissus_acuminata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6787,8 +6787,8 @@
   {
     "name": "iNatAg-mini/anogeissus_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6808,8 +6808,8 @@
   {
     "name": "iNatAg-mini/anogeissus_pendula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6829,8 +6829,8 @@
   {
     "name": "iNatAg-mini/antennaria_dioica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6850,8 +6850,8 @@
   {
     "name": "iNatAg-mini/anthemis_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6871,8 +6871,8 @@
   {
     "name": "iNatAg-mini/anthemis_cotula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6892,8 +6892,8 @@
   {
     "name": "iNatAg-mini/anthemis_tinctoria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6913,8 +6913,8 @@
   {
     "name": "iNatAg-mini/anthephora_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6934,8 +6934,8 @@
   {
     "name": "iNatAg-mini/anthoxanthum_odoratum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6955,8 +6955,8 @@
   {
     "name": "iNatAg-mini/anthriscus_cerefolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6976,8 +6976,8 @@
   {
     "name": "iNatAg-mini/anthyllis_vulneraria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -6997,8 +6997,8 @@
   {
     "name": "iNatAg-mini/antidesma_bunius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7018,8 +7018,8 @@
   {
     "name": "iNatAg-mini/antirrhinum_majus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7039,8 +7039,8 @@
   {
     "name": "iNatAg-mini/aphandra_natalia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7060,8 +7060,8 @@
   {
     "name": "iNatAg-mini/aphanes_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7081,8 +7081,8 @@
   {
     "name": "iNatAg-mini/apios_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7102,8 +7102,8 @@
   {
     "name": "iNatAg-mini/apium_graveolens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7123,8 +7123,8 @@
   {
     "name": "iNatAg-mini/apocynum_cannabinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7144,8 +7144,8 @@
   {
     "name": "iNatAg-mini/apocynum_sibiricum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7165,8 +7165,8 @@
   {
     "name": "iNatAg-mini/aponogeton_distachyos",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7186,8 +7186,8 @@
   {
     "name": "iNatAg-mini/aquilaria_malaccensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7207,8 +7207,8 @@
   {
     "name": "iNatAg-mini/aquilegia_canadensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7228,8 +7228,8 @@
   {
     "name": "iNatAg-mini/aquilegia_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7249,8 +7249,8 @@
   {
     "name": "iNatAg-mini/arachis_glabrata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7270,8 +7270,8 @@
   {
     "name": "iNatAg-mini/arachis_hypogaea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7291,8 +7291,8 @@
   {
     "name": "iNatAg-mini/arachis_pintoi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7312,8 +7312,8 @@
   {
     "name": "iNatAg-mini/arachis_villosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7333,8 +7333,8 @@
   {
     "name": "iNatAg-mini/araucaria_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7354,8 +7354,8 @@
   {
     "name": "iNatAg-mini/araucaria_bidwillii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7375,8 +7375,8 @@
   {
     "name": "iNatAg-mini/araucaria_cunninghamii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7396,8 +7396,8 @@
   {
     "name": "iNatAg-mini/araucaria_hunsteinii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7417,8 +7417,8 @@
   {
     "name": "iNatAg-mini/arbutus_unedo",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7438,8 +7438,8 @@
   {
     "name": "iNatAg-mini/archidendron_jiringa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7459,8 +7459,8 @@
   {
     "name": "iNatAg-mini/arctium_lappa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7480,8 +7480,8 @@
   {
     "name": "iNatAg-mini/arctostaphylos_glandulosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7501,8 +7501,8 @@
   {
     "name": "iNatAg-mini/arctostaphylos_manzanita",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7522,8 +7522,8 @@
   {
     "name": "iNatAg-mini/arctostaphylos_patula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7543,8 +7543,8 @@
   {
     "name": "iNatAg-mini/arctostaphylos_uva-ursi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7564,8 +7564,8 @@
   {
     "name": "iNatAg-mini/arctostaphylos_viscida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7585,8 +7585,8 @@
   {
     "name": "iNatAg-mini/ardisia_crenata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7606,8 +7606,8 @@
   {
     "name": "iNatAg-mini/areca_catechu",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7627,8 +7627,8 @@
   {
     "name": "iNatAg-mini/arenaria_serpyllifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7648,8 +7648,8 @@
   {
     "name": "iNatAg-mini/arenga_pinnata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7669,8 +7669,8 @@
   {
     "name": "iNatAg-mini/argemone_mexicana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7690,8 +7690,8 @@
   {
     "name": "iNatAg-mini/argyrodendron_actinophyllum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7711,8 +7711,8 @@
   {
     "name": "iNatAg-mini/argyrodendron_peralatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7732,8 +7732,8 @@
   {
     "name": "iNatAg-mini/aria_alnifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7753,8 +7753,8 @@
   {
     "name": "iNatAg-mini/aristida_adscensionis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7774,8 +7774,8 @@
   {
     "name": "iNatAg-mini/aristida_behriana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7795,8 +7795,8 @@
   {
     "name": "iNatAg-mini/aristida_congesta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7816,8 +7816,8 @@
   {
     "name": "iNatAg-mini/aristida_junciformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7837,8 +7837,8 @@
   {
     "name": "iNatAg-mini/aristida_lanosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7858,8 +7858,8 @@
   {
     "name": "iNatAg-mini/aristida_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7879,8 +7879,8 @@
   {
     "name": "iNatAg-mini/aristida_longispica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7900,8 +7900,8 @@
   {
     "name": "iNatAg-mini/aristida_personata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7921,8 +7921,8 @@
   {
     "name": "iNatAg-mini/aristida_purpurascens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7942,8 +7942,8 @@
   {
     "name": "iNatAg-mini/aristida_schiedeana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7963,8 +7963,8 @@
   {
     "name": "iNatAg-mini/aristida_transvaalensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -7984,8 +7984,8 @@
   {
     "name": "iNatAg-mini/aristolochia_rotunda",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8005,8 +8005,8 @@
   {
     "name": "iNatAg-mini/armoracia_rusticana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8026,8 +8026,8 @@
   {
     "name": "iNatAg-mini/arnica_montana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8047,8 +8047,8 @@
   {
     "name": "iNatAg-mini/arrhenatherum_elatius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8068,8 +8068,8 @@
   {
     "name": "iNatAg-mini/artemisia_abrotanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8089,8 +8089,8 @@
   {
     "name": "iNatAg-mini/artemisia_absinthium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8110,8 +8110,8 @@
   {
     "name": "iNatAg-mini/artemisia_afra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8131,8 +8131,8 @@
   {
     "name": "iNatAg-mini/artemisia_annua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8152,8 +8152,8 @@
   {
     "name": "iNatAg-mini/artemisia_campestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8173,8 +8173,8 @@
   {
     "name": "iNatAg-mini/artemisia_dracunculus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8194,8 +8194,8 @@
   {
     "name": "iNatAg-mini/artemisia_filifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8215,8 +8215,8 @@
   {
     "name": "iNatAg-mini/artemisia_glacialis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8236,8 +8236,8 @@
   {
     "name": "iNatAg-mini/artemisia_herba-alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8257,8 +8257,8 @@
   {
     "name": "iNatAg-mini/artemisia_ludoviciana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8278,8 +8278,8 @@
   {
     "name": "iNatAg-mini/artemisia_stelleriana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8299,8 +8299,8 @@
   {
     "name": "iNatAg-mini/artemisia_tridentata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8320,8 +8320,8 @@
   {
     "name": "iNatAg-mini/artemisia_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8341,8 +8341,8 @@
   {
     "name": "iNatAg-mini/artocarpus_altilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8362,8 +8362,8 @@
   {
     "name": "iNatAg-mini/artocarpus_heterophyllus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8383,8 +8383,8 @@
   {
     "name": "iNatAg-mini/artocarpus_hirsutus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8404,8 +8404,8 @@
   {
     "name": "iNatAg-mini/artocarpus_integer",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8425,8 +8425,8 @@
   {
     "name": "iNatAg-mini/artocarpus_lakoocha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8446,8 +8446,8 @@
   {
     "name": "iNatAg-mini/arundinella_hirta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8467,8 +8467,8 @@
   {
     "name": "iNatAg-mini/arundo_donax",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8488,8 +8488,8 @@
   {
     "name": "iNatAg-mini/asarina_stricta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8509,8 +8509,8 @@
   {
     "name": "iNatAg-mini/asarum_europaeum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8530,8 +8530,8 @@
   {
     "name": "iNatAg-mini/asclepias_curassavica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8551,8 +8551,8 @@
   {
     "name": "iNatAg-mini/asclepias_fascicularis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8572,8 +8572,8 @@
   {
     "name": "iNatAg-mini/asclepias_incarnata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8593,8 +8593,8 @@
   {
     "name": "iNatAg-mini/asclepias_lanceolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8614,8 +8614,8 @@
   {
     "name": "iNatAg-mini/asclepias_purpurascens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8635,8 +8635,8 @@
   {
     "name": "iNatAg-mini/asclepias_speciosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8656,8 +8656,8 @@
   {
     "name": "iNatAg-mini/asclepias_subverticillata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8677,8 +8677,8 @@
   {
     "name": "iNatAg-mini/asclepias_tuberosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8698,8 +8698,8 @@
   {
     "name": "iNatAg-mini/asclepias_verticillata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8719,8 +8719,8 @@
   {
     "name": "iNatAg-mini/asclepias_viridiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8740,8 +8740,8 @@
   {
     "name": "iNatAg-mini/asimina_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8761,8 +8761,8 @@
   {
     "name": "iNatAg-mini/asimina_triloba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8782,8 +8782,8 @@
   {
     "name": "iNatAg-mini/asparagus_densiflorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8803,8 +8803,8 @@
   {
     "name": "iNatAg-mini/asparagus_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8824,8 +8824,8 @@
   {
     "name": "iNatAg-mini/asperula_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8845,8 +8845,8 @@
   {
     "name": "iNatAg-mini/asphodelus_albus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8866,8 +8866,8 @@
   {
     "name": "iNatAg-mini/asphodelus_tenuifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8887,8 +8887,8 @@
   {
     "name": "iNatAg-mini/aspilia_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8908,8 +8908,8 @@
   {
     "name": "iNatAg-mini/asplenium_ruta-muraria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8929,8 +8929,8 @@
   {
     "name": "iNatAg-mini/aster_ericoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8950,8 +8950,8 @@
   {
     "name": "iNatAg-mini/astragalus_adsurgens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8971,8 +8971,8 @@
   {
     "name": "iNatAg-mini/astragalus_asymmetricus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -8992,8 +8992,8 @@
   {
     "name": "iNatAg-mini/astragalus_canadensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9013,8 +9013,8 @@
   {
     "name": "iNatAg-mini/astragalus_cicer",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9034,8 +9034,8 @@
   {
     "name": "iNatAg-mini/astragalus_gummifer",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9055,8 +9055,8 @@
   {
     "name": "iNatAg-mini/astragalus_mollissimus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9076,8 +9076,8 @@
   {
     "name": "iNatAg-mini/astragalus_nuttallianus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9097,8 +9097,8 @@
   {
     "name": "iNatAg-mini/astragalus_sinicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9118,8 +9118,8 @@
   {
     "name": "iNatAg-mini/astragalus_tweedyi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9139,8 +9139,8 @@
   {
     "name": "iNatAg-mini/astrantia_major",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9160,8 +9160,8 @@
   {
     "name": "iNatAg-mini/astrebla_lappacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9181,8 +9181,8 @@
   {
     "name": "iNatAg-mini/astrebla_pectinata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9202,8 +9202,8 @@
   {
     "name": "iNatAg-mini/astrebla_squarrosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9223,8 +9223,8 @@
   {
     "name": "iNatAg-mini/astrocaryum_jauari",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9244,8 +9244,8 @@
   {
     "name": "iNatAg-mini/astrocaryum_vulgare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9265,8 +9265,8 @@
   {
     "name": "iNatAg-mini/asystasia_gangetica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9286,8 +9286,8 @@
   {
     "name": "iNatAg-mini/atalaya_hemiglauca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9307,8 +9307,8 @@
   {
     "name": "iNatAg-mini/atherosperma_moschatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9328,8 +9328,8 @@
   {
     "name": "iNatAg-mini/athrotaxis_selaginoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9349,8 +9349,8 @@
   {
     "name": "iNatAg-mini/atriplex_canescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9370,8 +9370,8 @@
   {
     "name": "iNatAg-mini/atriplex_confertifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9391,8 +9391,8 @@
   {
     "name": "iNatAg-mini/atriplex_gardneri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9412,8 +9412,8 @@
   {
     "name": "iNatAg-mini/atriplex_glauca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9433,8 +9433,8 @@
   {
     "name": "iNatAg-mini/atriplex_halimus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9454,8 +9454,8 @@
   {
     "name": "iNatAg-mini/atriplex_hortensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9475,8 +9475,8 @@
   {
     "name": "iNatAg-mini/atriplex_lentiformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9496,8 +9496,8 @@
   {
     "name": "iNatAg-mini/atriplex_nummularia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9517,8 +9517,8 @@
   {
     "name": "iNatAg-mini/atriplex_patula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9538,8 +9538,8 @@
   {
     "name": "iNatAg-mini/atriplex_rosea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9559,8 +9559,8 @@
   {
     "name": "iNatAg-mini/atriplex_semibaccata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9580,8 +9580,8 @@
   {
     "name": "iNatAg-mini/atriplex_vesicaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9601,8 +9601,8 @@
   {
     "name": "iNatAg-mini/atropa_belladonna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9622,8 +9622,8 @@
   {
     "name": "iNatAg-mini/attalea_cohune",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9643,8 +9643,8 @@
   {
     "name": "iNatAg-mini/avena_fatua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9664,8 +9664,8 @@
   {
     "name": "iNatAg-mini/avena_sativa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9685,8 +9685,8 @@
   {
     "name": "iNatAg-mini/avena_sterilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9706,8 +9706,8 @@
   {
     "name": "iNatAg-mini/avenula_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9727,8 +9727,8 @@
   {
     "name": "iNatAg-mini/averrhoa_bilimbi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9748,8 +9748,8 @@
   {
     "name": "iNatAg-mini/averrhoa_carambola",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9769,8 +9769,8 @@
   {
     "name": "iNatAg-mini/avicennia_germinans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9790,8 +9790,8 @@
   {
     "name": "iNatAg-mini/avicennia_marina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9811,8 +9811,8 @@
   {
     "name": "iNatAg-mini/avicennia_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9832,8 +9832,8 @@
   {
     "name": "iNatAg-mini/axonopus_affinis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9853,8 +9853,8 @@
   {
     "name": "iNatAg-mini/axonopus_compressus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9874,8 +9874,8 @@
   {
     "name": "iNatAg-mini/axonopus_fissifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9895,8 +9895,8 @@
   {
     "name": "iNatAg-mini/axyris_amaranthoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9916,8 +9916,8 @@
   {
     "name": "iNatAg-mini/azadirachta_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9937,8 +9937,8 @@
   {
     "name": "iNatAg-mini/azanza_garckeana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9958,8 +9958,8 @@
   {
     "name": "iNatAg-mini/azolla_filiculoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -9979,8 +9979,8 @@
   {
     "name": "iNatAg-mini/azolla_pinnata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10000,8 +10000,8 @@
   {
     "name": "iNatAg-mini/baccaurea_motleyana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10021,8 +10021,8 @@
   {
     "name": "iNatAg-mini/baccaurea_ramiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10042,8 +10042,8 @@
   {
     "name": "iNatAg-mini/baccharis_glutinosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10063,8 +10063,8 @@
   {
     "name": "iNatAg-mini/baccharis_pilularis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10084,8 +10084,8 @@
   {
     "name": "iNatAg-mini/bactris_gasipaes",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10105,8 +10105,8 @@
   {
     "name": "iNatAg-mini/baikiaea_plurijuga",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10126,8 +10126,8 @@
   {
     "name": "iNatAg-mini/balanites_aegyptiaca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10147,8 +10147,8 @@
   {
     "name": "iNatAg-mini/bambusa_arundinacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10168,8 +10168,8 @@
   {
     "name": "iNatAg-mini/bambusa_balcooa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10189,8 +10189,8 @@
   {
     "name": "iNatAg-mini/bambusa_blumeana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10210,8 +10210,8 @@
   {
     "name": "iNatAg-mini/bambusa_tulda",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10231,8 +10231,8 @@
   {
     "name": "iNatAg-mini/bambusa_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10252,8 +10252,8 @@
   {
     "name": "iNatAg-mini/banksia_integrifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10273,8 +10273,8 @@
   {
     "name": "iNatAg-mini/banksia_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10294,8 +10294,8 @@
   {
     "name": "iNatAg-mini/baphia_nitida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10315,8 +10315,8 @@
   {
     "name": "iNatAg-mini/barringtonia_racemosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10336,8 +10336,8 @@
   {
     "name": "iNatAg-mini/basella_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10357,8 +10357,8 @@
   {
     "name": "iNatAg-mini/bauhinia_aculeata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10378,8 +10378,8 @@
   {
     "name": "iNatAg-mini/bauhinia_petersiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10399,8 +10399,8 @@
   {
     "name": "iNatAg-mini/bauhinia_racemosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10420,8 +10420,8 @@
   {
     "name": "iNatAg-mini/bauhinia_rufescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10441,8 +10441,8 @@
   {
     "name": "iNatAg-mini/bauhinia_thonningii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10462,8 +10462,8 @@
   {
     "name": "iNatAg-mini/bauhinia_tomentosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10483,8 +10483,8 @@
   {
     "name": "iNatAg-mini/bauhinia_variegata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10504,8 +10504,8 @@
   {
     "name": "iNatAg-mini/beckmannia_eruciformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10525,8 +10525,8 @@
   {
     "name": "iNatAg-mini/beckmannia_syzigachne",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10546,8 +10546,8 @@
   {
     "name": "iNatAg-mini/bellis_perennis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10567,8 +10567,8 @@
   {
     "name": "iNatAg-mini/benincasa_hispida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10588,8 +10588,8 @@
   {
     "name": "iNatAg-mini/berberis_aquifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10609,8 +10609,8 @@
   {
     "name": "iNatAg-mini/berberis_thunbergii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10630,8 +10630,8 @@
   {
     "name": "iNatAg-mini/berberis_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10651,8 +10651,8 @@
   {
     "name": "iNatAg-mini/berchemia_discolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10672,8 +10672,8 @@
   {
     "name": "iNatAg-mini/berrya_cordifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10693,8 +10693,8 @@
   {
     "name": "iNatAg-mini/bersama_lucens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10714,8 +10714,8 @@
   {
     "name": "iNatAg-mini/bertholletia_excelsa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10735,8 +10735,8 @@
   {
     "name": "iNatAg-mini/beta_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10756,8 +10756,8 @@
   {
     "name": "iNatAg-mini/betula_nigra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10777,8 +10777,8 @@
   {
     "name": "iNatAg-mini/betula_pendula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10798,8 +10798,8 @@
   {
     "name": "iNatAg-mini/betula_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10819,8 +10819,8 @@
   {
     "name": "iNatAg-mini/bidens_bipinnata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10840,8 +10840,8 @@
   {
     "name": "iNatAg-mini/bidens_cernua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10861,8 +10861,8 @@
   {
     "name": "iNatAg-mini/bidens_frondosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10882,8 +10882,8 @@
   {
     "name": "iNatAg-mini/bidens_pilosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10903,8 +10903,8 @@
   {
     "name": "iNatAg-mini/bidens_tripartita",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10924,8 +10924,8 @@
   {
     "name": "iNatAg-mini/bignonia_capreolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10945,8 +10945,8 @@
   {
     "name": "iNatAg-mini/biserrula_pelecinus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10966,8 +10966,8 @@
   {
     "name": "iNatAg-mini/bixa_orellana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -10987,8 +10987,8 @@
   {
     "name": "iNatAg-mini/blighia_sapida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11008,8 +11008,8 @@
   {
     "name": "iNatAg-mini/blumea_balsamifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11029,8 +11029,8 @@
   {
     "name": "iNatAg-mini/bocconia_frutescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11050,8 +11050,8 @@
   {
     "name": "iNatAg-mini/boehmeria_nivea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11071,8 +11071,8 @@
   {
     "name": "iNatAg-mini/boerhavia_coccinea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11092,8 +11092,8 @@
   {
     "name": "iNatAg-mini/boerhavia_diffusa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11113,8 +11113,8 @@
   {
     "name": "iNatAg-mini/boerhavia_erecta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11134,8 +11134,8 @@
   {
     "name": "iNatAg-mini/boesenbergia_rotunda",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11155,8 +11155,8 @@
   {
     "name": "iNatAg-mini/bolusanthus_speciosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11176,8 +11176,8 @@
   {
     "name": "iNatAg-mini/bombacopsis_quinata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11197,8 +11197,8 @@
   {
     "name": "iNatAg-mini/bombax_ceiba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11218,8 +11218,8 @@
   {
     "name": "iNatAg-mini/bombax_insigne",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11239,8 +11239,8 @@
   {
     "name": "iNatAg-mini/borago_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11260,8 +11260,8 @@
   {
     "name": "iNatAg-mini/borassus_aethiopum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11281,8 +11281,8 @@
   {
     "name": "iNatAg-mini/borassus_flabellifer",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11302,8 +11302,8 @@
   {
     "name": "iNatAg-mini/borojoa_patinoi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11323,8 +11323,8 @@
   {
     "name": "iNatAg-mini/boronia_glabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11344,8 +11344,8 @@
   {
     "name": "iNatAg-mini/boscia_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11365,8 +11365,8 @@
   {
     "name": "iNatAg-mini/boswellia_serrata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11386,8 +11386,8 @@
   {
     "name": "iNatAg-mini/bothriochloa_bladhii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11407,8 +11407,8 @@
   {
     "name": "iNatAg-mini/bothriochloa_insculpta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11428,8 +11428,8 @@
   {
     "name": "iNatAg-mini/bothriochloa_ischaemum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11449,8 +11449,8 @@
   {
     "name": "iNatAg-mini/bothriochloa_pertusa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11470,8 +11470,8 @@
   {
     "name": "iNatAg-mini/bougainvillea_glabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11491,8 +11491,8 @@
   {
     "name": "iNatAg-mini/bouteloua_curtipendula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11512,8 +11512,8 @@
   {
     "name": "iNatAg-mini/bouteloua_gracilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11533,8 +11533,8 @@
   {
     "name": "iNatAg-mini/brachiaria_brizantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11554,8 +11554,8 @@
   {
     "name": "iNatAg-mini/brachiaria_decumbens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11575,8 +11575,8 @@
   {
     "name": "iNatAg-mini/brachiaria_deflexa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11596,8 +11596,8 @@
   {
     "name": "iNatAg-mini/brachiaria_distachya",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11617,8 +11617,8 @@
   {
     "name": "iNatAg-mini/brachiaria_humidicola",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11638,8 +11638,8 @@
   {
     "name": "iNatAg-mini/brachiaria_mutica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11659,8 +11659,8 @@
   {
     "name": "iNatAg-mini/brachiaria_ramosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11680,8 +11680,8 @@
   {
     "name": "iNatAg-mini/brachiaria_serrata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11701,8 +11701,8 @@
   {
     "name": "iNatAg-mini/brachychiton_acerifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11722,8 +11722,8 @@
   {
     "name": "iNatAg-mini/brachychiton_populneus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11743,8 +11743,8 @@
   {
     "name": "iNatAg-mini/brachylaena_huillensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11764,8 +11764,8 @@
   {
     "name": "iNatAg-mini/brachystegia_spiciformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11785,8 +11785,8 @@
   {
     "name": "iNatAg-mini/brassica_campestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11806,8 +11806,8 @@
   {
     "name": "iNatAg-mini/brassica_chinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11827,8 +11827,8 @@
   {
     "name": "iNatAg-mini/brassica_incana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11848,8 +11848,8 @@
   {
     "name": "iNatAg-mini/brassica_juncea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11869,8 +11869,8 @@
   {
     "name": "iNatAg-mini/brassica_napus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11890,8 +11890,8 @@
   {
     "name": "iNatAg-mini/brassica_nigra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11911,8 +11911,8 @@
   {
     "name": "iNatAg-mini/brassica_rapa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11932,8 +11932,8 @@
   {
     "name": "iNatAg-mini/brassica_tournefortii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11953,8 +11953,8 @@
   {
     "name": "iNatAg-mini/bridelia_micrantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11974,8 +11974,8 @@
   {
     "name": "iNatAg-mini/briza_maxima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -11995,8 +11995,8 @@
   {
     "name": "iNatAg-mini/briza_media",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12016,8 +12016,8 @@
   {
     "name": "iNatAg-mini/briza_minor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12037,8 +12037,8 @@
   {
     "name": "iNatAg-mini/bromus_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12058,8 +12058,8 @@
   {
     "name": "iNatAg-mini/bromus_carinatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12079,8 +12079,8 @@
   {
     "name": "iNatAg-mini/bromus_catharticus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12100,8 +12100,8 @@
   {
     "name": "iNatAg-mini/bromus_diandrus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12121,8 +12121,8 @@
   {
     "name": "iNatAg-mini/bromus_erectus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12142,8 +12142,8 @@
   {
     "name": "iNatAg-mini/bromus_hordeaceus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12163,8 +12163,8 @@
   {
     "name": "iNatAg-mini/bromus_inermis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12184,8 +12184,8 @@
   {
     "name": "iNatAg-mini/bromus_madritensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12205,8 +12205,8 @@
   {
     "name": "iNatAg-mini/bromus_marginatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12226,8 +12226,8 @@
   {
     "name": "iNatAg-mini/bromus_racemosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12247,8 +12247,8 @@
   {
     "name": "iNatAg-mini/bromus_rubens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12268,8 +12268,8 @@
   {
     "name": "iNatAg-mini/bromus_secalinus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12289,8 +12289,8 @@
   {
     "name": "iNatAg-mini/bromus_sterilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12310,8 +12310,8 @@
   {
     "name": "iNatAg-mini/bromus_tectorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12331,8 +12331,8 @@
   {
     "name": "iNatAg-mini/bromus_unioloides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12352,8 +12352,8 @@
   {
     "name": "iNatAg-mini/bromus_willdenowii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12373,8 +12373,8 @@
   {
     "name": "iNatAg-mini/brosimum_alicastrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12394,8 +12394,8 @@
   {
     "name": "iNatAg-mini/broussonetia_papyrifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12415,8 +12415,8 @@
   {
     "name": "iNatAg-mini/bruguiera_gymnorrhiza",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12436,8 +12436,8 @@
   {
     "name": "iNatAg-mini/bryonia_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12457,8 +12457,8 @@
   {
     "name": "iNatAg-mini/bryonia_cretica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12478,8 +12478,8 @@
   {
     "name": "iNatAg-mini/buchloe_dactyloides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12499,8 +12499,8 @@
   {
     "name": "iNatAg-mini/buckinghamia_celsissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12520,8 +12520,8 @@
   {
     "name": "iNatAg-mini/bunias_erucago",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12541,8 +12541,8 @@
   {
     "name": "iNatAg-mini/bunias_orientalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12562,8 +12562,8 @@
   {
     "name": "iNatAg-mini/burkea_africana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12583,8 +12583,8 @@
   {
     "name": "iNatAg-mini/bursera_simaruba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12604,8 +12604,8 @@
   {
     "name": "iNatAg-mini/butea_monosperma",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12625,8 +12625,8 @@
   {
     "name": "iNatAg-mini/butomus_umbellatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12646,8 +12646,8 @@
   {
     "name": "iNatAg-mini/buxus_sempervirens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12667,8 +12667,8 @@
   {
     "name": "iNatAg-mini/cacalia_atriplicifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12688,8 +12688,8 @@
   {
     "name": "iNatAg-mini/caesalpinia_coriaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12709,8 +12709,8 @@
   {
     "name": "iNatAg-mini/caesalpinia_sappan",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12730,8 +12730,8 @@
   {
     "name": "iNatAg-mini/cajanus_cajan",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12751,8 +12751,8 @@
   {
     "name": "iNatAg-mini/calamagrostis_epigeios",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12772,8 +12772,8 @@
   {
     "name": "iNatAg-mini/calathea_allouia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12793,8 +12793,8 @@
   {
     "name": "iNatAg-mini/calendula_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12814,8 +12814,8 @@
   {
     "name": "iNatAg-mini/calendula_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12835,8 +12835,8 @@
   {
     "name": "iNatAg-mini/calliandra_calothyrsus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12856,8 +12856,8 @@
   {
     "name": "iNatAg-mini/calliandra_tweedii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12877,8 +12877,8 @@
   {
     "name": "iNatAg-mini/callisia_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12898,8 +12898,8 @@
   {
     "name": "iNatAg-mini/callitriche_palustris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12919,8 +12919,8 @@
   {
     "name": "iNatAg-mini/callitriche_stagnalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12940,8 +12940,8 @@
   {
     "name": "iNatAg-mini/callitriche_verna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12961,8 +12961,8 @@
   {
     "name": "iNatAg-mini/callitris_columellaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -12982,8 +12982,8 @@
   {
     "name": "iNatAg-mini/callitris_endlicheri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13003,8 +13003,8 @@
   {
     "name": "iNatAg-mini/callitris_macleayana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13024,8 +13024,8 @@
   {
     "name": "iNatAg-mini/calluna_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13045,8 +13045,8 @@
   {
     "name": "iNatAg-mini/calodendrum_capense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13066,8 +13066,8 @@
   {
     "name": "iNatAg-mini/calophyllum_apetalum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13087,8 +13087,8 @@
   {
     "name": "iNatAg-mini/calophyllum_brasiliense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13108,8 +13108,8 @@
   {
     "name": "iNatAg-mini/calophyllum_inophyllum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13129,8 +13129,8 @@
   {
     "name": "iNatAg-mini/calopogonium_caeruleum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13150,8 +13150,8 @@
   {
     "name": "iNatAg-mini/calopogonium_mucunoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13171,8 +13171,8 @@
   {
     "name": "iNatAg-mini/calotropis_procera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13192,8 +13192,8 @@
   {
     "name": "iNatAg-mini/caltha_palustris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13213,8 +13213,8 @@
   {
     "name": "iNatAg-mini/calystegia_hederacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13234,8 +13234,8 @@
   {
     "name": "iNatAg-mini/calystegia_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13255,8 +13255,8 @@
   {
     "name": "iNatAg-mini/calystegia_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13276,8 +13276,8 @@
   {
     "name": "iNatAg-mini/camelina_sativa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13297,8 +13297,8 @@
   {
     "name": "iNatAg-mini/camellia_sinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13318,8 +13318,8 @@
   {
     "name": "iNatAg-mini/campanula_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13339,8 +13339,8 @@
   {
     "name": "iNatAg-mini/campanula_rapunculus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13360,8 +13360,8 @@
   {
     "name": "iNatAg-mini/campanula_rotundifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13381,8 +13381,8 @@
   {
     "name": "iNatAg-mini/cananga_odorata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13402,8 +13402,8 @@
   {
     "name": "iNatAg-mini/canavalia_brasiliensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13423,8 +13423,8 @@
   {
     "name": "iNatAg-mini/canavalia_ensiformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13444,8 +13444,8 @@
   {
     "name": "iNatAg-mini/canavalia_gladiata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13465,8 +13465,8 @@
   {
     "name": "iNatAg-mini/canna_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13486,8 +13486,8 @@
   {
     "name": "iNatAg-mini/canthium_spinosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13507,8 +13507,8 @@
   {
     "name": "iNatAg-mini/capparis_decidua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13528,8 +13528,8 @@
   {
     "name": "iNatAg-mini/capparis_spinosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13549,8 +13549,8 @@
   {
     "name": "iNatAg-mini/capparis_tomentosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13570,8 +13570,8 @@
   {
     "name": "iNatAg-mini/capsella_bursa-pastoris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13591,8 +13591,8 @@
   {
     "name": "iNatAg-mini/capsicum_annuum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13612,8 +13612,8 @@
   {
     "name": "iNatAg-mini/capsicum_chinense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13633,8 +13633,8 @@
   {
     "name": "iNatAg-mini/capsicum_frutescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13654,8 +13654,8 @@
   {
     "name": "iNatAg-mini/capsicum_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13675,8 +13675,8 @@
   {
     "name": "iNatAg-mini/caragana_arborescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13696,8 +13696,8 @@
   {
     "name": "iNatAg-mini/caragana_microphylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13717,8 +13717,8 @@
   {
     "name": "iNatAg-mini/carapa_guianensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13738,8 +13738,8 @@
   {
     "name": "iNatAg-mini/cardamine_flexuosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13759,8 +13759,8 @@
   {
     "name": "iNatAg-mini/cardamine_hirsuta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13780,8 +13780,8 @@
   {
     "name": "iNatAg-mini/cardamine_impatiens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13801,8 +13801,8 @@
   {
     "name": "iNatAg-mini/cardamine_oligosperma",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13822,8 +13822,8 @@
   {
     "name": "iNatAg-mini/cardamine_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13843,8 +13843,8 @@
   {
     "name": "iNatAg-mini/cardamine_pratensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13864,8 +13864,8 @@
   {
     "name": "iNatAg-mini/cardiospermum_halicacabum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13885,8 +13885,8 @@
   {
     "name": "iNatAg-mini/carduus_acanthoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13906,8 +13906,8 @@
   {
     "name": "iNatAg-mini/carduus_crispus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13927,8 +13927,8 @@
   {
     "name": "iNatAg-mini/carduus_lanceolatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13948,8 +13948,8 @@
   {
     "name": "iNatAg-mini/carduus_pycnocephalus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13969,8 +13969,8 @@
   {
     "name": "iNatAg-mini/carex_nebrascensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -13990,8 +13990,8 @@
   {
     "name": "iNatAg-mini/carex_pallescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14011,8 +14011,8 @@
   {
     "name": "iNatAg-mini/carica_cauliflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14032,8 +14032,8 @@
   {
     "name": "iNatAg-mini/carica_papaya",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14053,8 +14053,8 @@
   {
     "name": "iNatAg-mini/carica_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14074,8 +14074,8 @@
   {
     "name": "iNatAg-mini/cariniana_pyriformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14095,8 +14095,8 @@
   {
     "name": "iNatAg-mini/carissa_carandas",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14116,8 +14116,8 @@
   {
     "name": "iNatAg-mini/carissa_edulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14137,8 +14137,8 @@
   {
     "name": "iNatAg-mini/carissa_macrocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14158,8 +14158,8 @@
   {
     "name": "iNatAg-mini/carlina_acaulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14179,8 +14179,8 @@
   {
     "name": "iNatAg-mini/carludovica_palmata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14200,8 +14200,8 @@
   {
     "name": "iNatAg-mini/caroxylon_aphyllum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14221,8 +14221,8 @@
   {
     "name": "iNatAg-mini/carpinus_betulus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14242,8 +14242,8 @@
   {
     "name": "iNatAg-mini/carthamus_creticus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14263,8 +14263,8 @@
   {
     "name": "iNatAg-mini/carthamus_lanatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14284,8 +14284,8 @@
   {
     "name": "iNatAg-mini/carthamus_tinctorius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14305,8 +14305,8 @@
   {
     "name": "iNatAg-mini/carum_carvi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14326,8 +14326,8 @@
   {
     "name": "iNatAg-mini/carya_illinoensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14347,8 +14347,8 @@
   {
     "name": "iNatAg-mini/caryodendron_orinocense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14368,8 +14368,8 @@
   {
     "name": "iNatAg-mini/caryota_urens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14389,8 +14389,8 @@
   {
     "name": "iNatAg-mini/casimiroa_edulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14410,8 +14410,8 @@
   {
     "name": "iNatAg-mini/cassia_articulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14431,8 +14431,8 @@
   {
     "name": "iNatAg-mini/cassia_brewsteri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14452,8 +14452,8 @@
   {
     "name": "iNatAg-mini/cassia_fistula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14473,8 +14473,8 @@
   {
     "name": "iNatAg-mini/cassia_marilandica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14494,8 +14494,8 @@
   {
     "name": "iNatAg-mini/cassia_nictitans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14515,8 +14515,8 @@
   {
     "name": "iNatAg-mini/cassia_reticulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14536,8 +14536,8 @@
   {
     "name": "iNatAg-mini/cassia_senna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14557,8 +14557,8 @@
   {
     "name": "iNatAg-mini/cassia_siamea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14578,8 +14578,8 @@
   {
     "name": "iNatAg-mini/cassia_sieberiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14599,8 +14599,8 @@
   {
     "name": "iNatAg-mini/cassia_tomentosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14620,8 +14620,8 @@
   {
     "name": "iNatAg-mini/cassia_tora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14641,8 +14641,8 @@
   {
     "name": "iNatAg-mini/castanea_crenata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14662,8 +14662,8 @@
   {
     "name": "iNatAg-mini/castanea_dentata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14683,8 +14683,8 @@
   {
     "name": "iNatAg-mini/castanea_mollissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14704,8 +14704,8 @@
   {
     "name": "iNatAg-mini/castanea_pumila",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14725,8 +14725,8 @@
   {
     "name": "iNatAg-mini/castanea_sativa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14746,8 +14746,8 @@
   {
     "name": "iNatAg-mini/castanospermum_australe",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14767,8 +14767,8 @@
   {
     "name": "iNatAg-mini/castilla_elastica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14788,8 +14788,8 @@
   {
     "name": "iNatAg-mini/castilleja_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14809,8 +14809,8 @@
   {
     "name": "iNatAg-mini/castilleja_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14830,8 +14830,8 @@
   {
     "name": "iNatAg-mini/casuarina_cristata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14851,8 +14851,8 @@
   {
     "name": "iNatAg-mini/casuarina_cunninghamiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14872,8 +14872,8 @@
   {
     "name": "iNatAg-mini/casuarina_equisetifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14893,8 +14893,8 @@
   {
     "name": "iNatAg-mini/casuarina_glauca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14914,8 +14914,8 @@
   {
     "name": "iNatAg-mini/casuarina_junghuhniana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14935,8 +14935,8 @@
   {
     "name": "iNatAg-mini/casuarina_obesa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14956,8 +14956,8 @@
   {
     "name": "iNatAg-mini/catalpa_bignonioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14977,8 +14977,8 @@
   {
     "name": "iNatAg-mini/catha_edulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -14998,8 +14998,8 @@
   {
     "name": "iNatAg-mini/catharanthus_roseus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15019,8 +15019,8 @@
   {
     "name": "iNatAg-mini/ceanothus_americanus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15040,8 +15040,8 @@
   {
     "name": "iNatAg-mini/ceanothus_prostratus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15061,8 +15061,8 @@
   {
     "name": "iNatAg-mini/cedrela_odorata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15082,8 +15082,8 @@
   {
     "name": "iNatAg-mini/cedrus_deodara",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15103,8 +15103,8 @@
   {
     "name": "iNatAg-mini/ceiba_pentandra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15124,8 +15124,8 @@
   {
     "name": "iNatAg-mini/celastrus_orbiculatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15145,8 +15145,8 @@
   {
     "name": "iNatAg-mini/celastrus_scandens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15166,8 +15166,8 @@
   {
     "name": "iNatAg-mini/celosia_argentea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15187,8 +15187,8 @@
   {
     "name": "iNatAg-mini/celtis_australis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15208,8 +15208,8 @@
   {
     "name": "iNatAg-mini/cenchrus_biflorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15229,8 +15229,8 @@
   {
     "name": "iNatAg-mini/cenchrus_ciliaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15250,8 +15250,8 @@
   {
     "name": "iNatAg-mini/cenchrus_echinatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15271,8 +15271,8 @@
   {
     "name": "iNatAg-mini/cenchrus_setigerus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15292,8 +15292,8 @@
   {
     "name": "iNatAg-mini/cenchrus_spinifex",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15313,8 +15313,8 @@
   {
     "name": "iNatAg-mini/cenchrus_tribuloides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15334,8 +15334,8 @@
   {
     "name": "iNatAg-mini/centaurea_biebersteinii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15355,8 +15355,8 @@
   {
     "name": "iNatAg-mini/centaurea_calcitrapa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15376,8 +15376,8 @@
   {
     "name": "iNatAg-mini/centaurea_cyanus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15397,8 +15397,8 @@
   {
     "name": "iNatAg-mini/centaurea_diluta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15418,8 +15418,8 @@
   {
     "name": "iNatAg-mini/centaurea_jacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15439,8 +15439,8 @@
   {
     "name": "iNatAg-mini/centaurea_melitensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15460,8 +15460,8 @@
   {
     "name": "iNatAg-mini/centaurea_nigra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15481,8 +15481,8 @@
   {
     "name": "iNatAg-mini/centaurea_nigrescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15502,8 +15502,8 @@
   {
     "name": "iNatAg-mini/centaurea_solstitalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15523,8 +15523,8 @@
   {
     "name": "iNatAg-mini/centaurea_solstitialis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15544,8 +15544,8 @@
   {
     "name": "iNatAg-mini/centaurea_stoebe",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15565,8 +15565,8 @@
   {
     "name": "iNatAg-mini/centaurea_virgata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15586,8 +15586,8 @@
   {
     "name": "iNatAg-mini/centella_asiatica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15607,8 +15607,8 @@
   {
     "name": "iNatAg-mini/centropodia_glauca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15628,8 +15628,8 @@
   {
     "name": "iNatAg-mini/centrosema_brasilianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15649,8 +15649,8 @@
   {
     "name": "iNatAg-mini/centrosema_macrocarpum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15670,8 +15670,8 @@
   {
     "name": "iNatAg-mini/centrosema_pascuorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15691,8 +15691,8 @@
   {
     "name": "iNatAg-mini/centrosema_plumieri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15712,8 +15712,8 @@
   {
     "name": "iNatAg-mini/centrosema_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15733,8 +15733,8 @@
   {
     "name": "iNatAg-mini/centrosema_virginianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15754,8 +15754,8 @@
   {
     "name": "iNatAg-mini/cephalanthus_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15775,8 +15775,8 @@
   {
     "name": "iNatAg-mini/cerastium_arvense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15796,8 +15796,8 @@
   {
     "name": "iNatAg-mini/cerastium_nutans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15817,8 +15817,8 @@
   {
     "name": "iNatAg-mini/cerastium_vulgatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15838,8 +15838,8 @@
   {
     "name": "iNatAg-mini/ceratonia_siliqua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15859,8 +15859,8 @@
   {
     "name": "iNatAg-mini/ceratopetalum_apetalum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15880,8 +15880,8 @@
   {
     "name": "iNatAg-mini/ceratophyllum_demersum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15901,8 +15901,8 @@
   {
     "name": "iNatAg-mini/ceratophyllum_echinatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15922,8 +15922,8 @@
   {
     "name": "iNatAg-mini/ceriops_tagal",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15943,8 +15943,8 @@
   {
     "name": "iNatAg-mini/cestrum_diurnum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15964,8 +15964,8 @@
   {
     "name": "iNatAg-mini/ceterach_officinarum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -15985,8 +15985,8 @@
   {
     "name": "iNatAg-mini/chaerophyllum_tainturieri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16006,8 +16006,8 @@
   {
     "name": "iNatAg-mini/chamaebatia_foliolosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16027,8 +16027,8 @@
   {
     "name": "iNatAg-mini/chamaecrista_nictitans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16048,8 +16048,8 @@
   {
     "name": "iNatAg-mini/chamaecrista_rotundifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16069,8 +16069,8 @@
   {
     "name": "iNatAg-mini/chamaedorea_tepejilote",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16090,8 +16090,8 @@
   {
     "name": "iNatAg-mini/chamaerops_humilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16111,8 +16111,8 @@
   {
     "name": "iNatAg-mini/chara_intermedia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16132,8 +16132,8 @@
   {
     "name": "iNatAg-mini/chelidonium_majus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16153,8 +16153,8 @@
   {
     "name": "iNatAg-mini/chenopodium_album",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16174,8 +16174,8 @@
   {
     "name": "iNatAg-mini/chenopodium_ambrosioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16195,8 +16195,8 @@
   {
     "name": "iNatAg-mini/chenopodium_ambrosoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16216,8 +16216,8 @@
   {
     "name": "iNatAg-mini/chenopodium_berlandieri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16237,8 +16237,8 @@
   {
     "name": "iNatAg-mini/chenopodium_bonus-henricus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16258,8 +16258,8 @@
   {
     "name": "iNatAg-mini/chenopodium_botrys",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16279,8 +16279,8 @@
   {
     "name": "iNatAg-mini/chenopodium_ficifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16300,8 +16300,8 @@
   {
     "name": "iNatAg-mini/chenopodium_gigantospermum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16321,8 +16321,8 @@
   {
     "name": "iNatAg-mini/chenopodium_glaucum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16342,8 +16342,8 @@
   {
     "name": "iNatAg-mini/chenopodium_missouriense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16363,8 +16363,8 @@
   {
     "name": "iNatAg-mini/chenopodium_multifidum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16384,8 +16384,8 @@
   {
     "name": "iNatAg-mini/chenopodium_murale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16405,8 +16405,8 @@
   {
     "name": "iNatAg-mini/chenopodium_polyspermum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16426,8 +16426,8 @@
   {
     "name": "iNatAg-mini/chenopodium_quinoa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16447,8 +16447,8 @@
   {
     "name": "iNatAg-mini/chenopodium_rubrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16468,8 +16468,8 @@
   {
     "name": "iNatAg-mini/chenopodium_urbicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16489,8 +16489,8 @@
   {
     "name": "iNatAg-mini/chloris_ciliata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16510,8 +16510,8 @@
   {
     "name": "iNatAg-mini/chloris_gayana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16531,8 +16531,8 @@
   {
     "name": "iNatAg-mini/chloris_roxburghiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16552,8 +16552,8 @@
   {
     "name": "iNatAg-mini/chloris_verticillata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16573,8 +16573,8 @@
   {
     "name": "iNatAg-mini/chloris_virgata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16594,8 +16594,8 @@
   {
     "name": "iNatAg-mini/chlorogalum_pomeridianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16615,8 +16615,8 @@
   {
     "name": "iNatAg-mini/chlorophora_excelsa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16636,8 +16636,8 @@
   {
     "name": "iNatAg-mini/chlorophytum_comosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16657,8 +16657,8 @@
   {
     "name": "iNatAg-mini/chloroxylon_swietenia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16678,8 +16678,8 @@
   {
     "name": "iNatAg-mini/chromolaena_odorata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16699,8 +16699,8 @@
   {
     "name": "iNatAg-mini/chrysanthemum_coronarium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16720,8 +16720,8 @@
   {
     "name": "iNatAg-mini/chrysanthemum_leucanthemum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16741,8 +16741,8 @@
   {
     "name": "iNatAg-mini/chrysophyllum_cainito",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16762,8 +16762,8 @@
   {
     "name": "iNatAg-mini/chrysopogon_aciculatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16783,8 +16783,8 @@
   {
     "name": "iNatAg-mini/chukrasia_velutina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16804,8 +16804,8 @@
   {
     "name": "iNatAg-mini/cicer_arietinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16825,8 +16825,8 @@
   {
     "name": "iNatAg-mini/cichorium_endivia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16846,8 +16846,8 @@
   {
     "name": "iNatAg-mini/cichorium_intybus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16867,8 +16867,8 @@
   {
     "name": "iNatAg-mini/cicuta_bulbifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16888,8 +16888,8 @@
   {
     "name": "iNatAg-mini/cicuta_mackenzieana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16909,8 +16909,8 @@
   {
     "name": "iNatAg-mini/cicuta_maculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16930,8 +16930,8 @@
   {
     "name": "iNatAg-mini/cicuta_virosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16951,8 +16951,8 @@
   {
     "name": "iNatAg-mini/cimicifuga_racemosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16972,8 +16972,8 @@
   {
     "name": "iNatAg-mini/cinchona_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -16993,8 +16993,8 @@
   {
     "name": "iNatAg-mini/cinchona_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17014,8 +17014,8 @@
   {
     "name": "iNatAg-mini/cinnamomum_burmannii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17035,8 +17035,8 @@
   {
     "name": "iNatAg-mini/cinnamomum_camphora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17056,8 +17056,8 @@
   {
     "name": "iNatAg-mini/cinnamomum_cassia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17077,8 +17077,8 @@
   {
     "name": "iNatAg-mini/cinnamomum_verum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17098,8 +17098,8 @@
   {
     "name": "iNatAg-mini/cistus_creticus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17119,8 +17119,8 @@
   {
     "name": "iNatAg-mini/citrofortunella_microcarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17140,8 +17140,8 @@
   {
     "name": "iNatAg-mini/citrullus_colocynthis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17161,8 +17161,8 @@
   {
     "name": "iNatAg-mini/citrullus_lanatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17182,8 +17182,8 @@
   {
     "name": "iNatAg-mini/citrus_aurantifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17203,8 +17203,8 @@
   {
     "name": "iNatAg-mini/citrus_aurantium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17224,8 +17224,8 @@
   {
     "name": "iNatAg-mini/citrus_deliciosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17245,8 +17245,8 @@
   {
     "name": "iNatAg-mini/citrus_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17266,8 +17266,8 @@
   {
     "name": "iNatAg-mini/citrus_limon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17287,8 +17287,8 @@
   {
     "name": "iNatAg-mini/citrus_madurensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17308,8 +17308,8 @@
   {
     "name": "iNatAg-mini/citrus_medica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17329,8 +17329,8 @@
   {
     "name": "iNatAg-mini/citrus_paradisi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17350,8 +17350,8 @@
   {
     "name": "iNatAg-mini/citrus_reticulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17371,8 +17371,8 @@
   {
     "name": "iNatAg-mini/citrus_sinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17392,8 +17392,8 @@
   {
     "name": "iNatAg-mini/citrus_unshiu",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17413,8 +17413,8 @@
   {
     "name": "iNatAg-mini/clausena_lansium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17434,8 +17434,8 @@
   {
     "name": "iNatAg-mini/claytonia_caroliniana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17455,8 +17455,8 @@
   {
     "name": "iNatAg-mini/claytonia_virginica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17476,8 +17476,8 @@
   {
     "name": "iNatAg-mini/cleistogenes_squarrosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17497,8 +17497,8 @@
   {
     "name": "iNatAg-mini/clematis_ligusticifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17518,8 +17518,8 @@
   {
     "name": "iNatAg-mini/clematis_orientalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17539,8 +17539,8 @@
   {
     "name": "iNatAg-mini/clematis_virginiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17560,8 +17560,8 @@
   {
     "name": "iNatAg-mini/clematis_vitalba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17581,8 +17581,8 @@
   {
     "name": "iNatAg-mini/cleome_gynandra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17602,8 +17602,8 @@
   {
     "name": "iNatAg-mini/cleome_hassleriana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17623,8 +17623,8 @@
   {
     "name": "iNatAg-mini/cleome_viscosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17644,8 +17644,8 @@
   {
     "name": "iNatAg-mini/clitoria_laurifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17665,8 +17665,8 @@
   {
     "name": "iNatAg-mini/clitoria_ternatea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17686,8 +17686,8 @@
   {
     "name": "iNatAg-mini/clusia_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17707,8 +17707,8 @@
   {
     "name": "iNatAg-mini/cnicus_benedictus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17728,8 +17728,8 @@
   {
     "name": "iNatAg-mini/coccoloba_uvifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17749,8 +17749,8 @@
   {
     "name": "iNatAg-mini/cochlospermum_religiosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17770,8 +17770,8 @@
   {
     "name": "iNatAg-mini/cocos_nucifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17791,8 +17791,8 @@
   {
     "name": "iNatAg-mini/coffea_arabica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17812,8 +17812,8 @@
   {
     "name": "iNatAg-mini/coffea_canephora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17833,8 +17833,8 @@
   {
     "name": "iNatAg-mini/coffea_liberica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17854,8 +17854,8 @@
   {
     "name": "iNatAg-mini/coix_lacryma-jobi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17875,8 +17875,8 @@
   {
     "name": "iNatAg-mini/cola_acuminata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17896,8 +17896,8 @@
   {
     "name": "iNatAg-mini/cola_nitida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17917,8 +17917,8 @@
   {
     "name": "iNatAg-mini/colchicum_autumnale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17938,8 +17938,8 @@
   {
     "name": "iNatAg-mini/coleus_amboinicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17959,8 +17959,8 @@
   {
     "name": "iNatAg-mini/colocasia_esculenta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -17980,8 +17980,8 @@
   {
     "name": "iNatAg-mini/colophospermum_mopane",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18001,8 +18001,8 @@
   {
     "name": "iNatAg-mini/combretum_aculeatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18022,8 +18022,8 @@
   {
     "name": "iNatAg-mini/combretum_micranthum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18043,8 +18043,8 @@
   {
     "name": "iNatAg-mini/combretum_molle",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18064,8 +18064,8 @@
   {
     "name": "iNatAg-mini/commelina_bengalensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18085,8 +18085,8 @@
   {
     "name": "iNatAg-mini/commelina_benghalensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18106,8 +18106,8 @@
   {
     "name": "iNatAg-mini/commelina_communis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18127,8 +18127,8 @@
   {
     "name": "iNatAg-mini/commelina_erecta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18148,8 +18148,8 @@
   {
     "name": "iNatAg-mini/commiphora_africana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18169,8 +18169,8 @@
   {
     "name": "iNatAg-mini/conium_maculatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18190,8 +18190,8 @@
   {
     "name": "iNatAg-mini/conocarpus_erectus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18211,8 +18211,8 @@
   {
     "name": "iNatAg-mini/conocarpus_lancifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18232,8 +18232,8 @@
   {
     "name": "iNatAg-mini/convallaria_majalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18253,8 +18253,8 @@
   {
     "name": "iNatAg-mini/convolvulus_althaeoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18274,8 +18274,8 @@
   {
     "name": "iNatAg-mini/convolvulus_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18295,8 +18295,8 @@
   {
     "name": "iNatAg-mini/convolvulus_equitans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18316,8 +18316,8 @@
   {
     "name": "iNatAg-mini/convolvulus_sepium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18337,8 +18337,8 @@
   {
     "name": "iNatAg-mini/copaifera_langsdorffii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18358,8 +18358,8 @@
   {
     "name": "iNatAg-mini/corchorus_aestuans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18379,8 +18379,8 @@
   {
     "name": "iNatAg-mini/corchorus_capsularis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18400,8 +18400,8 @@
   {
     "name": "iNatAg-mini/cordia_africana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18421,8 +18421,8 @@
   {
     "name": "iNatAg-mini/cordia_alliodora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18442,8 +18442,8 @@
   {
     "name": "iNatAg-mini/coreopsis_lanceolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18463,8 +18463,8 @@
   {
     "name": "iNatAg-mini/coreopsis_tinctoria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18484,8 +18484,8 @@
   {
     "name": "iNatAg-mini/coreopsis_verticillata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18505,8 +18505,8 @@
   {
     "name": "iNatAg-mini/coriandrum_sativum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18526,8 +18526,8 @@
   {
     "name": "iNatAg-mini/corispermum_hyssopifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18547,8 +18547,8 @@
   {
     "name": "iNatAg-mini/corispermum_villosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18568,8 +18568,8 @@
   {
     "name": "iNatAg-mini/cornus_canadensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18589,8 +18589,8 @@
   {
     "name": "iNatAg-mini/cornus_florida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18610,8 +18610,8 @@
   {
     "name": "iNatAg-mini/cornus_mas",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18631,8 +18631,8 @@
   {
     "name": "iNatAg-mini/cornus_sanguinea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18652,8 +18652,8 @@
   {
     "name": "iNatAg-mini/coronilla_varia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18673,8 +18673,8 @@
   {
     "name": "iNatAg-mini/corylus_avellana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18694,8 +18694,8 @@
   {
     "name": "iNatAg-mini/corylus_maxima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18715,8 +18715,8 @@
   {
     "name": "iNatAg-mini/cotoneaster_franchetii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18736,8 +18736,8 @@
   {
     "name": "iNatAg-mini/cotula_coronopifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18757,8 +18757,8 @@
   {
     "name": "iNatAg-mini/crambe_cordifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18778,8 +18778,8 @@
   {
     "name": "iNatAg-mini/crambe_maritima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18799,8 +18799,8 @@
   {
     "name": "iNatAg-mini/crassula_sieberiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18820,8 +18820,8 @@
   {
     "name": "iNatAg-mini/crataegus_crus-galli",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18841,8 +18841,8 @@
   {
     "name": "iNatAg-mini/crataegus_crus-gallii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18862,8 +18862,8 @@
   {
     "name": "iNatAg-mini/crataegus_marshallii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18883,8 +18883,8 @@
   {
     "name": "iNatAg-mini/crataegus_monogyna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18904,8 +18904,8 @@
   {
     "name": "iNatAg-mini/crataegus_oxyacantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18925,8 +18925,8 @@
   {
     "name": "iNatAg-mini/crataegus_rivularis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18946,8 +18946,8 @@
   {
     "name": "iNatAg-mini/cratylia_argentea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18967,8 +18967,8 @@
   {
     "name": "iNatAg-mini/crepis_biennis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -18988,8 +18988,8 @@
   {
     "name": "iNatAg-mini/crepis_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19009,8 +19009,8 @@
   {
     "name": "iNatAg-mini/crepis_vesicaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19030,8 +19030,8 @@
   {
     "name": "iNatAg-mini/cressa_truxillensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19051,8 +19051,8 @@
   {
     "name": "iNatAg-mini/crinum_americanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19072,8 +19072,8 @@
   {
     "name": "iNatAg-mini/crithmum_maritimum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19093,8 +19093,8 @@
   {
     "name": "iNatAg-mini/crocus_sativus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19114,8 +19114,8 @@
   {
     "name": "iNatAg-mini/crotalaria_juncea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19135,8 +19135,8 @@
   {
     "name": "iNatAg-mini/crotalaria_lanceolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19156,8 +19156,8 @@
   {
     "name": "iNatAg-mini/crotalaria_pallida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19177,8 +19177,8 @@
   {
     "name": "iNatAg-mini/crotalaria_podocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19198,8 +19198,8 @@
   {
     "name": "iNatAg-mini/crotalaria_retusa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19219,8 +19219,8 @@
   {
     "name": "iNatAg-mini/crotalaria_sagittalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19240,8 +19240,8 @@
   {
     "name": "iNatAg-mini/crotalaria_spectabilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19261,8 +19261,8 @@
   {
     "name": "iNatAg-mini/croton_monanthogynus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19282,8 +19282,8 @@
   {
     "name": "iNatAg-mini/crucianella_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19303,8 +19303,8 @@
   {
     "name": "iNatAg-mini/cryptocarya_erythroxylon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19324,8 +19324,8 @@
   {
     "name": "iNatAg-mini/cryptomeria_japonica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19345,8 +19345,8 @@
   {
     "name": "iNatAg-mini/cryptotaenia_japonica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19366,8 +19366,8 @@
   {
     "name": "iNatAg-mini/ctenium_concinnum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19387,8 +19387,8 @@
   {
     "name": "iNatAg-mini/cucumis_anguria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19408,8 +19408,8 @@
   {
     "name": "iNatAg-mini/cucumis_melo",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19429,8 +19429,8 @@
   {
     "name": "iNatAg-mini/cucumis_sativus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19450,8 +19450,8 @@
   {
     "name": "iNatAg-mini/cucurbita_argyrosperma",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19471,8 +19471,8 @@
   {
     "name": "iNatAg-mini/cucurbita_digitata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19492,8 +19492,8 @@
   {
     "name": "iNatAg-mini/cucurbita_ficifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19513,8 +19513,8 @@
   {
     "name": "iNatAg-mini/cucurbita_foetidissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19534,8 +19534,8 @@
   {
     "name": "iNatAg-mini/cucurbita_maxima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19555,8 +19555,8 @@
   {
     "name": "iNatAg-mini/cucurbita_mixta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19576,8 +19576,8 @@
   {
     "name": "iNatAg-mini/cucurbita_moschata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19597,8 +19597,8 @@
   {
     "name": "iNatAg-mini/cucurbita_pepo",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19618,8 +19618,8 @@
   {
     "name": "iNatAg-mini/cunninghamia_lanceolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19639,8 +19639,8 @@
   {
     "name": "iNatAg-mini/cupania_auriculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19660,8 +19660,8 @@
   {
     "name": "iNatAg-mini/cuphea_viscosissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19681,8 +19681,8 @@
   {
     "name": "iNatAg-mini/cupressus_arizonica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19702,8 +19702,8 @@
   {
     "name": "iNatAg-mini/cupressus_lusitanica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19723,8 +19723,8 @@
   {
     "name": "iNatAg-mini/cupressus_macrocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19744,8 +19744,8 @@
   {
     "name": "iNatAg-mini/cupressus_sempervirens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19765,8 +19765,8 @@
   {
     "name": "iNatAg-mini/cupressus_torulosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19786,8 +19786,8 @@
   {
     "name": "iNatAg-mini/curcuma_longa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19807,8 +19807,8 @@
   {
     "name": "iNatAg-mini/curcuma_zedoaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19828,8 +19828,8 @@
   {
     "name": "iNatAg-mini/cuscuta_approximata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19849,8 +19849,8 @@
   {
     "name": "iNatAg-mini/cuscuta_epithymum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19870,8 +19870,8 @@
   {
     "name": "iNatAg-mini/cuscuta_obtusiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19891,8 +19891,8 @@
   {
     "name": "iNatAg-mini/cuscuta_planiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19912,8 +19912,8 @@
   {
     "name": "iNatAg-mini/cuscuta_sandwichiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19933,8 +19933,8 @@
   {
     "name": "iNatAg-mini/cydonia_oblonga",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19954,8 +19954,8 @@
   {
     "name": "iNatAg-mini/cymbalaria_muralis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19975,8 +19975,8 @@
   {
     "name": "iNatAg-mini/cymbopogon_citratus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -19996,8 +19996,8 @@
   {
     "name": "iNatAg-mini/cynanchum_scoparium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20017,8 +20017,8 @@
   {
     "name": "iNatAg-mini/cynara_cardunculus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20038,8 +20038,8 @@
   {
     "name": "iNatAg-mini/cynara_scolymus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20059,8 +20059,8 @@
   {
     "name": "iNatAg-mini/cynodon_dactylon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20080,8 +20080,8 @@
   {
     "name": "iNatAg-mini/cynodon_nlemfuensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20101,8 +20101,8 @@
   {
     "name": "iNatAg-mini/cynoglossum_officinale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20122,8 +20122,8 @@
   {
     "name": "iNatAg-mini/cynometra_cauliflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20143,8 +20143,8 @@
   {
     "name": "iNatAg-mini/cynosurus_cristatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20164,8 +20164,8 @@
   {
     "name": "iNatAg-mini/cyperus_alopecuroides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20185,8 +20185,8 @@
   {
     "name": "iNatAg-mini/cyperus_articulatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20206,8 +20206,8 @@
   {
     "name": "iNatAg-mini/cyperus_compressus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20227,8 +20227,8 @@
   {
     "name": "iNatAg-mini/cyperus_croceus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20248,8 +20248,8 @@
   {
     "name": "iNatAg-mini/cyperus_cuspidatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20269,8 +20269,8 @@
   {
     "name": "iNatAg-mini/cyperus_difformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20290,8 +20290,8 @@
   {
     "name": "iNatAg-mini/cyperus_eragrostis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20311,8 +20311,8 @@
   {
     "name": "iNatAg-mini/cyperus_erythrorhizos",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20332,8 +20332,8 @@
   {
     "name": "iNatAg-mini/cyperus_esculentus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20353,8 +20353,8 @@
   {
     "name": "iNatAg-mini/cyperus_flavescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20374,8 +20374,8 @@
   {
     "name": "iNatAg-mini/cyperus_fuscus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20395,8 +20395,8 @@
   {
     "name": "iNatAg-mini/cyperus_hyalinus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20416,8 +20416,8 @@
   {
     "name": "iNatAg-mini/cyperus_involucratus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20437,8 +20437,8 @@
   {
     "name": "iNatAg-mini/cyperus_iria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20458,8 +20458,8 @@
   {
     "name": "iNatAg-mini/cyperus_lanceolatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20479,8 +20479,8 @@
   {
     "name": "iNatAg-mini/cyperus_longus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20500,8 +20500,8 @@
   {
     "name": "iNatAg-mini/cyperus_odoratus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20521,8 +20521,8 @@
   {
     "name": "iNatAg-mini/cyperus_pilosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20542,8 +20542,8 @@
   {
     "name": "iNatAg-mini/cyperus_prolifer",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20563,8 +20563,8 @@
   {
     "name": "iNatAg-mini/cyperus_pseudovegetus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20584,8 +20584,8 @@
   {
     "name": "iNatAg-mini/cyperus_rotundus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20605,8 +20605,8 @@
   {
     "name": "iNatAg-mini/cyperus_sanguinolentus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20626,8 +20626,8 @@
   {
     "name": "iNatAg-mini/cyperus_squarrosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20647,8 +20647,8 @@
   {
     "name": "iNatAg-mini/cyperus_strigosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20668,8 +20668,8 @@
   {
     "name": "iNatAg-mini/cyperus_subsquarrosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20689,8 +20689,8 @@
   {
     "name": "iNatAg-mini/cyperus_surinamensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20710,8 +20710,8 @@
   {
     "name": "iNatAg-mini/cyphomandra_betacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20731,8 +20731,8 @@
   {
     "name": "iNatAg-mini/cytisus_albus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20752,8 +20752,8 @@
   {
     "name": "iNatAg-mini/cytisus_proliferus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20773,8 +20773,8 @@
   {
     "name": "iNatAg-mini/cytisus_supinus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20794,8 +20794,8 @@
   {
     "name": "iNatAg-mini/dacrydium_franklinii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20815,8 +20815,8 @@
   {
     "name": "iNatAg-mini/dactylis_glomerata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20836,8 +20836,8 @@
   {
     "name": "iNatAg-mini/dactyloctenium_aegyptium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20857,8 +20857,8 @@
   {
     "name": "iNatAg-mini/dactyloctenium_giganteum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20878,8 +20878,8 @@
   {
     "name": "iNatAg-mini/dalbergia_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20899,8 +20899,8 @@
   {
     "name": "iNatAg-mini/dalbergia_melanoxylon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20920,8 +20920,8 @@
   {
     "name": "iNatAg-mini/dalbergia_sissoo",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20941,8 +20941,8 @@
   {
     "name": "iNatAg-mini/daphne_laureola",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20962,8 +20962,8 @@
   {
     "name": "iNatAg-mini/daphne_mezereum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -20983,8 +20983,8 @@
   {
     "name": "iNatAg-mini/datura_ferox",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21004,8 +21004,8 @@
   {
     "name": "iNatAg-mini/datura_quercifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21025,8 +21025,8 @@
   {
     "name": "iNatAg-mini/datura_stramonium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21046,8 +21046,8 @@
   {
     "name": "iNatAg-mini/daucus_carota",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21067,8 +21067,8 @@
   {
     "name": "iNatAg-mini/daucus_carrota",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21088,8 +21088,8 @@
   {
     "name": "iNatAg-mini/delairea_odorata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21109,8 +21109,8 @@
   {
     "name": "iNatAg-mini/delonix_regia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21130,8 +21130,8 @@
   {
     "name": "iNatAg-mini/delphinium_bicolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21151,8 +21151,8 @@
   {
     "name": "iNatAg-mini/delphinium_carolinianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21172,8 +21172,8 @@
   {
     "name": "iNatAg-mini/delphinium_menziesii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21193,8 +21193,8 @@
   {
     "name": "iNatAg-mini/delphinium_trolliifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21214,8 +21214,8 @@
   {
     "name": "iNatAg-mini/dendrocalamus_asper",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21235,8 +21235,8 @@
   {
     "name": "iNatAg-mini/dendrocalamus_giganteus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21256,8 +21256,8 @@
   {
     "name": "iNatAg-mini/dendrocalamus_strictus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21277,8 +21277,8 @@
   {
     "name": "iNatAg-mini/dendrolobium_umbellatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21298,8 +21298,8 @@
   {
     "name": "iNatAg-mini/derris_elliptica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21319,8 +21319,8 @@
   {
     "name": "iNatAg-mini/deschampsia_caespitosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21340,8 +21340,8 @@
   {
     "name": "iNatAg-mini/deschampsia_flexuosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21361,8 +21361,8 @@
   {
     "name": "iNatAg-mini/desmanthus_leptophyllus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21382,8 +21382,8 @@
   {
     "name": "iNatAg-mini/desmanthus_virgatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21403,8 +21403,8 @@
   {
     "name": "iNatAg-mini/desmodium_affine",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21424,8 +21424,8 @@
   {
     "name": "iNatAg-mini/desmodium_barbatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21445,8 +21445,8 @@
   {
     "name": "iNatAg-mini/desmodium_cuneatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21466,8 +21466,8 @@
   {
     "name": "iNatAg-mini/desmodium_cuspidatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21487,8 +21487,8 @@
   {
     "name": "iNatAg-mini/desmodium_distortum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21508,8 +21508,8 @@
   {
     "name": "iNatAg-mini/desmodium_gyroides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21529,8 +21529,8 @@
   {
     "name": "iNatAg-mini/desmodium_heterophyllum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21550,8 +21550,8 @@
   {
     "name": "iNatAg-mini/desmodium_incanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21571,8 +21571,8 @@
   {
     "name": "iNatAg-mini/desmodium_intortum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21592,8 +21592,8 @@
   {
     "name": "iNatAg-mini/desmodium_paniculatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21613,8 +21613,8 @@
   {
     "name": "iNatAg-mini/desmodium_psilocarpum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21634,8 +21634,8 @@
   {
     "name": "iNatAg-mini/desmodium_reticulatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21655,8 +21655,8 @@
   {
     "name": "iNatAg-mini/desmodium_sandwicense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21676,8 +21676,8 @@
   {
     "name": "iNatAg-mini/desmodium_scorpiurus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21697,8 +21697,8 @@
   {
     "name": "iNatAg-mini/desmodium_tortuosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21718,8 +21718,8 @@
   {
     "name": "iNatAg-mini/desmodium_triflorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21739,8 +21739,8 @@
   {
     "name": "iNatAg-mini/desmodium_uncinatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21760,8 +21760,8 @@
   {
     "name": "iNatAg-mini/desmodium_velutinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21781,8 +21781,8 @@
   {
     "name": "iNatAg-mini/dialium_guineense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21802,8 +21802,8 @@
   {
     "name": "iNatAg-mini/dianthus_armeria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21823,8 +21823,8 @@
   {
     "name": "iNatAg-mini/dichanthium_annulatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21844,8 +21844,8 @@
   {
     "name": "iNatAg-mini/dichanthium_aristatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21865,8 +21865,8 @@
   {
     "name": "iNatAg-mini/dichanthium_caricosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21886,8 +21886,8 @@
   {
     "name": "iNatAg-mini/dichanthium_sericeum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21907,8 +21907,8 @@
   {
     "name": "iNatAg-mini/dichondra_carolinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21928,8 +21928,8 @@
   {
     "name": "iNatAg-mini/dichondra_micrantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21949,8 +21949,8 @@
   {
     "name": "iNatAg-mini/dichrostachys_cinerea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21970,8 +21970,8 @@
   {
     "name": "iNatAg-mini/dictamnus_albus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -21991,8 +21991,8 @@
   {
     "name": "iNatAg-mini/didymopanax_morototoni",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22012,8 +22012,8 @@
   {
     "name": "iNatAg-mini/diervilla_lonicera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22033,8 +22033,8 @@
   {
     "name": "iNatAg-mini/digitalis_lanata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22054,8 +22054,8 @@
   {
     "name": "iNatAg-mini/digitalis_lutea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22075,8 +22075,8 @@
   {
     "name": "iNatAg-mini/digitalis_purpurea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22096,8 +22096,8 @@
   {
     "name": "iNatAg-mini/digitaria_argyrograpta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22117,8 +22117,8 @@
   {
     "name": "iNatAg-mini/digitaria_ciliaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22138,8 +22138,8 @@
   {
     "name": "iNatAg-mini/digitaria_decumbens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22159,8 +22159,8 @@
   {
     "name": "iNatAg-mini/digitaria_didactyla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22180,8 +22180,8 @@
   {
     "name": "iNatAg-mini/digitaria_eriantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22201,8 +22201,8 @@
   {
     "name": "iNatAg-mini/digitaria_tricholaenoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22222,8 +22222,8 @@
   {
     "name": "iNatAg-mini/digitaria_violascens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22243,8 +22243,8 @@
   {
     "name": "iNatAg-mini/dillenia_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22264,8 +22264,8 @@
   {
     "name": "iNatAg-mini/dillenia_pentagyna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22285,8 +22285,8 @@
   {
     "name": "iNatAg-mini/diodia_virginiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22306,8 +22306,8 @@
   {
     "name": "iNatAg-mini/dioscorea_alata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22327,8 +22327,8 @@
   {
     "name": "iNatAg-mini/dioscorea_bulbifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22348,8 +22348,8 @@
   {
     "name": "iNatAg-mini/dioscorea_esculenta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22369,8 +22369,8 @@
   {
     "name": "iNatAg-mini/dioscorea_opposita",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22390,8 +22390,8 @@
   {
     "name": "iNatAg-mini/dioscorea_oppositifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22411,8 +22411,8 @@
   {
     "name": "iNatAg-mini/dioscorea_trifida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22432,8 +22432,8 @@
   {
     "name": "iNatAg-mini/diospyros_digyna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22453,8 +22453,8 @@
   {
     "name": "iNatAg-mini/diospyros_kaki",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22474,8 +22474,8 @@
   {
     "name": "iNatAg-mini/diospyros_malabarica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22495,8 +22495,8 @@
   {
     "name": "iNatAg-mini/diospyros_melanoxylon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22516,8 +22516,8 @@
   {
     "name": "iNatAg-mini/diospyros_mespiliformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22537,8 +22537,8 @@
   {
     "name": "iNatAg-mini/diospyros_virginiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22558,8 +22558,8 @@
   {
     "name": "iNatAg-mini/diplachne_fusca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22579,8 +22579,8 @@
   {
     "name": "iNatAg-mini/diploglottis_cunninghamii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22600,8 +22600,8 @@
   {
     "name": "iNatAg-mini/dipsacus_fullonum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22621,8 +22621,8 @@
   {
     "name": "iNatAg-mini/dipsacus_laciniatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22642,8 +22642,8 @@
   {
     "name": "iNatAg-mini/dipsacus_sylvestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22663,8 +22663,8 @@
   {
     "name": "iNatAg-mini/dipterocarpus_alatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22684,8 +22684,8 @@
   {
     "name": "iNatAg-mini/dipterocarpus_indicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22705,8 +22705,8 @@
   {
     "name": "iNatAg-mini/dipterocarpus_turbinatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22726,8 +22726,8 @@
   {
     "name": "iNatAg-mini/dodonaea_viscosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22747,8 +22747,8 @@
   {
     "name": "iNatAg-mini/dovyalis_caffra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22768,8 +22768,8 @@
   {
     "name": "iNatAg-mini/dovyalis_hebecarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22789,8 +22789,8 @@
   {
     "name": "iNatAg-mini/draba_nemorosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22810,8 +22810,8 @@
   {
     "name": "iNatAg-mini/draba_verna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22831,8 +22831,8 @@
   {
     "name": "iNatAg-mini/dracocephalum_parviflorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22852,8 +22852,8 @@
   {
     "name": "iNatAg-mini/dracocephalum_thymiflorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22873,8 +22873,8 @@
   {
     "name": "iNatAg-mini/drosera_rotundifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22894,8 +22894,8 @@
   {
     "name": "iNatAg-mini/dryopteris_filix-mas",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22915,8 +22915,8 @@
   {
     "name": "iNatAg-mini/duboisia_myoporoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22936,8 +22936,8 @@
   {
     "name": "iNatAg-mini/durio_zibethinus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22957,8 +22957,8 @@
   {
     "name": "iNatAg-mini/dysoxylum_fraserianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22978,8 +22978,8 @@
   {
     "name": "iNatAg-mini/ecballium_elaterium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -22999,8 +22999,8 @@
   {
     "name": "iNatAg-mini/echinacea_purpurea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23020,8 +23020,8 @@
   {
     "name": "iNatAg-mini/echinochloa_colona",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23041,8 +23041,8 @@
   {
     "name": "iNatAg-mini/echinochloa_crus-galli",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23062,8 +23062,8 @@
   {
     "name": "iNatAg-mini/echinochloa_frumentacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23083,8 +23083,8 @@
   {
     "name": "iNatAg-mini/echinochloa_polystachya",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23104,8 +23104,8 @@
   {
     "name": "iNatAg-mini/echinochloa_pyramidalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23125,8 +23125,8 @@
   {
     "name": "iNatAg-mini/echinops_sphaerocephalus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23146,8 +23146,8 @@
   {
     "name": "iNatAg-mini/echium_plantagineum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23167,8 +23167,8 @@
   {
     "name": "iNatAg-mini/echium_vulgare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23188,8 +23188,8 @@
   {
     "name": "iNatAg-mini/ehrharta_calycina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23209,8 +23209,8 @@
   {
     "name": "iNatAg-mini/ehrharta_erecta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23230,8 +23230,8 @@
   {
     "name": "iNatAg-mini/ehrharta_longiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23251,8 +23251,8 @@
   {
     "name": "iNatAg-mini/ehrharta_villosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23272,8 +23272,8 @@
   {
     "name": "iNatAg-mini/eichhornia_crassipes",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23293,8 +23293,8 @@
   {
     "name": "iNatAg-mini/ekebergia_capensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23314,8 +23314,8 @@
   {
     "name": "iNatAg-mini/elaeagnus_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23335,8 +23335,8 @@
   {
     "name": "iNatAg-mini/elaeagnus_multiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23356,8 +23356,8 @@
   {
     "name": "iNatAg-mini/elaeis_guineensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23377,8 +23377,8 @@
   {
     "name": "iNatAg-mini/elaeis_oleifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23398,8 +23398,8 @@
   {
     "name": "iNatAg-mini/elaeocarpus_grandis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23419,8 +23419,8 @@
   {
     "name": "iNatAg-mini/eleagnus_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23440,8 +23440,8 @@
   {
     "name": "iNatAg-mini/elegia_cuspidata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23461,8 +23461,8 @@
   {
     "name": "iNatAg-mini/eleocharis_cellulosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23482,8 +23482,8 @@
   {
     "name": "iNatAg-mini/eleocharis_dulcis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23503,8 +23503,8 @@
   {
     "name": "iNatAg-mini/eleocharis_macrostachya",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23524,8 +23524,8 @@
   {
     "name": "iNatAg-mini/eleocharis_montevidensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23545,8 +23545,8 @@
   {
     "name": "iNatAg-mini/eleocharis_vivipara",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23566,8 +23566,8 @@
   {
     "name": "iNatAg-mini/elephantopus_mollis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23587,8 +23587,8 @@
   {
     "name": "iNatAg-mini/elephantorrhiza_elephantina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23608,8 +23608,8 @@
   {
     "name": "iNatAg-mini/elettaria_cardamomum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23629,8 +23629,8 @@
   {
     "name": "iNatAg-mini/eleusine_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23650,8 +23650,8 @@
   {
     "name": "iNatAg-mini/ellisia_nyctelea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23671,8 +23671,8 @@
   {
     "name": "iNatAg-mini/elsholtzia_ciliata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23692,8 +23692,8 @@
   {
     "name": "iNatAg-mini/elymus_canadensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23713,8 +23713,8 @@
   {
     "name": "iNatAg-mini/elymus_caput-medusae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23734,8 +23734,8 @@
   {
     "name": "iNatAg-mini/elymus_cinereus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23755,8 +23755,8 @@
   {
     "name": "iNatAg-mini/elymus_condensatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23776,8 +23776,8 @@
   {
     "name": "iNatAg-mini/elymus_dahuricus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23797,8 +23797,8 @@
   {
     "name": "iNatAg-mini/elymus_glaucus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23818,8 +23818,8 @@
   {
     "name": "iNatAg-mini/elymus_viginicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23839,8 +23839,8 @@
   {
     "name": "iNatAg-mini/elymus_virginicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23860,8 +23860,8 @@
   {
     "name": "iNatAg-mini/emilia_sonchifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23881,8 +23881,8 @@
   {
     "name": "iNatAg-mini/encalypta_intermedia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23902,8 +23902,8 @@
   {
     "name": "iNatAg-mini/enneapogon_scoparius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23923,8 +23923,8 @@
   {
     "name": "iNatAg-mini/ensete_ventricosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23944,8 +23944,8 @@
   {
     "name": "iNatAg-mini/entada_abyssinica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23965,8 +23965,8 @@
   {
     "name": "iNatAg-mini/entada_africana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -23986,8 +23986,8 @@
   {
     "name": "iNatAg-mini/enterolobium_cyclocarpum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24007,8 +24007,8 @@
   {
     "name": "iNatAg-mini/epilobium_angustifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24028,8 +24028,8 @@
   {
     "name": "iNatAg-mini/epilobium_ciliatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24049,8 +24049,8 @@
   {
     "name": "iNatAg-mini/equisetum_arvense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24070,8 +24070,8 @@
   {
     "name": "iNatAg-mini/equisetum_hyemale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24091,8 +24091,8 @@
   {
     "name": "iNatAg-mini/equisetum_palustre",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24112,8 +24112,8 @@
   {
     "name": "iNatAg-mini/equisetum_sylvaticum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24133,8 +24133,8 @@
   {
     "name": "iNatAg-mini/equisetum_telmateia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24154,8 +24154,8 @@
   {
     "name": "iNatAg-mini/eragrostis_amabilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24175,8 +24175,8 @@
   {
     "name": "iNatAg-mini/eragrostis_barrelieri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24196,8 +24196,8 @@
   {
     "name": "iNatAg-mini/eragrostis_capillaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24217,8 +24217,8 @@
   {
     "name": "iNatAg-mini/eragrostis_chloromelas",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24238,8 +24238,8 @@
   {
     "name": "iNatAg-mini/eragrostis_cilianensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24259,8 +24259,8 @@
   {
     "name": "iNatAg-mini/eragrostis_curvula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24280,8 +24280,8 @@
   {
     "name": "iNatAg-mini/eragrostis_interrupta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24301,8 +24301,8 @@
   {
     "name": "iNatAg-mini/eragrostis_lehmanniana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24322,8 +24322,8 @@
   {
     "name": "iNatAg-mini/eragrostis_minor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24343,8 +24343,8 @@
   {
     "name": "iNatAg-mini/eragrostis_obtusa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24364,8 +24364,8 @@
   {
     "name": "iNatAg-mini/eragrostis_pilosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24385,8 +24385,8 @@
   {
     "name": "iNatAg-mini/eragrostis_racemosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24406,8 +24406,8 @@
   {
     "name": "iNatAg-mini/eragrostis_superba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24427,8 +24427,8 @@
   {
     "name": "iNatAg-mini/eragrostis_tef",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24448,8 +24448,8 @@
   {
     "name": "iNatAg-mini/eragrostis_tremula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24469,8 +24469,8 @@
   {
     "name": "iNatAg-mini/eragrostis_trichodes",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24490,8 +24490,8 @@
   {
     "name": "iNatAg-mini/eragrostis_unioloides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24511,8 +24511,8 @@
   {
     "name": "iNatAg-mini/eremochloa_ophiuroides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24532,8 +24532,8 @@
   {
     "name": "iNatAg-mini/erigeron_canadensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24553,8 +24553,8 @@
   {
     "name": "iNatAg-mini/erigeron_cascadensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24574,8 +24574,8 @@
   {
     "name": "iNatAg-mini/erigeron_divaricatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24595,8 +24595,8 @@
   {
     "name": "iNatAg-mini/erigeron_philadelphicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24616,8 +24616,8 @@
   {
     "name": "iNatAg-mini/eriobotrya_japonica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24637,8 +24637,8 @@
   {
     "name": "iNatAg-mini/eriochloa_punctata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24658,8 +24658,8 @@
   {
     "name": "iNatAg-mini/eriogonum_deflexum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24679,8 +24679,8 @@
   {
     "name": "iNatAg-mini/eriogonum_longifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24700,8 +24700,8 @@
   {
     "name": "iNatAg-mini/eriosema_psoraleoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24721,8 +24721,8 @@
   {
     "name": "iNatAg-mini/eruca_sativa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24742,8 +24742,8 @@
   {
     "name": "iNatAg-mini/eryngium_campestre",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24763,8 +24763,8 @@
   {
     "name": "iNatAg-mini/eryngium_yuccifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24784,8 +24784,8 @@
   {
     "name": "iNatAg-mini/erysimum_cheiranthoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24805,8 +24805,8 @@
   {
     "name": "iNatAg-mini/erysimum_hieracifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24826,8 +24826,8 @@
   {
     "name": "iNatAg-mini/erysimum_hieraciifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24847,8 +24847,8 @@
   {
     "name": "iNatAg-mini/erysimum_repandum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24868,8 +24868,8 @@
   {
     "name": "iNatAg-mini/erythrina_abyssinica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24889,8 +24889,8 @@
   {
     "name": "iNatAg-mini/erythrina_caffra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24910,8 +24910,8 @@
   {
     "name": "iNatAg-mini/erythrina_edulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24931,8 +24931,8 @@
   {
     "name": "iNatAg-mini/erythrina_fusca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24952,8 +24952,8 @@
   {
     "name": "iNatAg-mini/erythrina_poeppigiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24973,8 +24973,8 @@
   {
     "name": "iNatAg-mini/erythrina_variegata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -24994,8 +24994,8 @@
   {
     "name": "iNatAg-mini/erythrina_vespertilio",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25015,8 +25015,8 @@
   {
     "name": "iNatAg-mini/erythrophleum_chlorostachys",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25036,8 +25036,8 @@
   {
     "name": "iNatAg-mini/erythroxylum_coca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25057,8 +25057,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_accedens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25078,8 +25078,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_agglomerata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25099,8 +25099,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_albens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25120,8 +25120,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_astringens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25141,8 +25141,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_bosistoana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25162,8 +25162,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_botryoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25183,8 +25183,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_brockwayi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25204,8 +25204,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_calophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25225,8 +25225,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_camaldulensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25246,8 +25246,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_cinerea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25267,8 +25267,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_citriodora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25288,8 +25288,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_cladocalyx",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25309,8 +25309,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_cloeziana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25330,8 +25330,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_consideniana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25351,8 +25351,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_cornuta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25372,8 +25372,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_crebra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25393,8 +25393,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_cypellocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25414,8 +25414,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_dalrympleana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25435,8 +25435,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_deglupta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25456,8 +25456,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_delegatensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25477,8 +25477,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_diversicolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25498,8 +25498,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_dumosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25519,8 +25519,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_elata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25540,8 +25540,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_eremophila",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25561,8 +25561,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_eugenioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25582,8 +25582,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_exserta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25603,8 +25603,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_fastigata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25624,8 +25624,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_fraxinoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25645,8 +25645,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_globoidea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25666,8 +25666,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_globulus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25687,8 +25687,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_gomphocephala",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25708,8 +25708,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_gongylocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25729,8 +25729,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_grandis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25750,8 +25750,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_guilfoylei",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25771,8 +25771,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_gummifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25792,8 +25792,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_intertexta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25813,8 +25813,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_jacksonii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25834,8 +25834,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_johnstonii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25855,8 +25855,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_kessellii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25876,8 +25876,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_laophila",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25897,8 +25897,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_largiflorens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25918,8 +25918,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_leucoxylon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25939,8 +25939,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_longifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25960,8 +25960,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_loxophleba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -25981,8 +25981,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_maculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26002,8 +26002,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_marginata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26023,8 +26023,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_melliodora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26044,8 +26044,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_microcarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26065,8 +26065,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_microcorys",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26086,8 +26086,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_microtheca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26107,8 +26107,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_mitchelliana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26128,8 +26128,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_moluccana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26149,8 +26149,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_muelleriana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26170,8 +26170,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_nigrifunda",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26191,8 +26191,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_niphophila",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26212,8 +26212,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_nitens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26233,8 +26233,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_obliqua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26254,8 +26254,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26275,8 +26275,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_ochrophloia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26296,8 +26296,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_oreades",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26317,8 +26317,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_paniculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26338,8 +26338,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_papuana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26359,8 +26359,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_patens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26380,8 +26380,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_pauciflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26401,8 +26401,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_pellita",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26422,8 +26422,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_phoenicea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26443,8 +26443,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_pilularis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26464,8 +26464,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_piperita",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26485,8 +26485,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_planchoniana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26506,8 +26506,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_pleurocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26527,8 +26527,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_polyanthemos",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26548,8 +26548,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_populnea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26569,8 +26569,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_propinqua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26590,8 +26590,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_pulchella",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26611,8 +26611,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_punctata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26632,8 +26632,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_pyrocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26653,8 +26653,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_quadrangulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26674,8 +26674,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_regnans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26695,8 +26695,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_resinifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26716,8 +26716,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_robusta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26737,8 +26737,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_rubida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26758,8 +26758,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_rudis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26779,8 +26779,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_saligna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26800,8 +26800,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_salmonophloia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26821,8 +26821,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_salubris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26842,8 +26842,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_sargentii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26863,8 +26863,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_scias",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26884,8 +26884,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_sideroxylon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26905,8 +26905,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_sieberi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26926,8 +26926,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_socialis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26947,8 +26947,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_subcrenulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26968,8 +26968,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_tereticornis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -26989,8 +26989,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_thozetiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27010,8 +27010,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_transcontinentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27031,8 +27031,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_trivalva",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27052,8 +27052,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_urnigera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27073,8 +27073,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_urophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27094,8 +27094,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_utilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27115,8 +27115,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_viminalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27136,8 +27136,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_wandoo",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27157,8 +27157,8 @@
   {
     "name": "iNatAg-mini/eucalyptus_woollsiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27178,8 +27178,8 @@
   {
     "name": "iNatAg-mini/eucryphia_lucida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27199,8 +27199,8 @@
   {
     "name": "iNatAg-mini/eugenia_aromatica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27220,8 +27220,8 @@
   {
     "name": "iNatAg-mini/eugenia_stipitata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27241,8 +27241,8 @@
   {
     "name": "iNatAg-mini/eugenia_uniflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27262,8 +27262,8 @@
   {
     "name": "iNatAg-mini/euonymus_atropurpureus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27283,8 +27283,8 @@
   {
     "name": "iNatAg-mini/euonymus_europaeus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27304,8 +27304,8 @@
   {
     "name": "iNatAg-mini/euonymus_japonicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27325,8 +27325,8 @@
   {
     "name": "iNatAg-mini/eupatorium_album",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27346,8 +27346,8 @@
   {
     "name": "iNatAg-mini/eupatorium_altissimum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27367,8 +27367,8 @@
   {
     "name": "iNatAg-mini/eupatorium_cannabinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27388,8 +27388,8 @@
   {
     "name": "iNatAg-mini/eupatorium_compositifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27409,8 +27409,8 @@
   {
     "name": "iNatAg-mini/eupatorium_hyssopifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27430,8 +27430,8 @@
   {
     "name": "iNatAg-mini/eupatorium_maculatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27451,8 +27451,8 @@
   {
     "name": "iNatAg-mini/eupatorium_perfoliatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27472,8 +27472,8 @@
   {
     "name": "iNatAg-mini/eupatorium_purpureum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27493,8 +27493,8 @@
   {
     "name": "iNatAg-mini/eupatorium_serotinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27514,8 +27514,8 @@
   {
     "name": "iNatAg-mini/euphorbia_cyathophora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27535,8 +27535,8 @@
   {
     "name": "iNatAg-mini/euphorbia_cyparissias",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27556,8 +27556,8 @@
   {
     "name": "iNatAg-mini/euphorbia_dendroides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27577,8 +27577,8 @@
   {
     "name": "iNatAg-mini/euphorbia_epicyparissias",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27598,8 +27598,8 @@
   {
     "name": "iNatAg-mini/euphorbia_esula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27619,8 +27619,8 @@
   {
     "name": "iNatAg-mini/euphorbia_helioscopia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27640,8 +27640,8 @@
   {
     "name": "iNatAg-mini/euphorbia_heterophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27661,8 +27661,8 @@
   {
     "name": "iNatAg-mini/euphorbia_hirsuta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27682,8 +27682,8 @@
   {
     "name": "iNatAg-mini/euphorbia_hirta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27703,8 +27703,8 @@
   {
     "name": "iNatAg-mini/euphorbia_hyssopifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27724,8 +27724,8 @@
   {
     "name": "iNatAg-mini/euphorbia_lathyris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27745,8 +27745,8 @@
   {
     "name": "iNatAg-mini/euphorbia_lathyrus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27766,8 +27766,8 @@
   {
     "name": "iNatAg-mini/euphorbia_maculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27787,8 +27787,8 @@
   {
     "name": "iNatAg-mini/euphorbia_marginata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27808,8 +27808,8 @@
   {
     "name": "iNatAg-mini/euphorbia_nutans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27829,8 +27829,8 @@
   {
     "name": "iNatAg-mini/euphorbia_peplis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27850,8 +27850,8 @@
   {
     "name": "iNatAg-mini/euphorbia_peplus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27871,8 +27871,8 @@
   {
     "name": "iNatAg-mini/euphorbia_platyphyllos",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27892,8 +27892,8 @@
   {
     "name": "iNatAg-mini/euphorbia_prostata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27913,8 +27913,8 @@
   {
     "name": "iNatAg-mini/euphorbia_prostrata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27934,8 +27934,8 @@
   {
     "name": "iNatAg-mini/euphorbia_serphyllifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27955,8 +27955,8 @@
   {
     "name": "iNatAg-mini/euphorbia_serpyllifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27976,8 +27976,8 @@
   {
     "name": "iNatAg-mini/euphorbia_serrata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -27997,8 +27997,8 @@
   {
     "name": "iNatAg-mini/euphorbia_serrulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28018,8 +28018,8 @@
   {
     "name": "iNatAg-mini/euphorbia_spathulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28039,8 +28039,8 @@
   {
     "name": "iNatAg-mini/euphorbia_terracina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28060,8 +28060,8 @@
   {
     "name": "iNatAg-mini/euphorbia_tirucalli",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28081,8 +28081,8 @@
   {
     "name": "iNatAg-mini/euphorbia_vermiculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28102,8 +28102,8 @@
   {
     "name": "iNatAg-mini/eurycoma_longifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28123,8 +28123,8 @@
   {
     "name": "iNatAg-mini/eusideroxylon_zwageri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28144,8 +28144,8 @@
   {
     "name": "iNatAg-mini/eustachys_paspaloides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28165,8 +28165,8 @@
   {
     "name": "iNatAg-mini/euterpe_edulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28186,8 +28186,8 @@
   {
     "name": "iNatAg-mini/euterpe_oleracea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28207,8 +28207,8 @@
   {
     "name": "iNatAg-mini/euthamia_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28228,8 +28228,8 @@
   {
     "name": "iNatAg-mini/evax_multicaulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28249,8 +28249,8 @@
   {
     "name": "iNatAg-mini/evonymus_europaeus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28270,8 +28270,8 @@
   {
     "name": "iNatAg-mini/excoecaria_agallocha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28291,8 +28291,8 @@
   {
     "name": "iNatAg-mini/fagopyrum_esculentum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28312,8 +28312,8 @@
   {
     "name": "iNatAg-mini/fagopyrum_tataricum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28333,8 +28333,8 @@
   {
     "name": "iNatAg-mini/fagraea_fragrans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28354,8 +28354,8 @@
   {
     "name": "iNatAg-mini/fagus_grandifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28375,8 +28375,8 @@
   {
     "name": "iNatAg-mini/fagus_sylvatica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28396,8 +28396,8 @@
   {
     "name": "iNatAg-mini/faidherbia_albida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28417,8 +28417,8 @@
   {
     "name": "iNatAg-mini/faurea_saligna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28438,8 +28438,8 @@
   {
     "name": "iNatAg-mini/feijoa_sellowiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28459,8 +28459,8 @@
   {
     "name": "iNatAg-mini/festuca_arundinacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28480,8 +28480,8 @@
   {
     "name": "iNatAg-mini/festuca_gigantea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28501,8 +28501,8 @@
   {
     "name": "iNatAg-mini/festuca_idahoensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28522,8 +28522,8 @@
   {
     "name": "iNatAg-mini/festuca_microstachys",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28543,8 +28543,8 @@
   {
     "name": "iNatAg-mini/festuca_myuros",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28564,8 +28564,8 @@
   {
     "name": "iNatAg-mini/festuca_ovina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28585,8 +28585,8 @@
   {
     "name": "iNatAg-mini/festuca_pratensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28606,8 +28606,8 @@
   {
     "name": "iNatAg-mini/festuca_rubra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28627,8 +28627,8 @@
   {
     "name": "iNatAg-mini/festuca_scabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28648,8 +28648,8 @@
   {
     "name": "iNatAg-mini/fibraurea_tinctoria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28669,8 +28669,8 @@
   {
     "name": "iNatAg-mini/ficus_abutilifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28690,8 +28690,8 @@
   {
     "name": "iNatAg-mini/ficus_auriculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28711,8 +28711,8 @@
   {
     "name": "iNatAg-mini/ficus_benghalensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28732,8 +28732,8 @@
   {
     "name": "iNatAg-mini/ficus_carica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28753,8 +28753,8 @@
   {
     "name": "iNatAg-mini/ficus_elastica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28774,8 +28774,8 @@
   {
     "name": "iNatAg-mini/ficus_glumosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28795,8 +28795,8 @@
   {
     "name": "iNatAg-mini/ficus_macrophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28816,8 +28816,8 @@
   {
     "name": "iNatAg-mini/ficus_sycomorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28837,8 +28837,8 @@
   {
     "name": "iNatAg-mini/ficus_thonningii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28858,8 +28858,8 @@
   {
     "name": "iNatAg-mini/filago_gallica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28879,8 +28879,8 @@
   {
     "name": "iNatAg-mini/filipendula_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28900,8 +28900,8 @@
   {
     "name": "iNatAg-mini/flacourtia_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28921,8 +28921,8 @@
   {
     "name": "iNatAg-mini/flemingia_macrophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28942,8 +28942,8 @@
   {
     "name": "iNatAg-mini/flindersia_bourjotiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28963,8 +28963,8 @@
   {
     "name": "iNatAg-mini/flindersia_brayleyana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -28984,8 +28984,8 @@
   {
     "name": "iNatAg-mini/flindersia_pimenteliana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29005,8 +29005,8 @@
   {
     "name": "iNatAg-mini/foeniculum_vulgare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29026,8 +29026,8 @@
   {
     "name": "iNatAg-mini/fortunella_hindsii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29047,8 +29047,8 @@
   {
     "name": "iNatAg-mini/fortunella_japonica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29068,8 +29068,8 @@
   {
     "name": "iNatAg-mini/fortunella_margarita",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29089,8 +29089,8 @@
   {
     "name": "iNatAg-mini/fragaria_ananassa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29110,8 +29110,8 @@
   {
     "name": "iNatAg-mini/fragaria_chiloensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29131,8 +29131,8 @@
   {
     "name": "iNatAg-mini/fragaria_vesca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29152,8 +29152,8 @@
   {
     "name": "iNatAg-mini/fragaria_virginiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29173,8 +29173,8 @@
   {
     "name": "iNatAg-mini/frangula_alnus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29194,8 +29194,8 @@
   {
     "name": "iNatAg-mini/fraxinus_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29215,8 +29215,8 @@
   {
     "name": "iNatAg-mini/fraxinus_excelsior",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29236,8 +29236,8 @@
   {
     "name": "iNatAg-mini/frithia_humilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29257,8 +29257,8 @@
   {
     "name": "iNatAg-mini/fuirena_simplex",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29278,8 +29278,8 @@
   {
     "name": "iNatAg-mini/fumaria_capreolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29299,8 +29299,8 @@
   {
     "name": "iNatAg-mini/fumaria_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29320,8 +29320,8 @@
   {
     "name": "iNatAg-mini/fumaria_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29341,8 +29341,8 @@
   {
     "name": "iNatAg-mini/gaillardia_pulchella",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29362,8 +29362,8 @@
   {
     "name": "iNatAg-mini/galactia_marginalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29383,8 +29383,8 @@
   {
     "name": "iNatAg-mini/galactia_striata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29404,8 +29404,8 @@
   {
     "name": "iNatAg-mini/galega_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29425,8 +29425,8 @@
   {
     "name": "iNatAg-mini/galega_orientalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29446,8 +29446,8 @@
   {
     "name": "iNatAg-mini/galeopsis_ladanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29467,8 +29467,8 @@
   {
     "name": "iNatAg-mini/galeopsis_tetrahit",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29488,8 +29488,8 @@
   {
     "name": "iNatAg-mini/galinsoga_quadriradiata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29509,8 +29509,8 @@
   {
     "name": "iNatAg-mini/galium_aparine",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29530,8 +29530,8 @@
   {
     "name": "iNatAg-mini/galium_mollugo",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29551,8 +29551,8 @@
   {
     "name": "iNatAg-mini/galium_paniculatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29572,8 +29572,8 @@
   {
     "name": "iNatAg-mini/galium_parisiense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29593,8 +29593,8 @@
   {
     "name": "iNatAg-mini/galium_saxatile",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29614,8 +29614,8 @@
   {
     "name": "iNatAg-mini/galium_spurium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29635,8 +29635,8 @@
   {
     "name": "iNatAg-mini/galium_tricornutum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29656,8 +29656,8 @@
   {
     "name": "iNatAg-mini/galium_verum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29677,8 +29677,8 @@
   {
     "name": "iNatAg-mini/garcinia_dulcis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29698,8 +29698,8 @@
   {
     "name": "iNatAg-mini/garcinia_mangostana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29719,8 +29719,8 @@
   {
     "name": "iNatAg-mini/garcinia_multiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29740,8 +29740,8 @@
   {
     "name": "iNatAg-mini/garcinia_xanthochymus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29761,8 +29761,8 @@
   {
     "name": "iNatAg-mini/garuga_pinnata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29782,8 +29782,8 @@
   {
     "name": "iNatAg-mini/gaultheria_procumbens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29803,8 +29803,8 @@
   {
     "name": "iNatAg-mini/gaura_biennis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29824,8 +29824,8 @@
   {
     "name": "iNatAg-mini/geissois_benthamii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29845,8 +29845,8 @@
   {
     "name": "iNatAg-mini/genipa_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29866,8 +29866,8 @@
   {
     "name": "iNatAg-mini/genista_canariensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29887,8 +29887,8 @@
   {
     "name": "iNatAg-mini/genista_tinctoria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29908,8 +29908,8 @@
   {
     "name": "iNatAg-mini/gentiana_acaulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29929,8 +29929,8 @@
   {
     "name": "iNatAg-mini/gentiana_lutea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29950,8 +29950,8 @@
   {
     "name": "iNatAg-mini/geranium_carolinianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29971,8 +29971,8 @@
   {
     "name": "iNatAg-mini/geranium_dissectum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -29992,8 +29992,8 @@
   {
     "name": "iNatAg-mini/geranium_molle",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30013,8 +30013,8 @@
   {
     "name": "iNatAg-mini/geranium_pratense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30034,8 +30034,8 @@
   {
     "name": "iNatAg-mini/geranium_pusillum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30055,8 +30055,8 @@
   {
     "name": "iNatAg-mini/geranium_robertianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30076,8 +30076,8 @@
   {
     "name": "iNatAg-mini/girardinia_diversifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30097,8 +30097,8 @@
   {
     "name": "iNatAg-mini/glechoma_hederacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30118,8 +30118,8 @@
   {
     "name": "iNatAg-mini/glecoma_hederacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30139,8 +30139,8 @@
   {
     "name": "iNatAg-mini/gleditsia_triacanthos",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30160,8 +30160,8 @@
   {
     "name": "iNatAg-mini/gliricidia_sepium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30181,8 +30181,8 @@
   {
     "name": "iNatAg-mini/globularia_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30202,8 +30202,8 @@
   {
     "name": "iNatAg-mini/glyceria_fluitans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30223,8 +30223,8 @@
   {
     "name": "iNatAg-mini/glyceria_septentrionalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30244,8 +30244,8 @@
   {
     "name": "iNatAg-mini/glycine_max",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30265,8 +30265,8 @@
   {
     "name": "iNatAg-mini/glycyrrhiza_glabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30286,8 +30286,8 @@
   {
     "name": "iNatAg-mini/glycyrrhiza_lepidota",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30307,8 +30307,8 @@
   {
     "name": "iNatAg-mini/gmelina_arborea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30328,8 +30328,8 @@
   {
     "name": "iNatAg-mini/gmelina_leichhardtii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30349,8 +30349,8 @@
   {
     "name": "iNatAg-mini/gnaphalium_calviceps",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30370,8 +30370,8 @@
   {
     "name": "iNatAg-mini/gnaphalium_luteo-album",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30391,8 +30391,8 @@
   {
     "name": "iNatAg-mini/gnaphalium_luteoalbum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30412,8 +30412,8 @@
   {
     "name": "iNatAg-mini/gnaphalium_palustre",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30433,8 +30433,8 @@
   {
     "name": "iNatAg-mini/gnaphalium_pensylvanicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30454,8 +30454,8 @@
   {
     "name": "iNatAg-mini/gnaphalium_purpureum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30475,8 +30475,8 @@
   {
     "name": "iNatAg-mini/gnaphalium_uliginosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30496,8 +30496,8 @@
   {
     "name": "iNatAg-mini/gossypium_barbadense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30517,8 +30517,8 @@
   {
     "name": "iNatAg-mini/gossypium_herbaceum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30538,8 +30538,8 @@
   {
     "name": "iNatAg-mini/gossypium_hirsutum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30559,8 +30559,8 @@
   {
     "name": "iNatAg-mini/grevillea_parallela",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30580,8 +30580,8 @@
   {
     "name": "iNatAg-mini/grevillea_robusta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30601,8 +30601,8 @@
   {
     "name": "iNatAg-mini/grewia_asiatica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30622,8 +30622,8 @@
   {
     "name": "iNatAg-mini/grewia_bicolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30643,8 +30643,8 @@
   {
     "name": "iNatAg-mini/grewia_tiliifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30664,8 +30664,8 @@
   {
     "name": "iNatAg-mini/guaiacum_officinale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30685,8 +30685,8 @@
   {
     "name": "iNatAg-mini/guaiacum_sanctum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30706,8 +30706,8 @@
   {
     "name": "iNatAg-mini/guazuma_ulmifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30727,8 +30727,8 @@
   {
     "name": "iNatAg-mini/guizotia_abyssinica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30748,8 +30748,8 @@
   {
     "name": "iNatAg-mini/gunnera_tinctoria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30769,8 +30769,8 @@
   {
     "name": "iNatAg-mini/gypsophila_paniculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30790,8 +30790,8 @@
   {
     "name": "iNatAg-mini/hagenia_abyssinica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30811,8 +30811,8 @@
   {
     "name": "iNatAg-mini/hamamelis_virginiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30832,8 +30832,8 @@
   {
     "name": "iNatAg-mini/hardwickia_binata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30853,8 +30853,8 @@
   {
     "name": "iNatAg-mini/harpagophytum_procumbens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30874,8 +30874,8 @@
   {
     "name": "iNatAg-mini/harpochloa_falx",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30895,8 +30895,8 @@
   {
     "name": "iNatAg-mini/harungana_madagascariensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30916,8 +30916,8 @@
   {
     "name": "iNatAg-mini/hedera_helix",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30937,8 +30937,8 @@
   {
     "name": "iNatAg-mini/hedysarum_coronarium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30958,8 +30958,8 @@
   {
     "name": "iNatAg-mini/hedysarum_pallidum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -30979,8 +30979,8 @@
   {
     "name": "iNatAg-mini/hedysarum_spinosissimum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31000,8 +31000,8 @@
   {
     "name": "iNatAg-mini/helenium_autumnale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31021,8 +31021,8 @@
   {
     "name": "iNatAg-mini/helenium_tenuifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31042,8 +31042,8 @@
   {
     "name": "iNatAg-mini/helianthus_annus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31063,8 +31063,8 @@
   {
     "name": "iNatAg-mini/helianthus_annuus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31084,8 +31084,8 @@
   {
     "name": "iNatAg-mini/helianthus_ciliaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31105,8 +31105,8 @@
   {
     "name": "iNatAg-mini/helianthus_pauciflorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31126,8 +31126,8 @@
   {
     "name": "iNatAg-mini/helianthus_petiolaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31147,8 +31147,8 @@
   {
     "name": "iNatAg-mini/helianthus_tuberosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31168,8 +31168,8 @@
   {
     "name": "iNatAg-mini/helictotrichon_turgidulum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31189,8 +31189,8 @@
   {
     "name": "iNatAg-mini/heliotropium_amplexicaule",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31210,8 +31210,8 @@
   {
     "name": "iNatAg-mini/heliotropium_curassavicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31231,8 +31231,8 @@
   {
     "name": "iNatAg-mini/heliotropium_europaeum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31252,8 +31252,8 @@
   {
     "name": "iNatAg-mini/hemarthria_altissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31273,8 +31273,8 @@
   {
     "name": "iNatAg-mini/hemizonia_congesta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31294,8 +31294,8 @@
   {
     "name": "iNatAg-mini/heracleum_sphondylium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31315,8 +31315,8 @@
   {
     "name": "iNatAg-mini/heritiera_littoralis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31336,8 +31336,8 @@
   {
     "name": "iNatAg-mini/heteropogon_contortus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31357,8 +31357,8 @@
   {
     "name": "iNatAg-mini/heterotheca_grandiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31378,8 +31378,8 @@
   {
     "name": "iNatAg-mini/heuchera_mexicana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31399,8 +31399,8 @@
   {
     "name": "iNatAg-mini/hevea_brasiliensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31420,8 +31420,8 @@
   {
     "name": "iNatAg-mini/hibiscus_cannabinus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31441,8 +31441,8 @@
   {
     "name": "iNatAg-mini/hibiscus_sabdariffa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31462,8 +31462,8 @@
   {
     "name": "iNatAg-mini/hibiscus_syriacus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31483,8 +31483,8 @@
   {
     "name": "iNatAg-mini/hibiscus_tiliaceus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31504,8 +31504,8 @@
   {
     "name": "iNatAg-mini/hibiscus_tilliaceus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31525,8 +31525,8 @@
   {
     "name": "iNatAg-mini/hieracium_aurantiacum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31546,8 +31546,8 @@
   {
     "name": "iNatAg-mini/hieracium_gronovii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31567,8 +31567,8 @@
   {
     "name": "iNatAg-mini/hieracium_lachenalii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31588,8 +31588,8 @@
   {
     "name": "iNatAg-mini/hieracium_laevigatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31609,8 +31609,8 @@
   {
     "name": "iNatAg-mini/hieracium_murorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31630,8 +31630,8 @@
   {
     "name": "iNatAg-mini/hieracium_pilosella",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31651,8 +31651,8 @@
   {
     "name": "iNatAg-mini/hieracium_piloselloides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31672,8 +31672,8 @@
   {
     "name": "iNatAg-mini/hieracium_umbellatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31693,8 +31693,8 @@
   {
     "name": "iNatAg-mini/hieracium_venosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31714,8 +31714,8 @@
   {
     "name": "iNatAg-mini/hieracium_vulgatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31735,8 +31735,8 @@
   {
     "name": "iNatAg-mini/hierochloe_odorata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31756,8 +31756,8 @@
   {
     "name": "iNatAg-mini/hilaria_jamesii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31777,8 +31777,8 @@
   {
     "name": "iNatAg-mini/hilaria_mutica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31798,8 +31798,8 @@
   {
     "name": "iNatAg-mini/hippophae_rhamnoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31819,8 +31819,8 @@
   {
     "name": "iNatAg-mini/hippophae_salicifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31840,8 +31840,8 @@
   {
     "name": "iNatAg-mini/hippuris_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31861,8 +31861,8 @@
   {
     "name": "iNatAg-mini/holcus_lanatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31882,8 +31882,8 @@
   {
     "name": "iNatAg-mini/holcus_mollis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31903,8 +31903,8 @@
   {
     "name": "iNatAg-mini/hopea_odorata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31924,8 +31924,8 @@
   {
     "name": "iNatAg-mini/hopea_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31945,8 +31945,8 @@
   {
     "name": "iNatAg-mini/hopea_wightiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31966,8 +31966,8 @@
   {
     "name": "iNatAg-mini/hordeum_brachyantherum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -31987,8 +31987,8 @@
   {
     "name": "iNatAg-mini/hordeum_brevisubulatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32008,8 +32008,8 @@
   {
     "name": "iNatAg-mini/hordeum_bulbosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32029,8 +32029,8 @@
   {
     "name": "iNatAg-mini/hordeum_distichon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32050,8 +32050,8 @@
   {
     "name": "iNatAg-mini/hordeum_geniculatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32071,8 +32071,8 @@
   {
     "name": "iNatAg-mini/hordeum_jubatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32092,8 +32092,8 @@
   {
     "name": "iNatAg-mini/hordeum_murinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32113,8 +32113,8 @@
   {
     "name": "iNatAg-mini/hordeum_vulgare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32134,8 +32134,8 @@
   {
     "name": "iNatAg-mini/houstonia_caerulea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32155,8 +32155,8 @@
   {
     "name": "iNatAg-mini/humulus_lupulus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32176,8 +32176,8 @@
   {
     "name": "iNatAg-mini/hydnocarpus_alpina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32197,8 +32197,8 @@
   {
     "name": "iNatAg-mini/hydrocotyle_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32218,8 +32218,8 @@
   {
     "name": "iNatAg-mini/hydrocotyle_mexicana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32239,8 +32239,8 @@
   {
     "name": "iNatAg-mini/hydrocotyle_ranunculoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32260,8 +32260,8 @@
   {
     "name": "iNatAg-mini/hydrocotyle_sibthorpioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32281,8 +32281,8 @@
   {
     "name": "iNatAg-mini/hydrocotyle_umbellata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32302,8 +32302,8 @@
   {
     "name": "iNatAg-mini/hydrocotyle_verticillata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32323,8 +32323,8 @@
   {
     "name": "iNatAg-mini/hydrolea_uniflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32344,8 +32344,8 @@
   {
     "name": "iNatAg-mini/hylocereus_undatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32365,8 +32365,8 @@
   {
     "name": "iNatAg-mini/hymenaea_courbaril",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32386,8 +32386,8 @@
   {
     "name": "iNatAg-mini/hymenopappus_scabiosaeus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32407,8 +32407,8 @@
   {
     "name": "iNatAg-mini/hymenoxys_odorata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32428,8 +32428,8 @@
   {
     "name": "iNatAg-mini/hyosciamus_niger",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32449,8 +32449,8 @@
   {
     "name": "iNatAg-mini/hyoscyamus_niger",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32470,8 +32470,8 @@
   {
     "name": "iNatAg-mini/hyparrhenia_dregeana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32491,8 +32491,8 @@
   {
     "name": "iNatAg-mini/hyparrhenia_filipendula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32512,8 +32512,8 @@
   {
     "name": "iNatAg-mini/hyparrhenia_hirta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32533,8 +32533,8 @@
   {
     "name": "iNatAg-mini/hyparrhenia_rufa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32554,8 +32554,8 @@
   {
     "name": "iNatAg-mini/hypericum_canadense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32575,8 +32575,8 @@
   {
     "name": "iNatAg-mini/hypericum_canariense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32596,8 +32596,8 @@
   {
     "name": "iNatAg-mini/hypericum_mutilum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32617,8 +32617,8 @@
   {
     "name": "iNatAg-mini/hypericum_mutlium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32638,8 +32638,8 @@
   {
     "name": "iNatAg-mini/hypericum_perforatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32659,8 +32659,8 @@
   {
     "name": "iNatAg-mini/hypericum_prolificum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32680,8 +32680,8 @@
   {
     "name": "iNatAg-mini/hypericum_punctatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32701,8 +32701,8 @@
   {
     "name": "iNatAg-mini/hyperthelia_dissoluta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32722,8 +32722,8 @@
   {
     "name": "iNatAg-mini/hyphaene_compressa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32743,8 +32743,8 @@
   {
     "name": "iNatAg-mini/hyphaene_thebaica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32764,8 +32764,8 @@
   {
     "name": "iNatAg-mini/hypochaeris_glabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32785,8 +32785,8 @@
   {
     "name": "iNatAg-mini/hypochaeris_radicata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32806,8 +32806,8 @@
   {
     "name": "iNatAg-mini/hypoxis_hemerocallidea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32827,8 +32827,8 @@
   {
     "name": "iNatAg-mini/hyssopus_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32848,8 +32848,8 @@
   {
     "name": "iNatAg-mini/ilex_aquifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32869,8 +32869,8 @@
   {
     "name": "iNatAg-mini/ilex_dipyrena",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32890,8 +32890,8 @@
   {
     "name": "iNatAg-mini/ilex_paraguariensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32911,8 +32911,8 @@
   {
     "name": "iNatAg-mini/impatiens_balsamina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32932,8 +32932,8 @@
   {
     "name": "iNatAg-mini/impatiens_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32953,8 +32953,8 @@
   {
     "name": "iNatAg-mini/imperata_brevifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32974,8 +32974,8 @@
   {
     "name": "iNatAg-mini/imperata_cylindrica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -32995,8 +32995,8 @@
   {
     "name": "iNatAg-mini/indigofera_arrecta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33016,8 +33016,8 @@
   {
     "name": "iNatAg-mini/indigofera_hirsuta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33037,8 +33037,8 @@
   {
     "name": "iNatAg-mini/indigofera_oblongifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33058,8 +33058,8 @@
   {
     "name": "iNatAg-mini/indigofera_schimperi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33079,8 +33079,8 @@
   {
     "name": "iNatAg-mini/indigofera_spicata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33100,8 +33100,8 @@
   {
     "name": "iNatAg-mini/indigofera_suffruticosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33121,8 +33121,8 @@
   {
     "name": "iNatAg-mini/indigofera_tinctoria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33142,8 +33142,8 @@
   {
     "name": "iNatAg-mini/inga_edulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33163,8 +33163,8 @@
   {
     "name": "iNatAg-mini/inga_vera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33184,8 +33184,8 @@
   {
     "name": "iNatAg-mini/intsia_bijuga",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33205,8 +33205,8 @@
   {
     "name": "iNatAg-mini/inula_britannica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33226,8 +33226,8 @@
   {
     "name": "iNatAg-mini/inula_helenium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33247,8 +33247,8 @@
   {
     "name": "iNatAg-mini/ipomoea_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33268,8 +33268,8 @@
   {
     "name": "iNatAg-mini/ipomoea_aquatica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33289,8 +33289,8 @@
   {
     "name": "iNatAg-mini/ipomoea_batatas",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33310,8 +33310,8 @@
   {
     "name": "iNatAg-mini/ipomoea_coccinea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33331,8 +33331,8 @@
   {
     "name": "iNatAg-mini/ipomoea_hederifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33352,8 +33352,8 @@
   {
     "name": "iNatAg-mini/ipomoea_lacunosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33373,8 +33373,8 @@
   {
     "name": "iNatAg-mini/ipomoea_quamoclit",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33394,8 +33394,8 @@
   {
     "name": "iNatAg-mini/ipomoea_tricolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33415,8 +33415,8 @@
   {
     "name": "iNatAg-mini/ipomoea_triloba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33436,8 +33436,8 @@
   {
     "name": "iNatAg-mini/ipomoea_turbinata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33457,8 +33457,8 @@
   {
     "name": "iNatAg-mini/iris_germanica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33478,8 +33478,8 @@
   {
     "name": "iNatAg-mini/iris_missouriensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33499,8 +33499,8 @@
   {
     "name": "iNatAg-mini/iris_pseudacorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33520,8 +33520,8 @@
   {
     "name": "iNatAg-mini/iris_pseudoacorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33541,8 +33541,8 @@
   {
     "name": "iNatAg-mini/iris_virginica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33562,8 +33562,8 @@
   {
     "name": "iNatAg-mini/isatis_tinctoria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33583,8 +33583,8 @@
   {
     "name": "iNatAg-mini/ischaemum_ciliare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33604,8 +33604,8 @@
   {
     "name": "iNatAg-mini/ischaemum_muticum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33625,8 +33625,8 @@
   {
     "name": "iNatAg-mini/ischaemum_rugosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33646,8 +33646,8 @@
   {
     "name": "iNatAg-mini/iseilema_vaginiflorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33667,8 +33667,8 @@
   {
     "name": "iNatAg-mini/iva_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33688,8 +33688,8 @@
   {
     "name": "iNatAg-mini/iva_annua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33709,8 +33709,8 @@
   {
     "name": "iNatAg-mini/jacaranda_copaia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33730,8 +33730,8 @@
   {
     "name": "iNatAg-mini/jacaranda_mimosifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33751,8 +33751,8 @@
   {
     "name": "iNatAg-mini/jatropha_curcas",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33772,8 +33772,8 @@
   {
     "name": "iNatAg-mini/jatropha_gossypifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33793,8 +33793,8 @@
   {
     "name": "iNatAg-mini/jatropha_gossypiifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33814,8 +33814,8 @@
   {
     "name": "iNatAg-mini/juglans_hindsii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33835,8 +33835,8 @@
   {
     "name": "iNatAg-mini/juglans_nigra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33856,8 +33856,8 @@
   {
     "name": "iNatAg-mini/juglans_regia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33877,8 +33877,8 @@
   {
     "name": "iNatAg-mini/juncus_bufonius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33898,8 +33898,8 @@
   {
     "name": "iNatAg-mini/juncus_effusus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33919,8 +33919,8 @@
   {
     "name": "iNatAg-mini/juniperus_communis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33940,8 +33940,8 @@
   {
     "name": "iNatAg-mini/juniperus_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33961,8 +33961,8 @@
   {
     "name": "iNatAg-mini/juniperus_pinchotii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -33982,8 +33982,8 @@
   {
     "name": "iNatAg-mini/juniperus_procera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34003,8 +34003,8 @@
   {
     "name": "iNatAg-mini/juniperus_sabina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34024,8 +34024,8 @@
   {
     "name": "iNatAg-mini/justicia_adhatoda",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34045,8 +34045,8 @@
   {
     "name": "iNatAg-mini/kalmia_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34066,8 +34066,8 @@
   {
     "name": "iNatAg-mini/khaya_anthotheca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34087,8 +34087,8 @@
   {
     "name": "iNatAg-mini/khaya_senegalensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34108,8 +34108,8 @@
   {
     "name": "iNatAg-mini/kigelia_pinnata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34129,8 +34129,8 @@
   {
     "name": "iNatAg-mini/kyllinga_gracillima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34150,8 +34150,8 @@
   {
     "name": "iNatAg-mini/kyllinga_odorata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34171,8 +34171,8 @@
   {
     "name": "iNatAg-mini/lablab_purpureus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34192,8 +34192,8 @@
   {
     "name": "iNatAg-mini/lactuca_canadensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34213,8 +34213,8 @@
   {
     "name": "iNatAg-mini/lactuca_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34234,8 +34234,8 @@
   {
     "name": "iNatAg-mini/lactuca_saligna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34255,8 +34255,8 @@
   {
     "name": "iNatAg-mini/lactuca_serriola",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34276,8 +34276,8 @@
   {
     "name": "iNatAg-mini/lactuca_virosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34297,8 +34297,8 @@
   {
     "name": "iNatAg-mini/lagascea_mollis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34318,8 +34318,8 @@
   {
     "name": "iNatAg-mini/lagenaria_siceraria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34339,8 +34339,8 @@
   {
     "name": "iNatAg-mini/lagerstroemia_flos-reginae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34360,8 +34360,8 @@
   {
     "name": "iNatAg-mini/lagerstroemia_lanceolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34381,8 +34381,8 @@
   {
     "name": "iNatAg-mini/lagerstroemia_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34402,8 +34402,8 @@
   {
     "name": "iNatAg-mini/laguncularia_racemosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34423,8 +34423,8 @@
   {
     "name": "iNatAg-mini/lamium_album",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34444,8 +34444,8 @@
   {
     "name": "iNatAg-mini/lamium_amplexicaule",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34465,8 +34465,8 @@
   {
     "name": "iNatAg-mini/lamium_maculatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34486,8 +34486,8 @@
   {
     "name": "iNatAg-mini/lamium_purpureum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34507,8 +34507,8 @@
   {
     "name": "iNatAg-mini/lannea_coromandelica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34528,8 +34528,8 @@
   {
     "name": "iNatAg-mini/lannea_edulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34549,8 +34549,8 @@
   {
     "name": "iNatAg-mini/lansium_domesticum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34570,8 +34570,8 @@
   {
     "name": "iNatAg-mini/lantana_camara",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34591,8 +34591,8 @@
   {
     "name": "iNatAg-mini/lapsana_communis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34612,8 +34612,8 @@
   {
     "name": "iNatAg-mini/larix_decidua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34633,8 +34633,8 @@
   {
     "name": "iNatAg-mini/larrea_divaricata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34654,8 +34654,8 @@
   {
     "name": "iNatAg-mini/lathyrus_angulatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34675,8 +34675,8 @@
   {
     "name": "iNatAg-mini/lathyrus_cicera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34696,8 +34696,8 @@
   {
     "name": "iNatAg-mini/lathyrus_hirsutus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34717,8 +34717,8 @@
   {
     "name": "iNatAg-mini/lathyrus_latifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34738,8 +34738,8 @@
   {
     "name": "iNatAg-mini/lathyrus_ochrus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34759,8 +34759,8 @@
   {
     "name": "iNatAg-mini/lathyrus_odoratus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34780,8 +34780,8 @@
   {
     "name": "iNatAg-mini/lathyrus_palustris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34801,8 +34801,8 @@
   {
     "name": "iNatAg-mini/lathyrus_pratensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34822,8 +34822,8 @@
   {
     "name": "iNatAg-mini/lathyrus_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34843,8 +34843,8 @@
   {
     "name": "iNatAg-mini/lathyrus_sativus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34864,8 +34864,8 @@
   {
     "name": "iNatAg-mini/lathyrus_tingitanus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34885,8 +34885,8 @@
   {
     "name": "iNatAg-mini/lathyrus_tuberosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34906,8 +34906,8 @@
   {
     "name": "iNatAg-mini/laurus_nobilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34927,8 +34927,8 @@
   {
     "name": "iNatAg-mini/lavandula_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34948,8 +34948,8 @@
   {
     "name": "iNatAg-mini/lavandula_dentata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34969,8 +34969,8 @@
   {
     "name": "iNatAg-mini/lavandula_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -34990,8 +34990,8 @@
   {
     "name": "iNatAg-mini/lawsonia_inermis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35011,8 +35011,8 @@
   {
     "name": "iNatAg-mini/ledum_groenlandicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35032,8 +35032,8 @@
   {
     "name": "iNatAg-mini/leersia_hexandra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35053,8 +35053,8 @@
   {
     "name": "iNatAg-mini/leersia_lenticularis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35074,8 +35074,8 @@
   {
     "name": "iNatAg-mini/lemna_aequinoctialis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35095,8 +35095,8 @@
   {
     "name": "iNatAg-mini/lemna_gibba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35116,8 +35116,8 @@
   {
     "name": "iNatAg-mini/lemna_minor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35137,8 +35137,8 @@
   {
     "name": "iNatAg-mini/lemna_trisulca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35158,8 +35158,8 @@
   {
     "name": "iNatAg-mini/lens_culinaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35179,8 +35179,8 @@
   {
     "name": "iNatAg-mini/leontodon_autumnale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35200,8 +35200,8 @@
   {
     "name": "iNatAg-mini/leontodon_autumnalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35221,8 +35221,8 @@
   {
     "name": "iNatAg-mini/leontodon_hirtus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35242,8 +35242,8 @@
   {
     "name": "iNatAg-mini/leontodon_saxatilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35263,8 +35263,8 @@
   {
     "name": "iNatAg-mini/leontopodium_alpinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35284,8 +35284,8 @@
   {
     "name": "iNatAg-mini/leonurus_cardiaca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35305,8 +35305,8 @@
   {
     "name": "iNatAg-mini/leonurus_marrubiastrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35326,8 +35326,8 @@
   {
     "name": "iNatAg-mini/leonurus_sibericus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35347,8 +35347,8 @@
   {
     "name": "iNatAg-mini/leonurus_sibiricus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35368,8 +35368,8 @@
   {
     "name": "iNatAg-mini/lepidium_austrinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35389,8 +35389,8 @@
   {
     "name": "iNatAg-mini/lepidium_chalepense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35410,8 +35410,8 @@
   {
     "name": "iNatAg-mini/lepidium_didymum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35431,8 +35431,8 @@
   {
     "name": "iNatAg-mini/lepidium_draba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35452,8 +35452,8 @@
   {
     "name": "iNatAg-mini/lepidium_lasiocarpum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35473,8 +35473,8 @@
   {
     "name": "iNatAg-mini/lepidium_latifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35494,8 +35494,8 @@
   {
     "name": "iNatAg-mini/lepidium_perfoliatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35515,8 +35515,8 @@
   {
     "name": "iNatAg-mini/lepidium_ruderale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35536,8 +35536,8 @@
   {
     "name": "iNatAg-mini/lepidium_sativum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35557,8 +35557,8 @@
   {
     "name": "iNatAg-mini/lepidium_virginicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35578,8 +35578,8 @@
   {
     "name": "iNatAg-mini/leptochloa_chinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35599,8 +35599,8 @@
   {
     "name": "iNatAg-mini/leptochloa_fusca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35620,8 +35620,8 @@
   {
     "name": "iNatAg-mini/leptochloa_nealleyi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35641,8 +35641,8 @@
   {
     "name": "iNatAg-mini/lespedeza_cuneata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35662,8 +35662,8 @@
   {
     "name": "iNatAg-mini/lespedeza_striata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35683,8 +35683,8 @@
   {
     "name": "iNatAg-mini/lesquerella_fendleri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35704,8 +35704,8 @@
   {
     "name": "iNatAg-mini/leucaena_diversifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35725,8 +35725,8 @@
   {
     "name": "iNatAg-mini/leucaena_leucocephala",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35746,8 +35746,8 @@
   {
     "name": "iNatAg-mini/leucanthemum_vulgare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35767,8 +35767,8 @@
   {
     "name": "iNatAg-mini/leucojum_aestivum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35788,8 +35788,8 @@
   {
     "name": "iNatAg-mini/levisticum_officinale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35809,8 +35809,8 @@
   {
     "name": "iNatAg-mini/liatris_mucronata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35830,8 +35830,8 @@
   {
     "name": "iNatAg-mini/licuala_ramsayi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35851,8 +35851,8 @@
   {
     "name": "iNatAg-mini/ligustrum_ovalifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35872,8 +35872,8 @@
   {
     "name": "iNatAg-mini/ligustrum_vulgare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35893,8 +35893,8 @@
   {
     "name": "iNatAg-mini/lilium_canadense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35914,8 +35914,8 @@
   {
     "name": "iNatAg-mini/lilium_candidum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35935,8 +35935,8 @@
   {
     "name": "iNatAg-mini/limnanthes_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35956,8 +35956,8 @@
   {
     "name": "iNatAg-mini/limnophila_sessiliflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35977,8 +35977,8 @@
   {
     "name": "iNatAg-mini/linaria_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -35998,8 +35998,8 @@
   {
     "name": "iNatAg-mini/lindernia_grandiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36019,8 +36019,8 @@
   {
     "name": "iNatAg-mini/linum_usitatissimum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36040,8 +36040,8 @@
   {
     "name": "iNatAg-mini/lippia_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36061,8 +36061,8 @@
   {
     "name": "iNatAg-mini/liquidambar_styraciflua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36082,8 +36082,8 @@
   {
     "name": "iNatAg-mini/liriodendron_tulipifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36103,8 +36103,8 @@
   {
     "name": "iNatAg-mini/litchi_chinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36124,8 +36124,8 @@
   {
     "name": "iNatAg-mini/lithospermum_arvense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36145,8 +36145,8 @@
   {
     "name": "iNatAg-mini/lithospermum_officinale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36166,8 +36166,8 @@
   {
     "name": "iNatAg-mini/livistona_australis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36187,8 +36187,8 @@
   {
     "name": "iNatAg-mini/lobelia_inflata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36208,8 +36208,8 @@
   {
     "name": "iNatAg-mini/lobelia_siphilitica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36229,8 +36229,8 @@
   {
     "name": "iNatAg-mini/lolium_multiflorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36250,8 +36250,8 @@
   {
     "name": "iNatAg-mini/lolium_perenne",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36271,8 +36271,8 @@
   {
     "name": "iNatAg-mini/lolium_rigidum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36292,8 +36292,8 @@
   {
     "name": "iNatAg-mini/lolium_temulentum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36313,8 +36313,8 @@
   {
     "name": "iNatAg-mini/lonchocarpus_laxiflorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36334,8 +36334,8 @@
   {
     "name": "iNatAg-mini/lonicera_caerulea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36355,8 +36355,8 @@
   {
     "name": "iNatAg-mini/lonicera_caprifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36376,8 +36376,8 @@
   {
     "name": "iNatAg-mini/lonicera_periclymenum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36397,8 +36397,8 @@
   {
     "name": "iNatAg-mini/lonicera_sempervirens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36418,8 +36418,8 @@
   {
     "name": "iNatAg-mini/lonicera_tartarica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36439,8 +36439,8 @@
   {
     "name": "iNatAg-mini/lonicera_tatarica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36460,8 +36460,8 @@
   {
     "name": "iNatAg-mini/lonicera_xylosteum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36481,8 +36481,8 @@
   {
     "name": "iNatAg-mini/lophostemon_suaveolens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36502,8 +36502,8 @@
   {
     "name": "iNatAg-mini/lotus_corniculatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36523,8 +36523,8 @@
   {
     "name": "iNatAg-mini/lotus_creticus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36544,8 +36544,8 @@
   {
     "name": "iNatAg-mini/lotus_edulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36565,8 +36565,8 @@
   {
     "name": "iNatAg-mini/lotus_halophilus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36586,8 +36586,8 @@
   {
     "name": "iNatAg-mini/lotus_parviflorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36607,8 +36607,8 @@
   {
     "name": "iNatAg-mini/lotus_tenuis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36628,8 +36628,8 @@
   {
     "name": "iNatAg-mini/lotus_uliginosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36649,8 +36649,8 @@
   {
     "name": "iNatAg-mini/loudetia_simplex",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36670,8 +36670,8 @@
   {
     "name": "iNatAg-mini/ludwigia_adscendens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36691,8 +36691,8 @@
   {
     "name": "iNatAg-mini/ludwigia_alternifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36712,8 +36712,8 @@
   {
     "name": "iNatAg-mini/luffa_acutangula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36733,8 +36733,8 @@
   {
     "name": "iNatAg-mini/luffa_cylindrica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36754,8 +36754,8 @@
   {
     "name": "iNatAg-mini/lumnitzera_littorea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36775,8 +36775,8 @@
   {
     "name": "iNatAg-mini/lumnitzera_racemosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36796,8 +36796,8 @@
   {
     "name": "iNatAg-mini/lunaria_annua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36817,8 +36817,8 @@
   {
     "name": "iNatAg-mini/lupinus_albus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36838,8 +36838,8 @@
   {
     "name": "iNatAg-mini/lupinus_angustifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36859,8 +36859,8 @@
   {
     "name": "iNatAg-mini/lupinus_arboreus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36880,8 +36880,8 @@
   {
     "name": "iNatAg-mini/lupinus_cosentinii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36901,8 +36901,8 @@
   {
     "name": "iNatAg-mini/lupinus_luteus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36922,8 +36922,8 @@
   {
     "name": "iNatAg-mini/lupinus_mutabilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36943,8 +36943,8 @@
   {
     "name": "iNatAg-mini/lupinus_pilosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36964,8 +36964,8 @@
   {
     "name": "iNatAg-mini/lychnis_chalcedonica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -36985,8 +36985,8 @@
   {
     "name": "iNatAg-mini/lychnis_flos-cuculi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37006,8 +37006,8 @@
   {
     "name": "iNatAg-mini/lychnis_viscaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37027,8 +37027,8 @@
   {
     "name": "iNatAg-mini/lycium_barbarum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37048,8 +37048,8 @@
   {
     "name": "iNatAg-mini/lycium_berlandieri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37069,8 +37069,8 @@
   {
     "name": "iNatAg-mini/lycium_chinense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37090,8 +37090,8 @@
   {
     "name": "iNatAg-mini/lycium_ferocissimum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37111,8 +37111,8 @@
   {
     "name": "iNatAg-mini/lycium_halimifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37132,8 +37132,8 @@
   {
     "name": "iNatAg-mini/lycopersicon_esculentum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37153,8 +37153,8 @@
   {
     "name": "iNatAg-mini/lycopodium_clavatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37174,8 +37174,8 @@
   {
     "name": "iNatAg-mini/lycopus_europaeus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37195,8 +37195,8 @@
   {
     "name": "iNatAg-mini/lysimachia_ciliata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37216,8 +37216,8 @@
   {
     "name": "iNatAg-mini/lysimachia_nummularia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37237,8 +37237,8 @@
   {
     "name": "iNatAg-mini/lysimachia_punctata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37258,8 +37258,8 @@
   {
     "name": "iNatAg-mini/lysimachia_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37279,8 +37279,8 @@
   {
     "name": "iNatAg-mini/lythrum_hyssopifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37300,8 +37300,8 @@
   {
     "name": "iNatAg-mini/lythrum_salicaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37321,8 +37321,8 @@
   {
     "name": "iNatAg-mini/lythrum_virgatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37342,8 +37342,8 @@
   {
     "name": "iNatAg-mini/macadamia_integrifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37363,8 +37363,8 @@
   {
     "name": "iNatAg-mini/macadamia_tetraphylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37384,8 +37384,8 @@
   {
     "name": "iNatAg-mini/macaranga_tanarius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37405,8 +37405,8 @@
   {
     "name": "iNatAg-mini/macroptilium_atropurpureum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37426,8 +37426,8 @@
   {
     "name": "iNatAg-mini/macroptilium_erythroloma",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37447,8 +37447,8 @@
   {
     "name": "iNatAg-mini/macroptilium_gracile",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37468,8 +37468,8 @@
   {
     "name": "iNatAg-mini/macroptilium_lathyroides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37489,8 +37489,8 @@
   {
     "name": "iNatAg-mini/macroptilium_longepedunculatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37510,8 +37510,8 @@
   {
     "name": "iNatAg-mini/macrotyloma_axillare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37531,8 +37531,8 @@
   {
     "name": "iNatAg-mini/maesopsis_eminii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37552,8 +37552,8 @@
   {
     "name": "iNatAg-mini/maianthemum_canadense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37573,8 +37573,8 @@
   {
     "name": "iNatAg-mini/majorana_hortensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37594,8 +37594,8 @@
   {
     "name": "iNatAg-mini/malachra_alceifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37615,8 +37615,8 @@
   {
     "name": "iNatAg-mini/mallotus_philippensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37636,8 +37636,8 @@
   {
     "name": "iNatAg-mini/malpighia_glabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37657,8 +37657,8 @@
   {
     "name": "iNatAg-mini/malus_domestica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37678,8 +37678,8 @@
   {
     "name": "iNatAg-mini/malus_sylvestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37699,8 +37699,8 @@
   {
     "name": "iNatAg-mini/malva_alcea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37720,8 +37720,8 @@
   {
     "name": "iNatAg-mini/malva_moschata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37741,8 +37741,8 @@
   {
     "name": "iNatAg-mini/malva_nicaeensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37762,8 +37762,8 @@
   {
     "name": "iNatAg-mini/malva_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37783,8 +37783,8 @@
   {
     "name": "iNatAg-mini/malva_pusilla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37804,8 +37804,8 @@
   {
     "name": "iNatAg-mini/malva_rotundifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37825,8 +37825,8 @@
   {
     "name": "iNatAg-mini/malva_silvestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37846,8 +37846,8 @@
   {
     "name": "iNatAg-mini/malva_sylvestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37867,8 +37867,8 @@
   {
     "name": "iNatAg-mini/mammea_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37888,8 +37888,8 @@
   {
     "name": "iNatAg-mini/mangifera_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37909,8 +37909,8 @@
   {
     "name": "iNatAg-mini/manihot_esculenta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37930,8 +37930,8 @@
   {
     "name": "iNatAg-mini/manilkara_zapota",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37951,8 +37951,8 @@
   {
     "name": "iNatAg-mini/maranta_arundinacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37972,8 +37972,8 @@
   {
     "name": "iNatAg-mini/markhamia_lutea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -37993,8 +37993,8 @@
   {
     "name": "iNatAg-mini/marrubium_vulgare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38014,8 +38014,8 @@
   {
     "name": "iNatAg-mini/marsilea_quadrifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38035,8 +38035,8 @@
   {
     "name": "iNatAg-mini/matricaria_chamomila",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38056,8 +38056,8 @@
   {
     "name": "iNatAg-mini/matricaria_chamomilla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38077,8 +38077,8 @@
   {
     "name": "iNatAg-mini/matricaria_discoidea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38098,8 +38098,8 @@
   {
     "name": "iNatAg-mini/matricaria_perforata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38119,8 +38119,8 @@
   {
     "name": "iNatAg-mini/matricaria_recutita",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38140,8 +38140,8 @@
   {
     "name": "iNatAg-mini/mauritia_flexuosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38161,8 +38161,8 @@
   {
     "name": "iNatAg-mini/mayaca_fluviatilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38182,8 +38182,8 @@
   {
     "name": "iNatAg-mini/medicago_arabica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38203,8 +38203,8 @@
   {
     "name": "iNatAg-mini/medicago_falcata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38224,8 +38224,8 @@
   {
     "name": "iNatAg-mini/medicago_intertexta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38245,8 +38245,8 @@
   {
     "name": "iNatAg-mini/medicago_laciniata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38266,8 +38266,8 @@
   {
     "name": "iNatAg-mini/medicago_littoralis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38287,8 +38287,8 @@
   {
     "name": "iNatAg-mini/medicago_lupulina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38308,8 +38308,8 @@
   {
     "name": "iNatAg-mini/medicago_marina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38329,8 +38329,8 @@
   {
     "name": "iNatAg-mini/medicago_minima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38350,8 +38350,8 @@
   {
     "name": "iNatAg-mini/medicago_orbicularis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38371,8 +38371,8 @@
   {
     "name": "iNatAg-mini/medicago_polymorpha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38392,8 +38392,8 @@
   {
     "name": "iNatAg-mini/medicago_rigidula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38413,8 +38413,8 @@
   {
     "name": "iNatAg-mini/medicago_rugosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38434,8 +38434,8 @@
   {
     "name": "iNatAg-mini/medicago_sativa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38455,8 +38455,8 @@
   {
     "name": "iNatAg-mini/medicago_scutellata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38476,8 +38476,8 @@
   {
     "name": "iNatAg-mini/medicago_tornata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38497,8 +38497,8 @@
   {
     "name": "iNatAg-mini/medicago_truncatula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38518,8 +38518,8 @@
   {
     "name": "iNatAg-mini/medicago_turbinata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38539,8 +38539,8 @@
   {
     "name": "iNatAg-mini/melaleuca_bracteata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38560,8 +38560,8 @@
   {
     "name": "iNatAg-mini/melaleuca_cajuputi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38581,8 +38581,8 @@
   {
     "name": "iNatAg-mini/melaleuca_dealbata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38602,8 +38602,8 @@
   {
     "name": "iNatAg-mini/melaleuca_lanceolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38623,8 +38623,8 @@
   {
     "name": "iNatAg-mini/melaleuca_leucadendron",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38644,8 +38644,8 @@
   {
     "name": "iNatAg-mini/melaleuca_nervosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38665,8 +38665,8 @@
   {
     "name": "iNatAg-mini/melaleuca_quinquenervia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38686,8 +38686,8 @@
   {
     "name": "iNatAg-mini/melaleuca_viridiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38707,8 +38707,8 @@
   {
     "name": "iNatAg-mini/melampyrum_lineare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38728,8 +38728,8 @@
   {
     "name": "iNatAg-mini/melastoma_malabathricum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38749,8 +38749,8 @@
   {
     "name": "iNatAg-mini/melastoma_melabathricum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38770,8 +38770,8 @@
   {
     "name": "iNatAg-mini/melia_azedarach",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38791,8 +38791,8 @@
   {
     "name": "iNatAg-mini/melica_decumbens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38812,8 +38812,8 @@
   {
     "name": "iNatAg-mini/melicoccus_bijugatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38833,8 +38833,8 @@
   {
     "name": "iNatAg-mini/melilotus_albus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38854,8 +38854,8 @@
   {
     "name": "iNatAg-mini/melilotus_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38875,8 +38875,8 @@
   {
     "name": "iNatAg-mini/melilotus_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38896,8 +38896,8 @@
   {
     "name": "iNatAg-mini/melilotus_suaveolens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38917,8 +38917,8 @@
   {
     "name": "iNatAg-mini/melinis_minutiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38938,8 +38938,8 @@
   {
     "name": "iNatAg-mini/melissa_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38959,8 +38959,8 @@
   {
     "name": "iNatAg-mini/melochia_corchorifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -38980,8 +38980,8 @@
   {
     "name": "iNatAg-mini/melothria_pendula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39001,8 +39001,8 @@
   {
     "name": "iNatAg-mini/mentha_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39022,8 +39022,8 @@
   {
     "name": "iNatAg-mini/mentha_longifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39043,8 +39043,8 @@
   {
     "name": "iNatAg-mini/mentha_piperita",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39064,8 +39064,8 @@
   {
     "name": "iNatAg-mini/mentha_pulegium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39085,8 +39085,8 @@
   {
     "name": "iNatAg-mini/mentha_rotundifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39106,8 +39106,8 @@
   {
     "name": "iNatAg-mini/mentha_spicata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39127,8 +39127,8 @@
   {
     "name": "iNatAg-mini/menyanthes_trifoliata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39148,8 +39148,8 @@
   {
     "name": "iNatAg-mini/mercurialis_annua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39169,8 +39169,8 @@
   {
     "name": "iNatAg-mini/mesembryanthemum_cristallinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39190,8 +39190,8 @@
   {
     "name": "iNatAg-mini/mesembryanthemum_noctiflorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39211,8 +39211,8 @@
   {
     "name": "iNatAg-mini/mespilus_germanica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39232,8 +39232,8 @@
   {
     "name": "iNatAg-mini/mesua_ferrea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39253,8 +39253,8 @@
   {
     "name": "iNatAg-mini/metroxylon_sagu",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39274,8 +39274,8 @@
   {
     "name": "iNatAg-mini/michelia_champaca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39295,8 +39295,8 @@
   {
     "name": "iNatAg-mini/microstegium_ciliatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39316,8 +39316,8 @@
   {
     "name": "iNatAg-mini/miliusa_velutina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39337,8 +39337,8 @@
   {
     "name": "iNatAg-mini/mimosa_casta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39358,8 +39358,8 @@
   {
     "name": "iNatAg-mini/mimosa_dutrae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39379,8 +39379,8 @@
   {
     "name": "iNatAg-mini/mimosa_pigra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39400,8 +39400,8 @@
   {
     "name": "iNatAg-mini/mimosa_pudica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39421,8 +39421,8 @@
   {
     "name": "iNatAg-mini/mirabilis_jalapa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39442,8 +39442,8 @@
   {
     "name": "iNatAg-mini/molinia_caerulea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39463,8 +39463,8 @@
   {
     "name": "iNatAg-mini/mollugo_verticillata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39484,8 +39484,8 @@
   {
     "name": "iNatAg-mini/momordica_charantia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39505,8 +39505,8 @@
   {
     "name": "iNatAg-mini/momordica_cochinchinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39526,8 +39526,8 @@
   {
     "name": "iNatAg-mini/monarda_fistulosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39547,8 +39547,8 @@
   {
     "name": "iNatAg-mini/monarda_punctata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39568,8 +39568,8 @@
   {
     "name": "iNatAg-mini/monochoria_hastata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39589,8 +39589,8 @@
   {
     "name": "iNatAg-mini/monochoria_vaginalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39610,8 +39610,8 @@
   {
     "name": "iNatAg-mini/monocymbium_ceresiiforme",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39631,8 +39631,8 @@
   {
     "name": "iNatAg-mini/monstera_deliciosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39652,8 +39652,8 @@
   {
     "name": "iNatAg-mini/montanoa_hibiscifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39673,8 +39673,8 @@
   {
     "name": "iNatAg-mini/morinda_citrifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39694,8 +39694,8 @@
   {
     "name": "iNatAg-mini/moringa_oleifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39715,8 +39715,8 @@
   {
     "name": "iNatAg-mini/morus_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39736,8 +39736,8 @@
   {
     "name": "iNatAg-mini/morus_nigra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39757,8 +39757,8 @@
   {
     "name": "iNatAg-mini/morus_rubra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39778,8 +39778,8 @@
   {
     "name": "iNatAg-mini/mucuna_pruriens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39799,8 +39799,8 @@
   {
     "name": "iNatAg-mini/muntingia_calabura",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39820,8 +39820,8 @@
   {
     "name": "iNatAg-mini/murraya_koenigii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39841,8 +39841,8 @@
   {
     "name": "iNatAg-mini/musa_acuminata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39862,8 +39862,8 @@
   {
     "name": "iNatAg-mini/musa_acuminata_×_balbisiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39883,8 +39883,8 @@
   {
     "name": "iNatAg-mini/musa_balbisiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39904,8 +39904,8 @@
   {
     "name": "iNatAg-mini/musa_sapientium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39925,8 +39925,8 @@
   {
     "name": "iNatAg-mini/musanga_cecropioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39946,8 +39946,8 @@
   {
     "name": "iNatAg-mini/muscari_comosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39967,8 +39967,8 @@
   {
     "name": "iNatAg-mini/myosotis_alpestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -39988,8 +39988,8 @@
   {
     "name": "iNatAg-mini/myosurus_minimus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40009,8 +40009,8 @@
   {
     "name": "iNatAg-mini/myrica_cerifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40030,8 +40030,8 @@
   {
     "name": "iNatAg-mini/myriophyllum_heterophyllum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40051,8 +40051,8 @@
   {
     "name": "iNatAg-mini/myriophyllum_implicatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40072,8 +40072,8 @@
   {
     "name": "iNatAg-mini/myriophyllum_sibiricum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40093,8 +40093,8 @@
   {
     "name": "iNatAg-mini/myriophyllum_spicatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40114,8 +40114,8 @@
   {
     "name": "iNatAg-mini/myriophyllum_verticillatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40135,8 +40135,8 @@
   {
     "name": "iNatAg-mini/myristica_fragrans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40156,8 +40156,8 @@
   {
     "name": "iNatAg-mini/myroxylon_balsamum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40177,8 +40177,8 @@
   {
     "name": "iNatAg-mini/myrsine_africana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40198,8 +40198,8 @@
   {
     "name": "iNatAg-mini/myrtus_communis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40219,8 +40219,8 @@
   {
     "name": "iNatAg-mini/nardus_stricta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40240,8 +40240,8 @@
   {
     "name": "iNatAg-mini/nasturtium_officinale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40261,8 +40261,8 @@
   {
     "name": "iNatAg-mini/nauclea_orientalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40282,8 +40282,8 @@
   {
     "name": "iNatAg-mini/nelumbo_nucifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40303,8 +40303,8 @@
   {
     "name": "iNatAg-mini/neofabricia_myrtifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40324,8 +40324,8 @@
   {
     "name": "iNatAg-mini/neoglaziovia_variegata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40345,8 +40345,8 @@
   {
     "name": "iNatAg-mini/neonotonia_wightii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40366,8 +40366,8 @@
   {
     "name": "iNatAg-mini/nepeta_cataria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40387,8 +40387,8 @@
   {
     "name": "iNatAg-mini/nephelium_lappaceum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40408,8 +40408,8 @@
   {
     "name": "iNatAg-mini/nephelium_mutabile",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40429,8 +40429,8 @@
   {
     "name": "iNatAg-mini/nerium_oleander",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40450,8 +40450,8 @@
   {
     "name": "iNatAg-mini/nicotiana_quadrivalvis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40471,8 +40471,8 @@
   {
     "name": "iNatAg-mini/nicotiana_rustica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40492,8 +40492,8 @@
   {
     "name": "iNatAg-mini/nicotiana_trigonophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40513,8 +40513,8 @@
   {
     "name": "iNatAg-mini/nigella_sativa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40534,8 +40534,8 @@
   {
     "name": "iNatAg-mini/nothofagus_cunninghamii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40555,8 +40555,8 @@
   {
     "name": "iNatAg-mini/nothofagus_moorei",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40576,8 +40576,8 @@
   {
     "name": "iNatAg-mini/nothoscordum_borbonicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40597,8 +40597,8 @@
   {
     "name": "iNatAg-mini/nuphar_advena",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40618,8 +40618,8 @@
   {
     "name": "iNatAg-mini/nuphar_lutea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40639,8 +40639,8 @@
   {
     "name": "iNatAg-mini/nymphaea_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40660,8 +40660,8 @@
   {
     "name": "iNatAg-mini/nypa_fruticans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40681,8 +40681,8 @@
   {
     "name": "iNatAg-mini/ochroma_pyramidale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40702,8 +40702,8 @@
   {
     "name": "iNatAg-mini/ocimum_americanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40723,8 +40723,8 @@
   {
     "name": "iNatAg-mini/ocimum_basilicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40744,8 +40744,8 @@
   {
     "name": "iNatAg-mini/ocimum_tenuiflorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40765,8 +40765,8 @@
   {
     "name": "iNatAg-mini/octomeles_sumatrana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40786,8 +40786,8 @@
   {
     "name": "iNatAg-mini/oenanthe_javanica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40807,8 +40807,8 @@
   {
     "name": "iNatAg-mini/oenothera_albicaulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40828,8 +40828,8 @@
   {
     "name": "iNatAg-mini/oenothera_biennis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40849,8 +40849,8 @@
   {
     "name": "iNatAg-mini/oenothera_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40870,8 +40870,8 @@
   {
     "name": "iNatAg-mini/oenothera_perennis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40891,8 +40891,8 @@
   {
     "name": "iNatAg-mini/oldenlandia_corymbosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40912,8 +40912,8 @@
   {
     "name": "iNatAg-mini/olea_africana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40933,8 +40933,8 @@
   {
     "name": "iNatAg-mini/olea_capensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40954,8 +40954,8 @@
   {
     "name": "iNatAg-mini/olea_europaea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40975,8 +40975,8 @@
   {
     "name": "iNatAg-mini/olea_europea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -40996,8 +40996,8 @@
   {
     "name": "iNatAg-mini/oncosperma_tigillarium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41017,8 +41017,8 @@
   {
     "name": "iNatAg-mini/onobrychis_viciifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41038,8 +41038,8 @@
   {
     "name": "iNatAg-mini/ononis_alopecuroides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41059,8 +41059,8 @@
   {
     "name": "iNatAg-mini/ononis_spinosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41080,8 +41080,8 @@
   {
     "name": "iNatAg-mini/onopordum_acanthium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41101,8 +41101,8 @@
   {
     "name": "iNatAg-mini/onopordum_illyricum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41122,8 +41122,8 @@
   {
     "name": "iNatAg-mini/onosmodium_discolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41143,8 +41143,8 @@
   {
     "name": "iNatAg-mini/opuntia_ficus-indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41164,8 +41164,8 @@
   {
     "name": "iNatAg-mini/opuntia_leptocaulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41185,8 +41185,8 @@
   {
     "name": "iNatAg-mini/opuntia_polyacantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41206,8 +41206,8 @@
   {
     "name": "iNatAg-mini/opuntia_polycantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41227,8 +41227,8 @@
   {
     "name": "iNatAg-mini/origanum_majorana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41248,8 +41248,8 @@
   {
     "name": "iNatAg-mini/origanum_onites",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41269,8 +41269,8 @@
   {
     "name": "iNatAg-mini/origanum_vulgare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41290,8 +41290,8 @@
   {
     "name": "iNatAg-mini/ornithogalum_nutans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41311,8 +41311,8 @@
   {
     "name": "iNatAg-mini/ornithogalum_umbellatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41332,8 +41332,8 @@
   {
     "name": "iNatAg-mini/ornithopus_compressus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41353,8 +41353,8 @@
   {
     "name": "iNatAg-mini/ornithopus_sativus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41374,8 +41374,8 @@
   {
     "name": "iNatAg-mini/orobanche_flava",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41395,8 +41395,8 @@
   {
     "name": "iNatAg-mini/orobanche_ludoviciana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41416,8 +41416,8 @@
   {
     "name": "iNatAg-mini/orobanche_minor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41437,8 +41437,8 @@
   {
     "name": "iNatAg-mini/orobanche_ramosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41458,8 +41458,8 @@
   {
     "name": "iNatAg-mini/orontium_aquaticum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41479,8 +41479,8 @@
   {
     "name": "iNatAg-mini/orthosiphon_aristatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41500,8 +41500,8 @@
   {
     "name": "iNatAg-mini/oryza_sativa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41521,8 +41521,8 @@
   {
     "name": "iNatAg-mini/oryzopsis_holciformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41542,8 +41542,8 @@
   {
     "name": "iNatAg-mini/oryzopsis_miliacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41563,8 +41563,8 @@
   {
     "name": "iNatAg-mini/osmorhiza_berteroi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41584,8 +41584,8 @@
   {
     "name": "iNatAg-mini/osmunda_regalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41605,8 +41605,8 @@
   {
     "name": "iNatAg-mini/ottochloa_nodosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41626,8 +41626,8 @@
   {
     "name": "iNatAg-mini/oxalis_acetosella",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41647,8 +41647,8 @@
   {
     "name": "iNatAg-mini/oxalis_corniculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41668,8 +41668,8 @@
   {
     "name": "iNatAg-mini/oxalis_pes-caprae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41689,8 +41689,8 @@
   {
     "name": "iNatAg-mini/oxalis_pescaprae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41710,8 +41710,8 @@
   {
     "name": "iNatAg-mini/oxalis_stricta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41731,8 +41731,8 @@
   {
     "name": "iNatAg-mini/oxalis_tuberosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41752,8 +41752,8 @@
   {
     "name": "iNatAg-mini/oxytropis_lambertii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41773,8 +41773,8 @@
   {
     "name": "iNatAg-mini/pachyrhizus_erosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41794,8 +41794,8 @@
   {
     "name": "iNatAg-mini/paederia_cruddasiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41815,8 +41815,8 @@
   {
     "name": "iNatAg-mini/paederia_foetida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41836,8 +41836,8 @@
   {
     "name": "iNatAg-mini/paeonia_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41857,8 +41857,8 @@
   {
     "name": "iNatAg-mini/panax_ginseng",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41878,8 +41878,8 @@
   {
     "name": "iNatAg-mini/panax_quinquefolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41899,8 +41899,8 @@
   {
     "name": "iNatAg-mini/pangium_edule",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41920,8 +41920,8 @@
   {
     "name": "iNatAg-mini/panicum_antidotale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41941,8 +41941,8 @@
   {
     "name": "iNatAg-mini/panicum_capillare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41962,8 +41962,8 @@
   {
     "name": "iNatAg-mini/panicum_coloratum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -41983,8 +41983,8 @@
   {
     "name": "iNatAg-mini/panicum_ecklonii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42004,8 +42004,8 @@
   {
     "name": "iNatAg-mini/panicum_gattingeri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42025,8 +42025,8 @@
   {
     "name": "iNatAg-mini/panicum_maximum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42046,8 +42046,8 @@
   {
     "name": "iNatAg-mini/panicum_miliaceum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42067,8 +42067,8 @@
   {
     "name": "iNatAg-mini/panicum_natalense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42088,8 +42088,8 @@
   {
     "name": "iNatAg-mini/panicum_obtusum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42109,8 +42109,8 @@
   {
     "name": "iNatAg-mini/panicum_pilosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42130,8 +42130,8 @@
   {
     "name": "iNatAg-mini/panicum_racemosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42151,8 +42151,8 @@
   {
     "name": "iNatAg-mini/panicum_repens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42172,8 +42172,8 @@
   {
     "name": "iNatAg-mini/panicum_sphaerocarpon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42193,8 +42193,8 @@
   {
     "name": "iNatAg-mini/panicum_trichocladum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42214,8 +42214,8 @@
   {
     "name": "iNatAg-mini/panicum_turgidum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42235,8 +42235,8 @@
   {
     "name": "iNatAg-mini/panicum_virgatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42256,8 +42256,8 @@
   {
     "name": "iNatAg-mini/papaver_argemone",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42277,8 +42277,8 @@
   {
     "name": "iNatAg-mini/papaver_bracteatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42298,8 +42298,8 @@
   {
     "name": "iNatAg-mini/papaver_dubium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42319,8 +42319,8 @@
   {
     "name": "iNatAg-mini/papaver_rhoeas",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42340,8 +42340,8 @@
   {
     "name": "iNatAg-mini/papaver_somniferum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42361,8 +42361,8 @@
   {
     "name": "iNatAg-mini/parietaria_floridana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42382,8 +42382,8 @@
   {
     "name": "iNatAg-mini/parietaria_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42403,8 +42403,8 @@
   {
     "name": "iNatAg-mini/parinari_curatellifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42424,8 +42424,8 @@
   {
     "name": "iNatAg-mini/parkia_biglobosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42445,8 +42445,8 @@
   {
     "name": "iNatAg-mini/parkia_speciosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42466,8 +42466,8 @@
   {
     "name": "iNatAg-mini/parkinsonia_aculeata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42487,8 +42487,8 @@
   {
     "name": "iNatAg-mini/parnassia_palustris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42508,8 +42508,8 @@
   {
     "name": "iNatAg-mini/parsonsia_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42529,8 +42529,8 @@
   {
     "name": "iNatAg-mini/parthenium_argentatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42550,8 +42550,8 @@
   {
     "name": "iNatAg-mini/parthenium_hysterophorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42571,8 +42571,8 @@
   {
     "name": "iNatAg-mini/paspalum_conjugatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42592,8 +42592,8 @@
   {
     "name": "iNatAg-mini/paspalum_dilatatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42613,8 +42613,8 @@
   {
     "name": "iNatAg-mini/paspalum_distichum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42634,8 +42634,8 @@
   {
     "name": "iNatAg-mini/paspalum_nicorae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42655,8 +42655,8 @@
   {
     "name": "iNatAg-mini/paspalum_notatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42676,8 +42676,8 @@
   {
     "name": "iNatAg-mini/paspalum_plicatulum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42697,8 +42697,8 @@
   {
     "name": "iNatAg-mini/paspalum_scrobiculatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42718,8 +42718,8 @@
   {
     "name": "iNatAg-mini/paspalum_separatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42739,8 +42739,8 @@
   {
     "name": "iNatAg-mini/paspalum_urvillei",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42760,8 +42760,8 @@
   {
     "name": "iNatAg-mini/paspalum_vaginatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42781,8 +42781,8 @@
   {
     "name": "iNatAg-mini/passiflora_bicornis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42802,8 +42802,8 @@
   {
     "name": "iNatAg-mini/passiflora_edulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42823,8 +42823,8 @@
   {
     "name": "iNatAg-mini/passiflora_foetida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42844,8 +42844,8 @@
   {
     "name": "iNatAg-mini/passiflora_incarnata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42865,8 +42865,8 @@
   {
     "name": "iNatAg-mini/passiflora_laurifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42886,8 +42886,8 @@
   {
     "name": "iNatAg-mini/passiflora_ligularis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42907,8 +42907,8 @@
   {
     "name": "iNatAg-mini/passiflora_lutea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42928,8 +42928,8 @@
   {
     "name": "iNatAg-mini/passiflora_mollissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42949,8 +42949,8 @@
   {
     "name": "iNatAg-mini/passiflora_quadrangularis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42970,8 +42970,8 @@
   {
     "name": "iNatAg-mini/passiflora_suberosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -42991,8 +42991,8 @@
   {
     "name": "iNatAg-mini/pastinaca_sativa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43012,8 +43012,8 @@
   {
     "name": "iNatAg-mini/paullinia_cupana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43033,8 +43033,8 @@
   {
     "name": "iNatAg-mini/paulownia_tomentosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43054,8 +43054,8 @@
   {
     "name": "iNatAg-mini/peganum_harmala",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43075,8 +43075,8 @@
   {
     "name": "iNatAg-mini/pelargonium_graveolens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43096,8 +43096,8 @@
   {
     "name": "iNatAg-mini/peltandra_sagittifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43117,8 +43117,8 @@
   {
     "name": "iNatAg-mini/peltandra_virginica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43138,8 +43138,8 @@
   {
     "name": "iNatAg-mini/peltophorum_africanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43159,8 +43159,8 @@
   {
     "name": "iNatAg-mini/peltophorum_pterocarpum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43180,8 +43180,8 @@
   {
     "name": "iNatAg-mini/pennisetum_clandestinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43201,8 +43201,8 @@
   {
     "name": "iNatAg-mini/pennisetum_glaucum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43222,8 +43222,8 @@
   {
     "name": "iNatAg-mini/pennisetum_macrourum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43243,8 +43243,8 @@
   {
     "name": "iNatAg-mini/pennisetum_pedicellatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43264,8 +43264,8 @@
   {
     "name": "iNatAg-mini/pennisetum_polystachyon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43285,8 +43285,8 @@
   {
     "name": "iNatAg-mini/pennisetum_purpureum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43306,8 +43306,8 @@
   {
     "name": "iNatAg-mini/pennisetum_setaceum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43327,8 +43327,8 @@
   {
     "name": "iNatAg-mini/pennisetum_villosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43348,8 +43348,8 @@
   {
     "name": "iNatAg-mini/perilla_frutescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43369,8 +43369,8 @@
   {
     "name": "iNatAg-mini/persea_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43390,8 +43390,8 @@
   {
     "name": "iNatAg-mini/persicaria_maculosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43411,8 +43411,8 @@
   {
     "name": "iNatAg-mini/persoonia_falcata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43432,8 +43432,8 @@
   {
     "name": "iNatAg-mini/petalostigma_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43453,8 +43453,8 @@
   {
     "name": "iNatAg-mini/petasites_albus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43474,8 +43474,8 @@
   {
     "name": "iNatAg-mini/petasites_hybridus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43495,8 +43495,8 @@
   {
     "name": "iNatAg-mini/petroselinum_crispum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43516,8 +43516,8 @@
   {
     "name": "iNatAg-mini/petunia_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43537,8 +43537,8 @@
   {
     "name": "iNatAg-mini/peucedanum_ostruthium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43558,8 +43558,8 @@
   {
     "name": "iNatAg-mini/phalaris_aquatica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43579,8 +43579,8 @@
   {
     "name": "iNatAg-mini/phalaris_arundinacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43600,8 +43600,8 @@
   {
     "name": "iNatAg-mini/phalaris_arundinaceae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43621,8 +43621,8 @@
   {
     "name": "iNatAg-mini/phalaris_brachystachys",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43642,8 +43642,8 @@
   {
     "name": "iNatAg-mini/phalaris_canariensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43663,8 +43663,8 @@
   {
     "name": "iNatAg-mini/phalaris_caroliniana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43684,8 +43684,8 @@
   {
     "name": "iNatAg-mini/phalaris_coerulescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43705,8 +43705,8 @@
   {
     "name": "iNatAg-mini/phalaris_paradoxa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43726,8 +43726,8 @@
   {
     "name": "iNatAg-mini/phaseolus_acutifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43747,8 +43747,8 @@
   {
     "name": "iNatAg-mini/phaseolus_coccineus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43768,8 +43768,8 @@
   {
     "name": "iNatAg-mini/phaseolus_lunatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43789,8 +43789,8 @@
   {
     "name": "iNatAg-mini/phaseolus_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43810,8 +43810,8 @@
   {
     "name": "iNatAg-mini/phleum_alpinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43831,8 +43831,8 @@
   {
     "name": "iNatAg-mini/phleum_pratense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43852,8 +43852,8 @@
   {
     "name": "iNatAg-mini/phoenix_dactylifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43873,8 +43873,8 @@
   {
     "name": "iNatAg-mini/phoenix_reclinata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43894,8 +43894,8 @@
   {
     "name": "iNatAg-mini/phoenix_sylvestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43915,8 +43915,8 @@
   {
     "name": "iNatAg-mini/phormium_tenax",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43936,8 +43936,8 @@
   {
     "name": "iNatAg-mini/phragmites_australis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43957,8 +43957,8 @@
   {
     "name": "iNatAg-mini/phragmites_communis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43978,8 +43978,8 @@
   {
     "name": "iNatAg-mini/phragmites_karka",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -43999,8 +43999,8 @@
   {
     "name": "iNatAg-mini/phyllanthus_niruri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44020,8 +44020,8 @@
   {
     "name": "iNatAg-mini/phyllanthus_tenellus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44041,8 +44041,8 @@
   {
     "name": "iNatAg-mini/phyllanthus_urinaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44062,8 +44062,8 @@
   {
     "name": "iNatAg-mini/phyllocladus_aspleniifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44083,8 +44083,8 @@
   {
     "name": "iNatAg-mini/physalis_alkekengi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44104,8 +44104,8 @@
   {
     "name": "iNatAg-mini/physalis_angulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44125,8 +44125,8 @@
   {
     "name": "iNatAg-mini/physalis_heterophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44146,8 +44146,8 @@
   {
     "name": "iNatAg-mini/physalis_lancifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44167,8 +44167,8 @@
   {
     "name": "iNatAg-mini/physalis_peruviana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44188,8 +44188,8 @@
   {
     "name": "iNatAg-mini/physalis_philadelphica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44209,8 +44209,8 @@
   {
     "name": "iNatAg-mini/physalis_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44230,8 +44230,8 @@
   {
     "name": "iNatAg-mini/physalis_virginiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44251,8 +44251,8 @@
   {
     "name": "iNatAg-mini/physalis_viscosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44272,8 +44272,8 @@
   {
     "name": "iNatAg-mini/phytolacca_acinosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44293,8 +44293,8 @@
   {
     "name": "iNatAg-mini/phytolacca_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44314,8 +44314,8 @@
   {
     "name": "iNatAg-mini/phytolacca_dioica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44335,8 +44335,8 @@
   {
     "name": "iNatAg-mini/picea_abies",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44356,8 +44356,8 @@
   {
     "name": "iNatAg-mini/picea_omorica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44377,8 +44377,8 @@
   {
     "name": "iNatAg-mini/picea_omorika",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44398,8 +44398,8 @@
   {
     "name": "iNatAg-mini/picris_echioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44419,8 +44419,8 @@
   {
     "name": "iNatAg-mini/picris_hieracioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44440,8 +44440,8 @@
   {
     "name": "iNatAg-mini/piliostigma_reticulatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44461,8 +44461,8 @@
   {
     "name": "iNatAg-mini/piliostigma_thonningii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44482,8 +44482,8 @@
   {
     "name": "iNatAg-mini/pimenta_dioica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44503,8 +44503,8 @@
   {
     "name": "iNatAg-mini/pimenta_racemosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44524,8 +44524,8 @@
   {
     "name": "iNatAg-mini/pimpinella_anisum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44545,8 +44545,8 @@
   {
     "name": "iNatAg-mini/pimpinella_saxifraga",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44566,8 +44566,8 @@
   {
     "name": "iNatAg-mini/pinguicula_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44587,8 +44587,8 @@
   {
     "name": "iNatAg-mini/pinus_ayacahuite",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44608,8 +44608,8 @@
   {
     "name": "iNatAg-mini/pinus_brutia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44629,8 +44629,8 @@
   {
     "name": "iNatAg-mini/pinus_canariensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44650,8 +44650,8 @@
   {
     "name": "iNatAg-mini/pinus_caribaea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44671,8 +44671,8 @@
   {
     "name": "iNatAg-mini/pinus_chiapensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44692,8 +44692,8 @@
   {
     "name": "iNatAg-mini/pinus_douglasiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44713,8 +44713,8 @@
   {
     "name": "iNatAg-mini/pinus_durangensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44734,8 +44734,8 @@
   {
     "name": "iNatAg-mini/pinus_greggii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44755,8 +44755,8 @@
   {
     "name": "iNatAg-mini/pinus_halepensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44776,8 +44776,8 @@
   {
     "name": "iNatAg-mini/pinus_hartwegii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44797,8 +44797,8 @@
   {
     "name": "iNatAg-mini/pinus_kesiya",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44818,8 +44818,8 @@
   {
     "name": "iNatAg-mini/pinus_merkusii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44839,8 +44839,8 @@
   {
     "name": "iNatAg-mini/pinus_montezumae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44860,8 +44860,8 @@
   {
     "name": "iNatAg-mini/pinus_mugo",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44881,8 +44881,8 @@
   {
     "name": "iNatAg-mini/pinus_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44902,8 +44902,8 @@
   {
     "name": "iNatAg-mini/pinus_oocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44923,8 +44923,8 @@
   {
     "name": "iNatAg-mini/pinus_palustris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44944,8 +44944,8 @@
   {
     "name": "iNatAg-mini/pinus_patula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44965,8 +44965,8 @@
   {
     "name": "iNatAg-mini/pinus_pinaster",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -44986,8 +44986,8 @@
   {
     "name": "iNatAg-mini/pinus_pinea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45007,8 +45007,8 @@
   {
     "name": "iNatAg-mini/pinus_ponderosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45028,8 +45028,8 @@
   {
     "name": "iNatAg-mini/pinus_pseudostrobus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45049,8 +45049,8 @@
   {
     "name": "iNatAg-mini/pinus_radiata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45070,8 +45070,8 @@
   {
     "name": "iNatAg-mini/pinus_roxburghii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45091,8 +45091,8 @@
   {
     "name": "iNatAg-mini/pinus_sylvestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45112,8 +45112,8 @@
   {
     "name": "iNatAg-mini/pinus_tabuliformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45133,8 +45133,8 @@
   {
     "name": "iNatAg-mini/pinus_taeda",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45154,8 +45154,8 @@
   {
     "name": "iNatAg-mini/pinus_teocote",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45175,8 +45175,8 @@
   {
     "name": "iNatAg-mini/piper_aduncum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45196,8 +45196,8 @@
   {
     "name": "iNatAg-mini/piper_betle",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45217,8 +45217,8 @@
   {
     "name": "iNatAg-mini/piper_longum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45238,8 +45238,8 @@
   {
     "name": "iNatAg-mini/piper_methysticum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45259,8 +45259,8 @@
   {
     "name": "iNatAg-mini/piper_nigrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45280,8 +45280,8 @@
   {
     "name": "iNatAg-mini/pistacia_atlantica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45301,8 +45301,8 @@
   {
     "name": "iNatAg-mini/pistacia_lentiscus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45322,8 +45322,8 @@
   {
     "name": "iNatAg-mini/pistacia_vera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45343,8 +45343,8 @@
   {
     "name": "iNatAg-mini/pistia_stratiotes",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45364,8 +45364,8 @@
   {
     "name": "iNatAg-mini/pisum_sativum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45385,8 +45385,8 @@
   {
     "name": "iNatAg-mini/pithecellobium_dulce",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45406,8 +45406,8 @@
   {
     "name": "iNatAg-mini/pittosporum_resiniferum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45427,8 +45427,8 @@
   {
     "name": "iNatAg-mini/pittosporum_undulatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45448,8 +45448,8 @@
   {
     "name": "iNatAg-mini/plagiobothrys_canescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45469,8 +45469,8 @@
   {
     "name": "iNatAg-mini/plantago_coronopus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45490,8 +45490,8 @@
   {
     "name": "iNatAg-mini/plantago_heterophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45511,8 +45511,8 @@
   {
     "name": "iNatAg-mini/plantago_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45532,8 +45532,8 @@
   {
     "name": "iNatAg-mini/plantago_lanceolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45553,8 +45553,8 @@
   {
     "name": "iNatAg-mini/plantago_major",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45574,8 +45574,8 @@
   {
     "name": "iNatAg-mini/plantago_media",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45595,8 +45595,8 @@
   {
     "name": "iNatAg-mini/plantago_ovata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45616,8 +45616,8 @@
   {
     "name": "iNatAg-mini/plantago_psyllium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45637,8 +45637,8 @@
   {
     "name": "iNatAg-mini/plantago_virginica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45658,8 +45658,8 @@
   {
     "name": "iNatAg-mini/platanus_orientalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45679,8 +45679,8 @@
   {
     "name": "iNatAg-mini/poa_alpina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45700,8 +45700,8 @@
   {
     "name": "iNatAg-mini/poa_annua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45721,8 +45721,8 @@
   {
     "name": "iNatAg-mini/poa_bulbosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45742,8 +45742,8 @@
   {
     "name": "iNatAg-mini/poa_compressa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45763,8 +45763,8 @@
   {
     "name": "iNatAg-mini/poa_cuspidata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45784,8 +45784,8 @@
   {
     "name": "iNatAg-mini/poa_fendleriana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45805,8 +45805,8 @@
   {
     "name": "iNatAg-mini/poa_nemoralis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45826,8 +45826,8 @@
   {
     "name": "iNatAg-mini/poa_pratensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45847,8 +45847,8 @@
   {
     "name": "iNatAg-mini/poa_trivialis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45868,8 +45868,8 @@
   {
     "name": "iNatAg-mini/podocarpus_elatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45889,8 +45889,8 @@
   {
     "name": "iNatAg-mini/podocarpus_falcatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45910,8 +45910,8 @@
   {
     "name": "iNatAg-mini/poeciloneuron_indicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45931,8 +45931,8 @@
   {
     "name": "iNatAg-mini/pogostemon_cablin",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45952,8 +45952,8 @@
   {
     "name": "iNatAg-mini/polemonium_caeruleum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45973,8 +45973,8 @@
   {
     "name": "iNatAg-mini/polemonium_micranthum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -45994,8 +45994,8 @@
   {
     "name": "iNatAg-mini/polyalthia_fragrans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46015,8 +46015,8 @@
   {
     "name": "iNatAg-mini/polycarpon_tetraphyllum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46036,8 +46036,8 @@
   {
     "name": "iNatAg-mini/polygonatum_orientale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46057,8 +46057,8 @@
   {
     "name": "iNatAg-mini/polygonum_achoreum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46078,8 +46078,8 @@
   {
     "name": "iNatAg-mini/polygonum_arenastrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46099,8 +46099,8 @@
   {
     "name": "iNatAg-mini/polygonum_aviculare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46120,8 +46120,8 @@
   {
     "name": "iNatAg-mini/polygonum_bistorta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46141,8 +46141,8 @@
   {
     "name": "iNatAg-mini/polygonum_convolvulus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46162,8 +46162,8 @@
   {
     "name": "iNatAg-mini/polygonum_equisetiforme",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46183,8 +46183,8 @@
   {
     "name": "iNatAg-mini/polygonum_erectum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46204,8 +46204,8 @@
   {
     "name": "iNatAg-mini/polygonum_hydropiper",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46225,8 +46225,8 @@
   {
     "name": "iNatAg-mini/polygonum_hydropiperoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46246,8 +46246,8 @@
   {
     "name": "iNatAg-mini/polygonum_lapathifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46267,8 +46267,8 @@
   {
     "name": "iNatAg-mini/polygonum_orientale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46288,8 +46288,8 @@
   {
     "name": "iNatAg-mini/polygonum_pensylvanicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46309,8 +46309,8 @@
   {
     "name": "iNatAg-mini/polygonum_perfoliatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46330,8 +46330,8 @@
   {
     "name": "iNatAg-mini/polygonum_persicaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46351,8 +46351,8 @@
   {
     "name": "iNatAg-mini/polygonum_punctatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46372,8 +46372,8 @@
   {
     "name": "iNatAg-mini/polygonum_ramosissimum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46393,8 +46393,8 @@
   {
     "name": "iNatAg-mini/polygonum_scandens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46414,8 +46414,8 @@
   {
     "name": "iNatAg-mini/polymnia_sonchifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46435,8 +46435,8 @@
   {
     "name": "iNatAg-mini/polypodium_vulgare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46456,8 +46456,8 @@
   {
     "name": "iNatAg-mini/polypogon_interruptus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46477,8 +46477,8 @@
   {
     "name": "iNatAg-mini/polypremum_procumbens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46498,8 +46498,8 @@
   {
     "name": "iNatAg-mini/polyscias_fulva",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46519,8 +46519,8 @@
   {
     "name": "iNatAg-mini/polytrichum_commune",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46540,8 +46540,8 @@
   {
     "name": "iNatAg-mini/pongamia_pinnata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46561,8 +46561,8 @@
   {
     "name": "iNatAg-mini/pontederia_cordata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46582,8 +46582,8 @@
   {
     "name": "iNatAg-mini/pontederia_rotundifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46603,8 +46603,8 @@
   {
     "name": "iNatAg-mini/populus_balsamifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46624,8 +46624,8 @@
   {
     "name": "iNatAg-mini/populus_ciliata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46645,8 +46645,8 @@
   {
     "name": "iNatAg-mini/populus_deltoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46666,8 +46666,8 @@
   {
     "name": "iNatAg-mini/populus_euphratica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46687,8 +46687,8 @@
   {
     "name": "iNatAg-mini/populus_simonii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46708,8 +46708,8 @@
   {
     "name": "iNatAg-mini/portulaca_oleracea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46729,8 +46729,8 @@
   {
     "name": "iNatAg-mini/portulaca_pilosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46750,8 +46750,8 @@
   {
     "name": "iNatAg-mini/portulaca_pilosa_pilosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46771,8 +46771,8 @@
   {
     "name": "iNatAg-mini/portulaca_quadrifida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46792,8 +46792,8 @@
   {
     "name": "iNatAg-mini/potamogeton_diversifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46813,8 +46813,8 @@
   {
     "name": "iNatAg-mini/potamogeton_epihydrus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46834,8 +46834,8 @@
   {
     "name": "iNatAg-mini/potamogeton_filiformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46855,8 +46855,8 @@
   {
     "name": "iNatAg-mini/potamogeton_foliosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46876,8 +46876,8 @@
   {
     "name": "iNatAg-mini/potamogeton_friesii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46897,8 +46897,8 @@
   {
     "name": "iNatAg-mini/potamogeton_gramineus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46918,8 +46918,8 @@
   {
     "name": "iNatAg-mini/potamogeton_illinoensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46939,8 +46939,8 @@
   {
     "name": "iNatAg-mini/potamogeton_natans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46960,8 +46960,8 @@
   {
     "name": "iNatAg-mini/potamogeton_nodosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -46981,8 +46981,8 @@
   {
     "name": "iNatAg-mini/potamogeton_pectinatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47002,8 +47002,8 @@
   {
     "name": "iNatAg-mini/potamogeton_praelongus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47023,8 +47023,8 @@
   {
     "name": "iNatAg-mini/potamogeton_pusillus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47044,8 +47044,8 @@
   {
     "name": "iNatAg-mini/potamogeton_zosteriformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47065,8 +47065,8 @@
   {
     "name": "iNatAg-mini/potentilla_anserina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47086,8 +47086,8 @@
   {
     "name": "iNatAg-mini/potentilla_argentea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47107,8 +47107,8 @@
   {
     "name": "iNatAg-mini/potentilla_erecta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47128,8 +47128,8 @@
   {
     "name": "iNatAg-mini/potentilla_fruticosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47149,8 +47149,8 @@
   {
     "name": "iNatAg-mini/potentilla_intermedia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47170,8 +47170,8 @@
   {
     "name": "iNatAg-mini/potentilla_norvegica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47191,8 +47191,8 @@
   {
     "name": "iNatAg-mini/potentilla_norvegicae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47212,8 +47212,8 @@
   {
     "name": "iNatAg-mini/potentilla_recta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47233,8 +47233,8 @@
   {
     "name": "iNatAg-mini/potentilla_reptans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47254,8 +47254,8 @@
   {
     "name": "iNatAg-mini/potentilla_tridentata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47275,8 +47275,8 @@
   {
     "name": "iNatAg-mini/poterium_sanguisorba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47296,8 +47296,8 @@
   {
     "name": "iNatAg-mini/pouteria_campechiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47317,8 +47317,8 @@
   {
     "name": "iNatAg-mini/pouteria_lucuma",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47338,8 +47338,8 @@
   {
     "name": "iNatAg-mini/pouteria_sapota",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47359,8 +47359,8 @@
   {
     "name": "iNatAg-mini/prasophyllum_elatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47380,8 +47380,8 @@
   {
     "name": "iNatAg-mini/primula_veris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47401,8 +47401,8 @@
   {
     "name": "iNatAg-mini/proserpinaca_palustris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47422,8 +47422,8 @@
   {
     "name": "iNatAg-mini/proserpinaca_pectinata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47443,8 +47443,8 @@
   {
     "name": "iNatAg-mini/prosopis_affinis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47464,8 +47464,8 @@
   {
     "name": "iNatAg-mini/prosopis_africana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47485,8 +47485,8 @@
   {
     "name": "iNatAg-mini/prosopis_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47506,8 +47506,8 @@
   {
     "name": "iNatAg-mini/prosopis_chilensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47527,8 +47527,8 @@
   {
     "name": "iNatAg-mini/prosopis_cineraria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47548,8 +47548,8 @@
   {
     "name": "iNatAg-mini/prosopis_glandulosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47569,8 +47569,8 @@
   {
     "name": "iNatAg-mini/prosopis_juliflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47590,8 +47590,8 @@
   {
     "name": "iNatAg-mini/prosopis_nigra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47611,8 +47611,8 @@
   {
     "name": "iNatAg-mini/prosopis_pallida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47632,8 +47632,8 @@
   {
     "name": "iNatAg-mini/prosopis_tamarugo",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47653,8 +47653,8 @@
   {
     "name": "iNatAg-mini/prosopis_velutina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47674,8 +47674,8 @@
   {
     "name": "iNatAg-mini/prunella_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47695,8 +47695,8 @@
   {
     "name": "iNatAg-mini/prunus_africana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47716,8 +47716,8 @@
   {
     "name": "iNatAg-mini/prunus_amygdalus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47737,8 +47737,8 @@
   {
     "name": "iNatAg-mini/prunus_armeniaca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47758,8 +47758,8 @@
   {
     "name": "iNatAg-mini/prunus_avium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47779,8 +47779,8 @@
   {
     "name": "iNatAg-mini/prunus_capuli",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47800,8 +47800,8 @@
   {
     "name": "iNatAg-mini/prunus_cerasus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47821,8 +47821,8 @@
   {
     "name": "iNatAg-mini/prunus_domestica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47842,8 +47842,8 @@
   {
     "name": "iNatAg-mini/prunus_laurocerasus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47863,8 +47863,8 @@
   {
     "name": "iNatAg-mini/prunus_mahaleb",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47884,8 +47884,8 @@
   {
     "name": "iNatAg-mini/prunus_mume",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47905,8 +47905,8 @@
   {
     "name": "iNatAg-mini/prunus_padus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47926,8 +47926,8 @@
   {
     "name": "iNatAg-mini/prunus_pensylvanica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47947,8 +47947,8 @@
   {
     "name": "iNatAg-mini/prunus_persica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47968,8 +47968,8 @@
   {
     "name": "iNatAg-mini/prunus_salicina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -47989,8 +47989,8 @@
   {
     "name": "iNatAg-mini/prunus_spinosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48010,8 +48010,8 @@
   {
     "name": "iNatAg-mini/prunus_virginiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48031,8 +48031,8 @@
   {
     "name": "iNatAg-mini/psathyrostachys_juncea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48052,8 +48052,8 @@
   {
     "name": "iNatAg-mini/psidium_cattleianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48073,8 +48073,8 @@
   {
     "name": "iNatAg-mini/psidium_friedrichsthalianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48094,8 +48094,8 @@
   {
     "name": "iNatAg-mini/psidium_guajava",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48115,8 +48115,8 @@
   {
     "name": "iNatAg-mini/psophocarpus_tetragonolobus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48136,8 +48136,8 @@
   {
     "name": "iNatAg-mini/psoralea_repens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48157,8 +48157,8 @@
   {
     "name": "iNatAg-mini/ptelea_trifoliata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48178,8 +48178,8 @@
   {
     "name": "iNatAg-mini/pterocarpus_angolensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48199,8 +48199,8 @@
   {
     "name": "iNatAg-mini/pterocarpus_dalbergioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48220,8 +48220,8 @@
   {
     "name": "iNatAg-mini/pterocarpus_erinaceus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48241,8 +48241,8 @@
   {
     "name": "iNatAg-mini/pterocarpus_indicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48262,8 +48262,8 @@
   {
     "name": "iNatAg-mini/pterocarpus_lucens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48283,8 +48283,8 @@
   {
     "name": "iNatAg-mini/pterocarpus_macrocarpus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48304,8 +48304,8 @@
   {
     "name": "iNatAg-mini/pterocarpus_marsupium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48325,8 +48325,8 @@
   {
     "name": "iNatAg-mini/pterocarpus_santalinoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48346,8 +48346,8 @@
   {
     "name": "iNatAg-mini/pterocarpus_santalinus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48367,8 +48367,8 @@
   {
     "name": "iNatAg-mini/pueraria_lobata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48388,8 +48388,8 @@
   {
     "name": "iNatAg-mini/pueraria_phaseoloides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48409,8 +48409,8 @@
   {
     "name": "iNatAg-mini/pulmonaria_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48430,8 +48430,8 @@
   {
     "name": "iNatAg-mini/punica_granatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48451,8 +48451,8 @@
   {
     "name": "iNatAg-mini/pycnanthus_angolensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48472,8 +48472,8 @@
   {
     "name": "iNatAg-mini/pyrola_rotundifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48493,8 +48493,8 @@
   {
     "name": "iNatAg-mini/pyrus_communis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48514,8 +48514,8 @@
   {
     "name": "iNatAg-mini/pyrus_pyrifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48535,8 +48535,8 @@
   {
     "name": "iNatAg-mini/quercus_agrifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48556,8 +48556,8 @@
   {
     "name": "iNatAg-mini/quercus_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48577,8 +48577,8 @@
   {
     "name": "iNatAg-mini/quercus_bicolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48598,8 +48598,8 @@
   {
     "name": "iNatAg-mini/quercus_chrysolepis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48619,8 +48619,8 @@
   {
     "name": "iNatAg-mini/quercus_dumosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48640,8 +48640,8 @@
   {
     "name": "iNatAg-mini/quercus_fusiformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48661,8 +48661,8 @@
   {
     "name": "iNatAg-mini/quercus_ilex",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48682,8 +48682,8 @@
   {
     "name": "iNatAg-mini/quercus_incana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48703,8 +48703,8 @@
   {
     "name": "iNatAg-mini/quercus_lanata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48724,8 +48724,8 @@
   {
     "name": "iNatAg-mini/quercus_nigra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48745,8 +48745,8 @@
   {
     "name": "iNatAg-mini/quercus_phellos",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48766,8 +48766,8 @@
   {
     "name": "iNatAg-mini/quercus_robur",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48787,8 +48787,8 @@
   {
     "name": "iNatAg-mini/quercus_semecarpifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48808,8 +48808,8 @@
   {
     "name": "iNatAg-mini/quercus_suber",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48829,8 +48829,8 @@
   {
     "name": "iNatAg-mini/quercus_virginiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48850,8 +48850,8 @@
   {
     "name": "iNatAg-mini/quisqualis_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48871,8 +48871,8 @@
   {
     "name": "iNatAg-mini/ranunculus_abortivus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48892,8 +48892,8 @@
   {
     "name": "iNatAg-mini/ranunculus_acris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48913,8 +48913,8 @@
   {
     "name": "iNatAg-mini/ranunculus_arbortivus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48934,8 +48934,8 @@
   {
     "name": "iNatAg-mini/ranunculus_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48955,8 +48955,8 @@
   {
     "name": "iNatAg-mini/ranunculus_bulbosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48976,8 +48976,8 @@
   {
     "name": "iNatAg-mini/ranunculus_californicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -48997,8 +48997,8 @@
   {
     "name": "iNatAg-mini/ranunculus_cymbalaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49018,8 +49018,8 @@
   {
     "name": "iNatAg-mini/ranunculus_ficaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49039,8 +49039,8 @@
   {
     "name": "iNatAg-mini/ranunculus_flabellaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49060,8 +49060,8 @@
   {
     "name": "iNatAg-mini/ranunculus_muricatulus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49081,8 +49081,8 @@
   {
     "name": "iNatAg-mini/ranunculus_muricatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49102,8 +49102,8 @@
   {
     "name": "iNatAg-mini/ranunculus_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49123,8 +49123,8 @@
   {
     "name": "iNatAg-mini/ranunculus_orthorhynchus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49144,8 +49144,8 @@
   {
     "name": "iNatAg-mini/ranunculus_parviflorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49165,8 +49165,8 @@
   {
     "name": "iNatAg-mini/ranunculus_sceleratus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49186,8 +49186,8 @@
   {
     "name": "iNatAg-mini/ranunculus_testiculatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49207,8 +49207,8 @@
   {
     "name": "iNatAg-mini/ranunculus_trichophyllus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49228,8 +49228,8 @@
   {
     "name": "iNatAg-mini/raphanus_raphanistrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49249,8 +49249,8 @@
   {
     "name": "iNatAg-mini/raphanus_sativus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49270,8 +49270,8 @@
   {
     "name": "iNatAg-mini/rauvolfia_caffra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49291,8 +49291,8 @@
   {
     "name": "iNatAg-mini/rauvolfia_serpentina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49312,8 +49312,8 @@
   {
     "name": "iNatAg-mini/reseda_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49333,8 +49333,8 @@
   {
     "name": "iNatAg-mini/reseda_lutea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49354,8 +49354,8 @@
   {
     "name": "iNatAg-mini/retama_monosperma",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49375,8 +49375,8 @@
   {
     "name": "iNatAg-mini/rhamnus_cathartica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49396,8 +49396,8 @@
   {
     "name": "iNatAg-mini/rhamnus_prinoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49417,8 +49417,8 @@
   {
     "name": "iNatAg-mini/rheum_palmatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49438,8 +49438,8 @@
   {
     "name": "iNatAg-mini/rheum_rhaponticum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49459,8 +49459,8 @@
   {
     "name": "iNatAg-mini/rhigozum_trichotomum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49480,8 +49480,8 @@
   {
     "name": "iNatAg-mini/rhinanthus_crista-galli",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49501,8 +49501,8 @@
   {
     "name": "iNatAg-mini/rhinanthus_minor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49522,8 +49522,8 @@
   {
     "name": "iNatAg-mini/rhizophora_mangle",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49543,8 +49543,8 @@
   {
     "name": "iNatAg-mini/rhizophora_mucronata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49564,8 +49564,8 @@
   {
     "name": "iNatAg-mini/rhizophora_stylosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49585,8 +49585,8 @@
   {
     "name": "iNatAg-mini/rhodiola_rosea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49606,8 +49606,8 @@
   {
     "name": "iNatAg-mini/rhododendron_ferrugineum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49627,8 +49627,8 @@
   {
     "name": "iNatAg-mini/rhus_copallinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49648,8 +49648,8 @@
   {
     "name": "iNatAg-mini/rhus_glabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49669,8 +49669,8 @@
   {
     "name": "iNatAg-mini/rhus_typhina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49690,8 +49690,8 @@
   {
     "name": "iNatAg-mini/rhynchosia_minima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49711,8 +49711,8 @@
   {
     "name": "iNatAg-mini/rhynchosia_senna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49732,8 +49732,8 @@
   {
     "name": "iNatAg-mini/rhynchosia_sublobata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49753,8 +49753,8 @@
   {
     "name": "iNatAg-mini/ribes_hirtellum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49774,8 +49774,8 @@
   {
     "name": "iNatAg-mini/ribes_nigrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49795,8 +49795,8 @@
   {
     "name": "iNatAg-mini/ribes_rubrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49816,8 +49816,8 @@
   {
     "name": "iNatAg-mini/ribes_uva-crispa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49837,8 +49837,8 @@
   {
     "name": "iNatAg-mini/ribes_viscosissimum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49858,8 +49858,8 @@
   {
     "name": "iNatAg-mini/richardia_brasiliensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49879,8 +49879,8 @@
   {
     "name": "iNatAg-mini/richardia_scabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49900,8 +49900,8 @@
   {
     "name": "iNatAg-mini/ricinus_communis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49921,8 +49921,8 @@
   {
     "name": "iNatAg-mini/ricinus_comunis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49942,8 +49942,8 @@
   {
     "name": "iNatAg-mini/rivina_humilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49963,8 +49963,8 @@
   {
     "name": "iNatAg-mini/robinia_pseudoacacia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -49984,8 +49984,8 @@
   {
     "name": "iNatAg-mini/roemeria_refracta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50005,8 +50005,8 @@
   {
     "name": "iNatAg-mini/rosa_canina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50026,8 +50026,8 @@
   {
     "name": "iNatAg-mini/rosa_cinnamomea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50047,8 +50047,8 @@
   {
     "name": "iNatAg-mini/rosa_eglanteria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50068,8 +50068,8 @@
   {
     "name": "iNatAg-mini/rosa_pendulina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50089,8 +50089,8 @@
   {
     "name": "iNatAg-mini/rosa_pimpinellifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50110,8 +50110,8 @@
   {
     "name": "iNatAg-mini/rosa_rubiginosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50131,8 +50131,8 @@
   {
     "name": "iNatAg-mini/rosa_spinosissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50152,8 +50152,8 @@
   {
     "name": "iNatAg-mini/roseodendron_donnell-smithii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50173,8 +50173,8 @@
   {
     "name": "iNatAg-mini/rosmarinus_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50194,8 +50194,8 @@
   {
     "name": "iNatAg-mini/rubia_tinctorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50215,8 +50215,8 @@
   {
     "name": "iNatAg-mini/rubus_ellipticus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50236,8 +50236,8 @@
   {
     "name": "iNatAg-mini/rubus_fructicosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50257,8 +50257,8 @@
   {
     "name": "iNatAg-mini/rubus_fruticosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50278,8 +50278,8 @@
   {
     "name": "iNatAg-mini/rubus_hispidus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50299,8 +50299,8 @@
   {
     "name": "iNatAg-mini/rubus_idaeus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50320,8 +50320,8 @@
   {
     "name": "iNatAg-mini/rubus_moluccanus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50341,8 +50341,8 @@
   {
     "name": "iNatAg-mini/rubus_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50362,8 +50362,8 @@
   {
     "name": "iNatAg-mini/rubus_pensilvanicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50383,8 +50383,8 @@
   {
     "name": "iNatAg-mini/rudbeckia_amplexicaulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50404,8 +50404,8 @@
   {
     "name": "iNatAg-mini/rudbeckia_hirta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50425,8 +50425,8 @@
   {
     "name": "iNatAg-mini/rudbeckia_laciniata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50446,8 +50446,8 @@
   {
     "name": "iNatAg-mini/rudbeckia_triloba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50467,8 +50467,8 @@
   {
     "name": "iNatAg-mini/rumex_acetosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50488,8 +50488,8 @@
   {
     "name": "iNatAg-mini/rumex_acetosella",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50509,8 +50509,8 @@
   {
     "name": "iNatAg-mini/rumex_aquaticus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50530,8 +50530,8 @@
   {
     "name": "iNatAg-mini/rumex_crispus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50551,8 +50551,8 @@
   {
     "name": "iNatAg-mini/rumex_dentatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50572,8 +50572,8 @@
   {
     "name": "iNatAg-mini/rumex_hymenosepalus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50593,8 +50593,8 @@
   {
     "name": "iNatAg-mini/rumex_longifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50614,8 +50614,8 @@
   {
     "name": "iNatAg-mini/rumex_maritimus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50635,8 +50635,8 @@
   {
     "name": "iNatAg-mini/rumex_obtusifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50656,8 +50656,8 @@
   {
     "name": "iNatAg-mini/rumex_patienta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50677,8 +50677,8 @@
   {
     "name": "iNatAg-mini/rumex_patientia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50698,8 +50698,8 @@
   {
     "name": "iNatAg-mini/rumex_pseudonatronatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50719,8 +50719,8 @@
   {
     "name": "iNatAg-mini/rumex_pulcher",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50740,8 +50740,8 @@
   {
     "name": "iNatAg-mini/rumex_verticillatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50761,8 +50761,8 @@
   {
     "name": "iNatAg-mini/ruppia_maritima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50782,8 +50782,8 @@
   {
     "name": "iNatAg-mini/ruscus_aculeatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50803,8 +50803,8 @@
   {
     "name": "iNatAg-mini/ruta_graveolens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50824,8 +50824,8 @@
   {
     "name": "iNatAg-mini/saccharum_officinarum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50845,8 +50845,8 @@
   {
     "name": "iNatAg-mini/saccharum_sinense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50866,8 +50866,8 @@
   {
     "name": "iNatAg-mini/saccharum_spontaneum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50887,8 +50887,8 @@
   {
     "name": "iNatAg-mini/sacorstemma_cynanchoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50908,8 +50908,8 @@
   {
     "name": "iNatAg-mini/sagina_procumbens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50929,8 +50929,8 @@
   {
     "name": "iNatAg-mini/sagittaria_kurziana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50950,8 +50950,8 @@
   {
     "name": "iNatAg-mini/sagittaria_lancifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50971,8 +50971,8 @@
   {
     "name": "iNatAg-mini/sagittaria_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -50992,8 +50992,8 @@
   {
     "name": "iNatAg-mini/sagittaria_sagittifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51013,8 +51013,8 @@
   {
     "name": "iNatAg-mini/salacca_wallichiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51034,8 +51034,8 @@
   {
     "name": "iNatAg-mini/salacca_zalacca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51055,8 +51055,8 @@
   {
     "name": "iNatAg-mini/salicornia_bigelovii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51076,8 +51076,8 @@
   {
     "name": "iNatAg-mini/salix_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51097,8 +51097,8 @@
   {
     "name": "iNatAg-mini/salix_caprea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51118,8 +51118,8 @@
   {
     "name": "iNatAg-mini/salix_laevigata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51139,8 +51139,8 @@
   {
     "name": "iNatAg-mini/salix_pentandra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51160,8 +51160,8 @@
   {
     "name": "iNatAg-mini/salix_viminalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51181,8 +51181,8 @@
   {
     "name": "iNatAg-mini/salsola_kali",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51202,8 +51202,8 @@
   {
     "name": "iNatAg-mini/salsola_tragus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51223,8 +51223,8 @@
   {
     "name": "iNatAg-mini/salsola_vermiculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51244,8 +51244,8 @@
   {
     "name": "iNatAg-mini/salvadora_persica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51265,8 +51265,8 @@
   {
     "name": "iNatAg-mini/salvia_lyrata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51286,8 +51286,8 @@
   {
     "name": "iNatAg-mini/salvia_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51307,8 +51307,8 @@
   {
     "name": "iNatAg-mini/salvia_sclarea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51328,8 +51328,8 @@
   {
     "name": "iNatAg-mini/salvia_verticillata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51349,8 +51349,8 @@
   {
     "name": "iNatAg-mini/salvinia_auriculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51370,8 +51370,8 @@
   {
     "name": "iNatAg-mini/samanea_saman",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51391,8 +51391,8 @@
   {
     "name": "iNatAg-mini/sambucus_canadensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51412,8 +51412,8 @@
   {
     "name": "iNatAg-mini/sambucus_canadiensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51433,8 +51433,8 @@
   {
     "name": "iNatAg-mini/sambucus_cerulea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51454,8 +51454,8 @@
   {
     "name": "iNatAg-mini/sambucus_ebulus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51475,8 +51475,8 @@
   {
     "name": "iNatAg-mini/sambucus_nigra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51496,8 +51496,8 @@
   {
     "name": "iNatAg-mini/sambucus_racemosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51517,8 +51517,8 @@
   {
     "name": "iNatAg-mini/samolus_parviflorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51538,8 +51538,8 @@
   {
     "name": "iNatAg-mini/samolus_valerandi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51559,8 +51559,8 @@
   {
     "name": "iNatAg-mini/sanguisorba_minor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51580,8 +51580,8 @@
   {
     "name": "iNatAg-mini/sanguisorba_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51601,8 +51601,8 @@
   {
     "name": "iNatAg-mini/sanicula_europaea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51622,8 +51622,8 @@
   {
     "name": "iNatAg-mini/santalum_acuminatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51643,8 +51643,8 @@
   {
     "name": "iNatAg-mini/santalum_album",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51664,8 +51664,8 @@
   {
     "name": "iNatAg-mini/santolina_chamaecyparissus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51685,8 +51685,8 @@
   {
     "name": "iNatAg-mini/sapindus_emarginatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51706,8 +51706,8 @@
   {
     "name": "iNatAg-mini/sapindus_saponaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51727,8 +51727,8 @@
   {
     "name": "iNatAg-mini/sapium_sebiferum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51748,8 +51748,8 @@
   {
     "name": "iNatAg-mini/saponaria_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51769,8 +51769,8 @@
   {
     "name": "iNatAg-mini/sarcostemma_cynanchoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51790,8 +51790,8 @@
   {
     "name": "iNatAg-mini/satureja_hortensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51811,8 +51811,8 @@
   {
     "name": "iNatAg-mini/satureja_montana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51832,8 +51832,8 @@
   {
     "name": "iNatAg-mini/sauropus_androgynus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51853,8 +51853,8 @@
   {
     "name": "iNatAg-mini/saururus_cernuus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51874,8 +51874,8 @@
   {
     "name": "iNatAg-mini/scandix_pecten-veneris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51895,8 +51895,8 @@
   {
     "name": "iNatAg-mini/schima_wallichii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51916,8 +51916,8 @@
   {
     "name": "iNatAg-mini/schinus_molle",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51937,8 +51937,8 @@
   {
     "name": "iNatAg-mini/schinus_terebinthifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51958,8 +51958,8 @@
   {
     "name": "iNatAg-mini/schinus_terebinthifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -51979,8 +51979,8 @@
   {
     "name": "iNatAg-mini/schismus_arabicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52000,8 +52000,8 @@
   {
     "name": "iNatAg-mini/schizolobium_parahyba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52021,8 +52021,8 @@
   {
     "name": "iNatAg-mini/schizomeria_ovata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52042,8 +52042,8 @@
   {
     "name": "iNatAg-mini/schleichera_oleosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52063,8 +52063,8 @@
   {
     "name": "iNatAg-mini/scirpus_lacustris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52084,8 +52084,8 @@
   {
     "name": "iNatAg-mini/scleranthus_annuus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52105,8 +52105,8 @@
   {
     "name": "iNatAg-mini/sclerocarya_caffra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52126,8 +52126,8 @@
   {
     "name": "iNatAg-mini/scoparia_dulcis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52147,8 +52147,8 @@
   {
     "name": "iNatAg-mini/scorzonera_laciniata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52168,8 +52168,8 @@
   {
     "name": "iNatAg-mini/scrophularia_lanceolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52189,8 +52189,8 @@
   {
     "name": "iNatAg-mini/searsia_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52210,8 +52210,8 @@
   {
     "name": "iNatAg-mini/secale_cereale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52231,8 +52231,8 @@
   {
     "name": "iNatAg-mini/secale_montanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52252,8 +52252,8 @@
   {
     "name": "iNatAg-mini/sechium_edule",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52273,8 +52273,8 @@
   {
     "name": "iNatAg-mini/securidaca_longepedunculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52294,8 +52294,8 @@
   {
     "name": "iNatAg-mini/securidaca_longipedunculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52315,8 +52315,8 @@
   {
     "name": "iNatAg-mini/sedum_acre",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52336,8 +52336,8 @@
   {
     "name": "iNatAg-mini/sedum_telephium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52357,8 +52357,8 @@
   {
     "name": "iNatAg-mini/sehima_nervosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52378,8 +52378,8 @@
   {
     "name": "iNatAg-mini/sempervivum_arachnoideum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52399,8 +52399,8 @@
   {
     "name": "iNatAg-mini/sempervivum_tectorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52420,8 +52420,8 @@
   {
     "name": "iNatAg-mini/senecio_elegans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52441,8 +52441,8 @@
   {
     "name": "iNatAg-mini/senecio_jacobaea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52462,8 +52462,8 @@
   {
     "name": "iNatAg-mini/senecio_madagascariensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52483,8 +52483,8 @@
   {
     "name": "iNatAg-mini/senecio_plattensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52504,8 +52504,8 @@
   {
     "name": "iNatAg-mini/senecio_squalidus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52525,8 +52525,8 @@
   {
     "name": "iNatAg-mini/senecio_sylvaticus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52546,8 +52546,8 @@
   {
     "name": "iNatAg-mini/senecio_viscosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52567,8 +52567,8 @@
   {
     "name": "iNatAg-mini/senecio_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52588,8 +52588,8 @@
   {
     "name": "iNatAg-mini/senna_spectabilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52609,8 +52609,8 @@
   {
     "name": "iNatAg-mini/serenoa_repens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52630,8 +52630,8 @@
   {
     "name": "iNatAg-mini/sesamum_indicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52651,8 +52651,8 @@
   {
     "name": "iNatAg-mini/sesbania_bispinosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52672,8 +52672,8 @@
   {
     "name": "iNatAg-mini/sesbania_cannabina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52693,8 +52693,8 @@
   {
     "name": "iNatAg-mini/sesbania_exaltata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52714,8 +52714,8 @@
   {
     "name": "iNatAg-mini/sesbania_formosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52735,8 +52735,8 @@
   {
     "name": "iNatAg-mini/sesbania_grandiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52756,8 +52756,8 @@
   {
     "name": "iNatAg-mini/sesbania_pachycarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52777,8 +52777,8 @@
   {
     "name": "iNatAg-mini/sesbania_sesban",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52798,8 +52798,8 @@
   {
     "name": "iNatAg-mini/setaria_incrassata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52819,8 +52819,8 @@
   {
     "name": "iNatAg-mini/setaria_italica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52840,8 +52840,8 @@
   {
     "name": "iNatAg-mini/setaria_lindenbergiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52861,8 +52861,8 @@
   {
     "name": "iNatAg-mini/setaria_pumila",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52882,8 +52882,8 @@
   {
     "name": "iNatAg-mini/seymeria_pectinata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52903,8 +52903,8 @@
   {
     "name": "iNatAg-mini/shorea_robusta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52924,8 +52924,8 @@
   {
     "name": "iNatAg-mini/shorea_talura",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52945,8 +52945,8 @@
   {
     "name": "iNatAg-mini/sicyos_angulatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52966,8 +52966,8 @@
   {
     "name": "iNatAg-mini/sida_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -52987,8 +52987,8 @@
   {
     "name": "iNatAg-mini/sida_cordifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53008,8 +53008,8 @@
   {
     "name": "iNatAg-mini/sida_spinosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53029,8 +53029,8 @@
   {
     "name": "iNatAg-mini/silene_antirrhina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53050,8 +53050,8 @@
   {
     "name": "iNatAg-mini/silene_armeria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53071,8 +53071,8 @@
   {
     "name": "iNatAg-mini/silene_conica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53092,8 +53092,8 @@
   {
     "name": "iNatAg-mini/silene_conoidea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53113,8 +53113,8 @@
   {
     "name": "iNatAg-mini/silene_gallica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53134,8 +53134,8 @@
   {
     "name": "iNatAg-mini/silene_noctiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53155,8 +53155,8 @@
   {
     "name": "iNatAg-mini/silene_pendula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53176,8 +53176,8 @@
   {
     "name": "iNatAg-mini/silphium_asperrimum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53197,8 +53197,8 @@
   {
     "name": "iNatAg-mini/silphium_integrifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53218,8 +53218,8 @@
   {
     "name": "iNatAg-mini/silphium_laciniatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53239,8 +53239,8 @@
   {
     "name": "iNatAg-mini/silybum_marianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53260,8 +53260,8 @@
   {
     "name": "iNatAg-mini/simarouba_glauca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53281,8 +53281,8 @@
   {
     "name": "iNatAg-mini/simmondsia_chinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53302,8 +53302,8 @@
   {
     "name": "iNatAg-mini/simsia_auriculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53323,8 +53323,8 @@
   {
     "name": "iNatAg-mini/sinapis_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53344,8 +53344,8 @@
   {
     "name": "iNatAg-mini/sinapis_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53365,8 +53365,8 @@
   {
     "name": "iNatAg-mini/sinapis_incana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53386,8 +53386,8 @@
   {
     "name": "iNatAg-mini/siphonochilus_aethiopicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53407,8 +53407,8 @@
   {
     "name": "iNatAg-mini/sisymbrium_altissimum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53428,8 +53428,8 @@
   {
     "name": "iNatAg-mini/sisymbrium_erysimoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53449,8 +53449,8 @@
   {
     "name": "iNatAg-mini/sisymbrium_irio",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53470,8 +53470,8 @@
   {
     "name": "iNatAg-mini/sisymbrium_officinale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53491,8 +53491,8 @@
   {
     "name": "iNatAg-mini/sisymbrium_orientale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53512,8 +53512,8 @@
   {
     "name": "iNatAg-mini/sisymbrium_sophia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53533,8 +53533,8 @@
   {
     "name": "iNatAg-mini/sisyrinchium_montanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53554,8 +53554,8 @@
   {
     "name": "iNatAg-mini/sloanea_woollsii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53575,8 +53575,8 @@
   {
     "name": "iNatAg-mini/smilax_aspera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53596,8 +53596,8 @@
   {
     "name": "iNatAg-mini/smilax_bona-nox",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53617,8 +53617,8 @@
   {
     "name": "iNatAg-mini/smilax_laurifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53638,8 +53638,8 @@
   {
     "name": "iNatAg-mini/smilax_rotundifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53659,8 +53659,8 @@
   {
     "name": "iNatAg-mini/solanum_aethiopicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53680,8 +53680,8 @@
   {
     "name": "iNatAg-mini/solanum_americanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53701,8 +53701,8 @@
   {
     "name": "iNatAg-mini/solanum_capsicoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53722,8 +53722,8 @@
   {
     "name": "iNatAg-mini/solanum_carolinense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53743,8 +53743,8 @@
   {
     "name": "iNatAg-mini/solanum_coriaceum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53764,8 +53764,8 @@
   {
     "name": "iNatAg-mini/solanum_dimidiatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53785,8 +53785,8 @@
   {
     "name": "iNatAg-mini/solanum_diphyllum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53806,8 +53806,8 @@
   {
     "name": "iNatAg-mini/solanum_dulcamara",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53827,8 +53827,8 @@
   {
     "name": "iNatAg-mini/solanum_elaeagnifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53848,8 +53848,8 @@
   {
     "name": "iNatAg-mini/solanum_eleagnifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53869,8 +53869,8 @@
   {
     "name": "iNatAg-mini/solanum_ellipticum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53890,8 +53890,8 @@
   {
     "name": "iNatAg-mini/solanum_ferox",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53911,8 +53911,8 @@
   {
     "name": "iNatAg-mini/solanum_heterodoxum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53932,8 +53932,8 @@
   {
     "name": "iNatAg-mini/solanum_incanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53953,8 +53953,8 @@
   {
     "name": "iNatAg-mini/solanum_jamaicense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53974,8 +53974,8 @@
   {
     "name": "iNatAg-mini/solanum_lanceolatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -53995,8 +53995,8 @@
   {
     "name": "iNatAg-mini/solanum_macrocarpon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54016,8 +54016,8 @@
   {
     "name": "iNatAg-mini/solanum_mammosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54037,8 +54037,8 @@
   {
     "name": "iNatAg-mini/solanum_marginatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54058,8 +54058,8 @@
   {
     "name": "iNatAg-mini/solanum_mauritianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54079,8 +54079,8 @@
   {
     "name": "iNatAg-mini/solanum_melongena",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54100,8 +54100,8 @@
   {
     "name": "iNatAg-mini/solanum_muricatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54121,8 +54121,8 @@
   {
     "name": "iNatAg-mini/solanum_nigrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54142,8 +54142,8 @@
   {
     "name": "iNatAg-mini/solanum_physalifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54163,8 +54163,8 @@
   {
     "name": "iNatAg-mini/solanum_pseudo-capsicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54184,8 +54184,8 @@
   {
     "name": "iNatAg-mini/solanum_pseudocapsicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54205,8 +54205,8 @@
   {
     "name": "iNatAg-mini/solanum_quitoense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54226,8 +54226,8 @@
   {
     "name": "iNatAg-mini/solanum_sisymbrifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54247,8 +54247,8 @@
   {
     "name": "iNatAg-mini/solanum_sisymbriifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54268,8 +54268,8 @@
   {
     "name": "iNatAg-mini/solanum_tampicense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54289,8 +54289,8 @@
   {
     "name": "iNatAg-mini/solanum_torvum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54310,8 +54310,8 @@
   {
     "name": "iNatAg-mini/solanum_tuberosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54331,8 +54331,8 @@
   {
     "name": "iNatAg-mini/solanum_violaceum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54352,8 +54352,8 @@
   {
     "name": "iNatAg-mini/soldanella_alpina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54373,8 +54373,8 @@
   {
     "name": "iNatAg-mini/solidago_californica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54394,8 +54394,8 @@
   {
     "name": "iNatAg-mini/solidago_canadensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54415,8 +54415,8 @@
   {
     "name": "iNatAg-mini/solidago_fistulosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54436,8 +54436,8 @@
   {
     "name": "iNatAg-mini/solidago_missouriensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54457,8 +54457,8 @@
   {
     "name": "iNatAg-mini/solidago_nemoralis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54478,8 +54478,8 @@
   {
     "name": "iNatAg-mini/solidago_rigida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54499,8 +54499,8 @@
   {
     "name": "iNatAg-mini/solidago_sempervirens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54520,8 +54520,8 @@
   {
     "name": "iNatAg-mini/solidago_virgaurea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54541,8 +54541,8 @@
   {
     "name": "iNatAg-mini/sonchus_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54562,8 +54562,8 @@
   {
     "name": "iNatAg-mini/sonchus_oleraceus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54583,8 +54583,8 @@
   {
     "name": "iNatAg-mini/sonchus_palustris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54604,8 +54604,8 @@
   {
     "name": "iNatAg-mini/sonneratia_apetala",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54625,8 +54625,8 @@
   {
     "name": "iNatAg-mini/sonneratia_caseolaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54646,8 +54646,8 @@
   {
     "name": "iNatAg-mini/sorbus_aucuparia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54667,8 +54667,8 @@
   {
     "name": "iNatAg-mini/sorbus_domestica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54688,8 +54688,8 @@
   {
     "name": "iNatAg-mini/sorghum_bicolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54709,8 +54709,8 @@
   {
     "name": "iNatAg-mini/sorghum_drummondii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54730,8 +54730,8 @@
   {
     "name": "iNatAg-mini/sorghum_halepense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54751,8 +54751,8 @@
   {
     "name": "iNatAg-mini/soymida_febrifuga",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54772,8 +54772,8 @@
   {
     "name": "iNatAg-mini/sparganium_americanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54793,8 +54793,8 @@
   {
     "name": "iNatAg-mini/sparganium_erectum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54814,8 +54814,8 @@
   {
     "name": "iNatAg-mini/spartina_pectinata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54835,8 +54835,8 @@
   {
     "name": "iNatAg-mini/spartium_junceum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54856,8 +54856,8 @@
   {
     "name": "iNatAg-mini/spathodea_campanulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54877,8 +54877,8 @@
   {
     "name": "iNatAg-mini/spergula_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54898,8 +54898,8 @@
   {
     "name": "iNatAg-mini/spermacoce_verticillata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54919,8 +54919,8 @@
   {
     "name": "iNatAg-mini/spinacia_oleracea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54940,8 +54940,8 @@
   {
     "name": "iNatAg-mini/spinifex_hirsutus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54961,8 +54961,8 @@
   {
     "name": "iNatAg-mini/spirea_tomentosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -54982,8 +54982,8 @@
   {
     "name": "iNatAg-mini/spodiopogon_sibiricus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55003,8 +55003,8 @@
   {
     "name": "iNatAg-mini/spondias_cythera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55024,8 +55024,8 @@
   {
     "name": "iNatAg-mini/spondias_mombin",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55045,8 +55045,8 @@
   {
     "name": "iNatAg-mini/spondias_purpurea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55066,8 +55066,8 @@
   {
     "name": "iNatAg-mini/sporobolus_airoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55087,8 +55087,8 @@
   {
     "name": "iNatAg-mini/sporobolus_fimbriatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55108,8 +55108,8 @@
   {
     "name": "iNatAg-mini/sporobolus_maritimus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55129,8 +55129,8 @@
   {
     "name": "iNatAg-mini/sporobolus_neglectus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55150,8 +55150,8 @@
   {
     "name": "iNatAg-mini/sporobolus_spicatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55171,8 +55171,8 @@
   {
     "name": "iNatAg-mini/sporobolus_virginicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55192,8 +55192,8 @@
   {
     "name": "iNatAg-mini/stachys_affinis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55213,8 +55213,8 @@
   {
     "name": "iNatAg-mini/stachys_palustris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55234,8 +55234,8 @@
   {
     "name": "iNatAg-mini/stachytarpheta_incana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55255,8 +55255,8 @@
   {
     "name": "iNatAg-mini/stachytarpheta_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55276,8 +55276,8 @@
   {
     "name": "iNatAg-mini/stellaria_graminea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55297,8 +55297,8 @@
   {
     "name": "iNatAg-mini/stellaria_holostea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55318,8 +55318,8 @@
   {
     "name": "iNatAg-mini/stellaria_media",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55339,8 +55339,8 @@
   {
     "name": "iNatAg-mini/stenotaphrum_secundatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55360,8 +55360,8 @@
   {
     "name": "iNatAg-mini/sterculia_foetida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55381,8 +55381,8 @@
   {
     "name": "iNatAg-mini/sterculia_urens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55402,8 +55402,8 @@
   {
     "name": "iNatAg-mini/sterculia_villosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55423,8 +55423,8 @@
   {
     "name": "iNatAg-mini/stereospermum_kunthianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55444,8 +55444,8 @@
   {
     "name": "iNatAg-mini/stevia_rebaudiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55465,8 +55465,8 @@
   {
     "name": "iNatAg-mini/stipa_baicalensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55486,8 +55486,8 @@
   {
     "name": "iNatAg-mini/stipa_brachychaeta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55507,8 +55507,8 @@
   {
     "name": "iNatAg-mini/stipa_capillata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55528,8 +55528,8 @@
   {
     "name": "iNatAg-mini/stipa_glareosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55549,8 +55549,8 @@
   {
     "name": "iNatAg-mini/stipa_grandis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55570,8 +55570,8 @@
   {
     "name": "iNatAg-mini/stipa_krylovii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55591,8 +55591,8 @@
   {
     "name": "iNatAg-mini/stipa_lagascae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55612,8 +55612,8 @@
   {
     "name": "iNatAg-mini/stipa_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55633,8 +55633,8 @@
   {
     "name": "iNatAg-mini/stipa_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55654,8 +55654,8 @@
   {
     "name": "iNatAg-mini/stipa_tenacissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55675,8 +55675,8 @@
   {
     "name": "iNatAg-mini/stipa_trichotoma",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55696,8 +55696,8 @@
   {
     "name": "iNatAg-mini/stipagrostis_amabilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55717,8 +55717,8 @@
   {
     "name": "iNatAg-mini/stipagrostis_zeyheri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55738,8 +55738,8 @@
   {
     "name": "iNatAg-mini/stratiotes_aloides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55759,8 +55759,8 @@
   {
     "name": "iNatAg-mini/strychnos_cocculoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55780,8 +55780,8 @@
   {
     "name": "iNatAg-mini/strychnos_innocua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55801,8 +55801,8 @@
   {
     "name": "iNatAg-mini/strychnos_spinosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55822,8 +55822,8 @@
   {
     "name": "iNatAg-mini/stylidium_desertorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55843,8 +55843,8 @@
   {
     "name": "iNatAg-mini/stylosanthes_capitata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55864,8 +55864,8 @@
   {
     "name": "iNatAg-mini/stylosanthes_fruticosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55885,8 +55885,8 @@
   {
     "name": "iNatAg-mini/stylosanthes_hamata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55906,8 +55906,8 @@
   {
     "name": "iNatAg-mini/stylosanthes_humilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55927,8 +55927,8 @@
   {
     "name": "iNatAg-mini/stylosanthes_scabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55948,8 +55948,8 @@
   {
     "name": "iNatAg-mini/stylosanthes_viscosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55969,8 +55969,8 @@
   {
     "name": "iNatAg-mini/succisa_pratensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -55990,8 +55990,8 @@
   {
     "name": "iNatAg-mini/swertia_baicalensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56011,8 +56011,8 @@
   {
     "name": "iNatAg-mini/swietenia_macrophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56032,8 +56032,8 @@
   {
     "name": "iNatAg-mini/swietenia_mahogani",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56053,8 +56053,8 @@
   {
     "name": "iNatAg-mini/symphoricarpos_mollis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56074,8 +56074,8 @@
   {
     "name": "iNatAg-mini/symphoricarpos_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56095,8 +56095,8 @@
   {
     "name": "iNatAg-mini/symphoricarpos_orbiculatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56116,8 +56116,8 @@
   {
     "name": "iNatAg-mini/symphoricarpos_rotundifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56137,8 +56137,8 @@
   {
     "name": "iNatAg-mini/symphytum_officinale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56158,8 +56158,8 @@
   {
     "name": "iNatAg-mini/syncarpia_glomulifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56179,8 +56179,8 @@
   {
     "name": "iNatAg-mini/syncarpia_hillii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56200,8 +56200,8 @@
   {
     "name": "iNatAg-mini/syzygium_cordatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56221,8 +56221,8 @@
   {
     "name": "iNatAg-mini/syzygium_cumini",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56242,8 +56242,8 @@
   {
     "name": "iNatAg-mini/syzygium_guineense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56263,8 +56263,8 @@
   {
     "name": "iNatAg-mini/syzygium_malaccense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56284,8 +56284,8 @@
   {
     "name": "iNatAg-mini/syzygium_taiwanicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56305,8 +56305,8 @@
   {
     "name": "iNatAg-mini/tabebuia_rosea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56326,8 +56326,8 @@
   {
     "name": "iNatAg-mini/tabebuia_serratifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56347,8 +56347,8 @@
   {
     "name": "iNatAg-mini/tagetes_minuta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56368,8 +56368,8 @@
   {
     "name": "iNatAg-mini/talinum_triangulare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56389,8 +56389,8 @@
   {
     "name": "iNatAg-mini/tamarindus_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56410,8 +56410,8 @@
   {
     "name": "iNatAg-mini/tamarix_aphylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56431,8 +56431,8 @@
   {
     "name": "iNatAg-mini/tamarix_chinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56452,8 +56452,8 @@
   {
     "name": "iNatAg-mini/tamarix_gallica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56473,8 +56473,8 @@
   {
     "name": "iNatAg-mini/tamarix_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56494,8 +56494,8 @@
   {
     "name": "iNatAg-mini/tanacetum_balsamita",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56515,8 +56515,8 @@
   {
     "name": "iNatAg-mini/tanacetum_vulgare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56536,8 +56536,8 @@
   {
     "name": "iNatAg-mini/taraxacum_officinale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56557,8 +56557,8 @@
   {
     "name": "iNatAg-mini/taraxia_breviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56578,8 +56578,8 @@
   {
     "name": "iNatAg-mini/tarchonanthus_camphoratus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56599,8 +56599,8 @@
   {
     "name": "iNatAg-mini/taxodium_distichum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56620,8 +56620,8 @@
   {
     "name": "iNatAg-mini/taxus_baccata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56641,8 +56641,8 @@
   {
     "name": "iNatAg-mini/tecoma_stans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56662,8 +56662,8 @@
   {
     "name": "iNatAg-mini/tectona_grandis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56683,8 +56683,8 @@
   {
     "name": "iNatAg-mini/tephrosia_candida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56704,8 +56704,8 @@
   {
     "name": "iNatAg-mini/tephrosia_lupinifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56725,8 +56725,8 @@
   {
     "name": "iNatAg-mini/tephrosia_obovata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56746,8 +56746,8 @@
   {
     "name": "iNatAg-mini/tephrosia_purpurea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56767,8 +56767,8 @@
   {
     "name": "iNatAg-mini/tephrosia_vogelii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56788,8 +56788,8 @@
   {
     "name": "iNatAg-mini/teramnus_labialis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56809,8 +56809,8 @@
   {
     "name": "iNatAg-mini/terminalia_arjuna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56830,8 +56830,8 @@
   {
     "name": "iNatAg-mini/terminalia_bellirica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56851,8 +56851,8 @@
   {
     "name": "iNatAg-mini/terminalia_brownii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56872,8 +56872,8 @@
   {
     "name": "iNatAg-mini/terminalia_calamansanai",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56893,8 +56893,8 @@
   {
     "name": "iNatAg-mini/terminalia_catappa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56914,8 +56914,8 @@
   {
     "name": "iNatAg-mini/terminalia_chebula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56935,8 +56935,8 @@
   {
     "name": "iNatAg-mini/terminalia_ivorensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56956,8 +56956,8 @@
   {
     "name": "iNatAg-mini/terminalia_mantaly",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56977,8 +56977,8 @@
   {
     "name": "iNatAg-mini/terminalia_myriocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -56998,8 +56998,8 @@
   {
     "name": "iNatAg-mini/terminalia_paniculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57019,8 +57019,8 @@
   {
     "name": "iNatAg-mini/terminalia_prunioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57040,8 +57040,8 @@
   {
     "name": "iNatAg-mini/terminalia_sericocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57061,8 +57061,8 @@
   {
     "name": "iNatAg-mini/terminalia_tomentosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57082,8 +57082,8 @@
   {
     "name": "iNatAg-mini/tetradymia_canescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57103,8 +57103,8 @@
   {
     "name": "iNatAg-mini/tetragonia_tetragonioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57124,8 +57124,8 @@
   {
     "name": "iNatAg-mini/teucrium_botrys",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57145,8 +57145,8 @@
   {
     "name": "iNatAg-mini/teucrium_canadense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57166,8 +57166,8 @@
   {
     "name": "iNatAg-mini/teucrium_chamaedrys",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57187,8 +57187,8 @@
   {
     "name": "iNatAg-mini/teucrium_polium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57208,8 +57208,8 @@
   {
     "name": "iNatAg-mini/thalia_geniculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57229,8 +57229,8 @@
   {
     "name": "iNatAg-mini/thalictrum_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57250,8 +57250,8 @@
   {
     "name": "iNatAg-mini/thaumatococcus_daniellii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57271,8 +57271,8 @@
   {
     "name": "iNatAg-mini/themeda_australis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57292,8 +57292,8 @@
   {
     "name": "iNatAg-mini/themeda_quadrivalvis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57313,8 +57313,8 @@
   {
     "name": "iNatAg-mini/themeda_triandra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57334,8 +57334,8 @@
   {
     "name": "iNatAg-mini/theobroma_bicolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57355,8 +57355,8 @@
   {
     "name": "iNatAg-mini/theobroma_cacao",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57376,8 +57376,8 @@
   {
     "name": "iNatAg-mini/theobroma_grandiflorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57397,8 +57397,8 @@
   {
     "name": "iNatAg-mini/thermopsis_montana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57418,8 +57418,8 @@
   {
     "name": "iNatAg-mini/thermopsis_rhombifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57439,8 +57439,8 @@
   {
     "name": "iNatAg-mini/thespesia_populnea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57460,8 +57460,8 @@
   {
     "name": "iNatAg-mini/thlaspi_arvense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57481,8 +57481,8 @@
   {
     "name": "iNatAg-mini/thlaspi_perfoliatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57502,8 +57502,8 @@
   {
     "name": "iNatAg-mini/thuja_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57523,8 +57523,8 @@
   {
     "name": "iNatAg-mini/thymus_serphyllum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57544,8 +57544,8 @@
   {
     "name": "iNatAg-mini/thymus_serpyllum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57565,8 +57565,8 @@
   {
     "name": "iNatAg-mini/thymus_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57586,8 +57586,8 @@
   {
     "name": "iNatAg-mini/thyrsostachys_siamensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57607,8 +57607,8 @@
   {
     "name": "iNatAg-mini/thysanolaena_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57628,8 +57628,8 @@
   {
     "name": "iNatAg-mini/tilia_cordata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57649,8 +57649,8 @@
   {
     "name": "iNatAg-mini/tilia_platyphyllos",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57670,8 +57670,8 @@
   {
     "name": "iNatAg-mini/tipuana_tipu",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57691,8 +57691,8 @@
   {
     "name": "iNatAg-mini/tithonia_diversifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57712,8 +57712,8 @@
   {
     "name": "iNatAg-mini/toona_ciliata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57733,8 +57733,8 @@
   {
     "name": "iNatAg-mini/torenia_glabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57754,8 +57754,8 @@
   {
     "name": "iNatAg-mini/toxicodendron_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57775,8 +57775,8 @@
   {
     "name": "iNatAg-mini/trachypogon_spicatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57796,8 +57796,8 @@
   {
     "name": "iNatAg-mini/tradescantia_bracteata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57817,8 +57817,8 @@
   {
     "name": "iNatAg-mini/tradescantia_fluminensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57838,8 +57838,8 @@
   {
     "name": "iNatAg-mini/tradescantia_ohiensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57859,8 +57859,8 @@
   {
     "name": "iNatAg-mini/tradescantia_virginiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57880,8 +57880,8 @@
   {
     "name": "iNatAg-mini/tragia_betonicifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57901,8 +57901,8 @@
   {
     "name": "iNatAg-mini/tragopogon_lamottei",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57922,8 +57922,8 @@
   {
     "name": "iNatAg-mini/tragopogon_porrifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57943,8 +57943,8 @@
   {
     "name": "iNatAg-mini/tragopogon_pratensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57964,8 +57964,8 @@
   {
     "name": "iNatAg-mini/tragus_koelerioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -57985,8 +57985,8 @@
   {
     "name": "iNatAg-mini/trapa_natans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58006,8 +58006,8 @@
   {
     "name": "iNatAg-mini/trema_orientale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58027,8 +58027,8 @@
   {
     "name": "iNatAg-mini/trianthema_portulacastrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58048,8 +58048,8 @@
   {
     "name": "iNatAg-mini/tribulus_cistoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58069,8 +58069,8 @@
   {
     "name": "iNatAg-mini/tribulus_terrestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58090,8 +58090,8 @@
   {
     "name": "iNatAg-mini/trichanthera_gigantea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58111,8 +58111,8 @@
   {
     "name": "iNatAg-mini/trichoneura_grandiglumis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58132,8 +58132,8 @@
   {
     "name": "iNatAg-mini/trichosanthes_cucumerina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58153,8 +58153,8 @@
   {
     "name": "iNatAg-mini/trichostema_lanceolatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58174,8 +58174,8 @@
   {
     "name": "iNatAg-mini/tridax_procumbens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58195,8 +58195,8 @@
   {
     "name": "iNatAg-mini/trifolium_africanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58216,8 +58216,8 @@
   {
     "name": "iNatAg-mini/trifolium_alexandrinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58237,8 +58237,8 @@
   {
     "name": "iNatAg-mini/trifolium_ambiguum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58258,8 +58258,8 @@
   {
     "name": "iNatAg-mini/trifolium_angustifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58279,8 +58279,8 @@
   {
     "name": "iNatAg-mini/trifolium_arvense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58300,8 +58300,8 @@
   {
     "name": "iNatAg-mini/trifolium_burchellianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58321,8 +58321,8 @@
   {
     "name": "iNatAg-mini/trifolium_campestre",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58342,8 +58342,8 @@
   {
     "name": "iNatAg-mini/trifolium_carolinianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58363,8 +58363,8 @@
   {
     "name": "iNatAg-mini/trifolium_cherleri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58384,8 +58384,8 @@
   {
     "name": "iNatAg-mini/trifolium_dubium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58405,8 +58405,8 @@
   {
     "name": "iNatAg-mini/trifolium_fragiferum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58426,8 +58426,8 @@
   {
     "name": "iNatAg-mini/trifolium_glanduliferum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58447,8 +58447,8 @@
   {
     "name": "iNatAg-mini/trifolium_glomeratum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58468,8 +58468,8 @@
   {
     "name": "iNatAg-mini/trifolium_hirtum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58489,8 +58489,8 @@
   {
     "name": "iNatAg-mini/trifolium_hybridum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58510,8 +58510,8 @@
   {
     "name": "iNatAg-mini/trifolium_incarnatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58531,8 +58531,8 @@
   {
     "name": "iNatAg-mini/trifolium_medium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58552,8 +58552,8 @@
   {
     "name": "iNatAg-mini/trifolium_michelianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58573,8 +58573,8 @@
   {
     "name": "iNatAg-mini/trifolium_nigrescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58594,8 +58594,8 @@
   {
     "name": "iNatAg-mini/trifolium_patens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58615,8 +58615,8 @@
   {
     "name": "iNatAg-mini/trifolium_pilulare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58636,8 +58636,8 @@
   {
     "name": "iNatAg-mini/trifolium_polymorphum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58657,8 +58657,8 @@
   {
     "name": "iNatAg-mini/trifolium_pratense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58678,8 +58678,8 @@
   {
     "name": "iNatAg-mini/trifolium_reflexum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58699,8 +58699,8 @@
   {
     "name": "iNatAg-mini/trifolium_repens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58720,8 +58720,8 @@
   {
     "name": "iNatAg-mini/trifolium_resupinatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58741,8 +58741,8 @@
   {
     "name": "iNatAg-mini/trifolium_subterraneum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58762,8 +58762,8 @@
   {
     "name": "iNatAg-mini/trifolium_tomentosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58783,8 +58783,8 @@
   {
     "name": "iNatAg-mini/trifolium_variegatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58804,8 +58804,8 @@
   {
     "name": "iNatAg-mini/trifolium_vesiculosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58825,8 +58825,8 @@
   {
     "name": "iNatAg-mini/trifolium_wormskioldii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58846,8 +58846,8 @@
   {
     "name": "iNatAg-mini/triglochin_maritima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58867,8 +58867,8 @@
   {
     "name": "iNatAg-mini/triglochin_maritimum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58888,8 +58888,8 @@
   {
     "name": "iNatAg-mini/triglochin_palustre",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58909,8 +58909,8 @@
   {
     "name": "iNatAg-mini/trigonella_foenum-graecum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58930,8 +58930,8 @@
   {
     "name": "iNatAg-mini/tripsacum_dactyloides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58951,8 +58951,8 @@
   {
     "name": "iNatAg-mini/trisetum_flavescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58972,8 +58972,8 @@
   {
     "name": "iNatAg-mini/tristachya_leucothrix",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -58993,8 +58993,8 @@
   {
     "name": "iNatAg-mini/triticum_aestivum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59014,8 +59014,8 @@
   {
     "name": "iNatAg-mini/triticum_dicoccoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59035,8 +59035,8 @@
   {
     "name": "iNatAg-mini/triticum_durum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59056,8 +59056,8 @@
   {
     "name": "iNatAg-mini/triticum_spelta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59077,8 +59077,8 @@
   {
     "name": "iNatAg-mini/triumfetta_rhomboidea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59098,8 +59098,8 @@
   {
     "name": "iNatAg-mini/triumfetta_semitriloba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59119,8 +59119,8 @@
   {
     "name": "iNatAg-mini/trollius_europaeus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59140,8 +59140,8 @@
   {
     "name": "iNatAg-mini/tropaeolum_majus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59161,8 +59161,8 @@
   {
     "name": "iNatAg-mini/tropaeolum_tuberosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59182,8 +59182,8 @@
   {
     "name": "iNatAg-mini/tropidocarpum_gracile",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59203,8 +59203,8 @@
   {
     "name": "iNatAg-mini/turritis_glabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59224,8 +59224,8 @@
   {
     "name": "iNatAg-mini/tussilago_farfara",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59245,8 +59245,8 @@
   {
     "name": "iNatAg-mini/tylosema_esculentum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59266,8 +59266,8 @@
   {
     "name": "iNatAg-mini/typha_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59287,8 +59287,8 @@
   {
     "name": "iNatAg-mini/typha_domingensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59308,8 +59308,8 @@
   {
     "name": "iNatAg-mini/typha_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59329,8 +59329,8 @@
   {
     "name": "iNatAg-mini/uapaca_kirkiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59350,8 +59350,8 @@
   {
     "name": "iNatAg-mini/ulex_europaeus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59371,8 +59371,8 @@
   {
     "name": "iNatAg-mini/ullucus_tuberosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59392,8 +59392,8 @@
   {
     "name": "iNatAg-mini/ulmus_procera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59413,8 +59413,8 @@
   {
     "name": "iNatAg-mini/umbilicus_rupestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59434,8 +59434,8 @@
   {
     "name": "iNatAg-mini/uncaria_gambir",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59455,8 +59455,8 @@
   {
     "name": "iNatAg-mini/urelytrum_agropyroides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59476,8 +59476,8 @@
   {
     "name": "iNatAg-mini/urena_lobata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59497,8 +59497,8 @@
   {
     "name": "iNatAg-mini/urochloa_mosambicensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59518,8 +59518,8 @@
   {
     "name": "iNatAg-mini/urochloa_panicoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59539,8 +59539,8 @@
   {
     "name": "iNatAg-mini/urtica_chamaedryoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59560,8 +59560,8 @@
   {
     "name": "iNatAg-mini/urtica_dioica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59581,8 +59581,8 @@
   {
     "name": "iNatAg-mini/urtica_urens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59602,8 +59602,8 @@
   {
     "name": "iNatAg-mini/utricularia_floridana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59623,8 +59623,8 @@
   {
     "name": "iNatAg-mini/utricularia_foliosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59644,8 +59644,8 @@
   {
     "name": "iNatAg-mini/utricularia_gibba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59665,8 +59665,8 @@
   {
     "name": "iNatAg-mini/utricularia_purpurea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59686,8 +59686,8 @@
   {
     "name": "iNatAg-mini/utricularia_radiata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59707,8 +59707,8 @@
   {
     "name": "iNatAg-mini/utricularia_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59728,8 +59728,8 @@
   {
     "name": "iNatAg-mini/uvaria_littoralis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59749,8 +59749,8 @@
   {
     "name": "iNatAg-mini/uvularia_sessilifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59770,8 +59770,8 @@
   {
     "name": "iNatAg-mini/vaccinium_angustifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59791,8 +59791,8 @@
   {
     "name": "iNatAg-mini/vaccinium_corymbosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59812,8 +59812,8 @@
   {
     "name": "iNatAg-mini/vaccinium_macrocarpon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59833,8 +59833,8 @@
   {
     "name": "iNatAg-mini/vaccinium_myrtillus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59854,8 +59854,8 @@
   {
     "name": "iNatAg-mini/vaccinium_uliginosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59875,8 +59875,8 @@
   {
     "name": "iNatAg-mini/vaccinium_vitis-idaea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59896,8 +59896,8 @@
   {
     "name": "iNatAg-mini/valeriana_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59917,8 +59917,8 @@
   {
     "name": "iNatAg-mini/valerianella_eriocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59938,8 +59938,8 @@
   {
     "name": "iNatAg-mini/vallisneria_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59959,8 +59959,8 @@
   {
     "name": "iNatAg-mini/vangueria_infausta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -59980,8 +59980,8 @@
   {
     "name": "iNatAg-mini/vangueria_madagascariensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60001,8 +60001,8 @@
   {
     "name": "iNatAg-mini/vanilla_planifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60022,8 +60022,8 @@
   {
     "name": "iNatAg-mini/vateria_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60043,8 +60043,8 @@
   {
     "name": "iNatAg-mini/ventilago_viminalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60064,8 +60064,8 @@
   {
     "name": "iNatAg-mini/veratrum_album",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60085,8 +60085,8 @@
   {
     "name": "iNatAg-mini/veratrum_californicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60106,8 +60106,8 @@
   {
     "name": "iNatAg-mini/verbascum_blattaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60127,8 +60127,8 @@
   {
     "name": "iNatAg-mini/verbascum_lychnitis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60148,8 +60148,8 @@
   {
     "name": "iNatAg-mini/verbascum_phlomoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60169,8 +60169,8 @@
   {
     "name": "iNatAg-mini/verbascum_thapsus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60190,8 +60190,8 @@
   {
     "name": "iNatAg-mini/verbascum_thaspus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60211,8 +60211,8 @@
   {
     "name": "iNatAg-mini/verbena_bonariensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60232,8 +60232,8 @@
   {
     "name": "iNatAg-mini/verbena_brasiliensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60253,8 +60253,8 @@
   {
     "name": "iNatAg-mini/verbena_hastata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60274,8 +60274,8 @@
   {
     "name": "iNatAg-mini/verbena_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60295,8 +60295,8 @@
   {
     "name": "iNatAg-mini/verbena_urticifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60316,8 +60316,8 @@
   {
     "name": "iNatAg-mini/vernonia_altissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60337,8 +60337,8 @@
   {
     "name": "iNatAg-mini/vernonia_amygdalina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60358,8 +60358,8 @@
   {
     "name": "iNatAg-mini/vernonia_baldwinii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60379,8 +60379,8 @@
   {
     "name": "iNatAg-mini/vernonia_chamaedrys",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60400,8 +60400,8 @@
   {
     "name": "iNatAg-mini/vernonia_fasciculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60421,8 +60421,8 @@
   {
     "name": "iNatAg-mini/veronica_agrestis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60442,8 +60442,8 @@
   {
     "name": "iNatAg-mini/veronica_anagallis-aquatica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60463,8 +60463,8 @@
   {
     "name": "iNatAg-mini/veronica_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60484,8 +60484,8 @@
   {
     "name": "iNatAg-mini/veronica_biloba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60505,8 +60505,8 @@
   {
     "name": "iNatAg-mini/veronica_chamaedrys",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60526,8 +60526,8 @@
   {
     "name": "iNatAg-mini/veronica_filiformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60547,8 +60547,8 @@
   {
     "name": "iNatAg-mini/veronica_hederaefolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60568,8 +60568,8 @@
   {
     "name": "iNatAg-mini/veronica_hederifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60589,8 +60589,8 @@
   {
     "name": "iNatAg-mini/veronica_longifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60610,8 +60610,8 @@
   {
     "name": "iNatAg-mini/veronica_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60631,8 +60631,8 @@
   {
     "name": "iNatAg-mini/veronica_peregrina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60652,8 +60652,8 @@
   {
     "name": "iNatAg-mini/veronica_polita",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60673,8 +60673,8 @@
   {
     "name": "iNatAg-mini/veronica_serpyllifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60694,8 +60694,8 @@
   {
     "name": "iNatAg-mini/vetiveria_zizanioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60715,8 +60715,8 @@
   {
     "name": "iNatAg-mini/viburnum_cassinoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60736,8 +60736,8 @@
   {
     "name": "iNatAg-mini/viburnum_lentago",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60757,8 +60757,8 @@
   {
     "name": "iNatAg-mini/viburnum_prunifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60778,8 +60778,8 @@
   {
     "name": "iNatAg-mini/viccia_cracca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60799,8 +60799,8 @@
   {
     "name": "iNatAg-mini/vicia_augustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60820,8 +60820,8 @@
   {
     "name": "iNatAg-mini/vicia_benghalensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60841,8 +60841,8 @@
   {
     "name": "iNatAg-mini/vicia_cracca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60862,8 +60862,8 @@
   {
     "name": "iNatAg-mini/vicia_ervilia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60883,8 +60883,8 @@
   {
     "name": "iNatAg-mini/vicia_faba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60904,8 +60904,8 @@
   {
     "name": "iNatAg-mini/vicia_monantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60925,8 +60925,8 @@
   {
     "name": "iNatAg-mini/vicia_narbonensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60946,8 +60946,8 @@
   {
     "name": "iNatAg-mini/vicia_pannonica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60967,8 +60967,8 @@
   {
     "name": "iNatAg-mini/vicia_sativa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -60988,8 +60988,8 @@
   {
     "name": "iNatAg-mini/vicia_sepium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61009,8 +61009,8 @@
   {
     "name": "iNatAg-mini/vigna_adenantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61030,8 +61030,8 @@
   {
     "name": "iNatAg-mini/vigna_angularis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61051,8 +61051,8 @@
   {
     "name": "iNatAg-mini/vigna_hosei",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61072,8 +61072,8 @@
   {
     "name": "iNatAg-mini/vigna_lanceolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61093,8 +61093,8 @@
   {
     "name": "iNatAg-mini/vigna_longifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61114,8 +61114,8 @@
   {
     "name": "iNatAg-mini/vigna_luteola",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61135,8 +61135,8 @@
   {
     "name": "iNatAg-mini/vigna_parkeri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61156,8 +61156,8 @@
   {
     "name": "iNatAg-mini/vigna_radiata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61177,8 +61177,8 @@
   {
     "name": "iNatAg-mini/vigna_trilobata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61198,8 +61198,8 @@
   {
     "name": "iNatAg-mini/vigna_umbellata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61219,8 +61219,8 @@
   {
     "name": "iNatAg-mini/vigna_unguiculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61240,8 +61240,8 @@
   {
     "name": "iNatAg-mini/vigna_vexillata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61261,8 +61261,8 @@
   {
     "name": "iNatAg-mini/vinca_major",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61282,8 +61282,8 @@
   {
     "name": "iNatAg-mini/vinca_minor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61303,8 +61303,8 @@
   {
     "name": "iNatAg-mini/viola_lanceolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61324,8 +61324,8 @@
   {
     "name": "iNatAg-mini/viola_odorata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61345,8 +61345,8 @@
   {
     "name": "iNatAg-mini/viola_tricolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61366,8 +61366,8 @@
   {
     "name": "iNatAg-mini/viscum_album",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61387,8 +61387,8 @@
   {
     "name": "iNatAg-mini/vitellaria_paradoxa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61408,8 +61408,8 @@
   {
     "name": "iNatAg-mini/vitex_agnus-castus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61429,8 +61429,8 @@
   {
     "name": "iNatAg-mini/vitex_doniana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61450,8 +61450,8 @@
   {
     "name": "iNatAg-mini/vitex_negundo",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61471,8 +61471,8 @@
   {
     "name": "iNatAg-mini/vitis_labrusca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61492,8 +61492,8 @@
   {
     "name": "iNatAg-mini/vitis_rotundifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61513,8 +61513,8 @@
   {
     "name": "iNatAg-mini/vitis_vinifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61534,8 +61534,8 @@
   {
     "name": "iNatAg-mini/vitis_vulpina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61555,8 +61555,8 @@
   {
     "name": "iNatAg-mini/waltheria_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61576,8 +61576,8 @@
   {
     "name": "iNatAg-mini/warburgia_salutaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61597,8 +61597,8 @@
   {
     "name": "iNatAg-mini/warburgia_ugandensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61618,8 +61618,8 @@
   {
     "name": "iNatAg-mini/withania_somnifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61639,8 +61639,8 @@
   {
     "name": "iNatAg-mini/wrightia_tomentosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61660,8 +61660,8 @@
   {
     "name": "iNatAg-mini/xanthium_spinosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61681,8 +61681,8 @@
   {
     "name": "iNatAg-mini/xanthium_strumarium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61702,8 +61702,8 @@
   {
     "name": "iNatAg-mini/xanthosoma_sagittifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61723,8 +61723,8 @@
   {
     "name": "iNatAg-mini/ximenia_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61744,8 +61744,8 @@
   {
     "name": "iNatAg-mini/xylia_xylocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61765,8 +61765,8 @@
   {
     "name": "iNatAg-mini/xylocarpus_granatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61786,8 +61786,8 @@
   {
     "name": "iNatAg-mini/xylocarpus_mekongensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61807,8 +61807,8 @@
   {
     "name": "iNatAg-mini/xylocarpus_moluccensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61828,8 +61828,8 @@
   {
     "name": "iNatAg-mini/xylorhiza_glabriuscula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61849,8 +61849,8 @@
   {
     "name": "iNatAg-mini/yucca_elephantipes",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61870,8 +61870,8 @@
   {
     "name": "iNatAg-mini/zannichellia_palustris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61891,8 +61891,8 @@
   {
     "name": "iNatAg-mini/zanthoxylum_americanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61912,8 +61912,8 @@
   {
     "name": "iNatAg-mini/zea_mays",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61933,8 +61933,8 @@
   {
     "name": "iNatAg-mini/zingiber_officinale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61954,8 +61954,8 @@
   {
     "name": "iNatAg-mini/zizania_aquatica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61975,8 +61975,8 @@
   {
     "name": "iNatAg-mini/zizania_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -61996,8 +61996,8 @@
   {
     "name": "iNatAg-mini/ziziphus_abyssinica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62017,8 +62017,8 @@
   {
     "name": "iNatAg-mini/ziziphus_mauritiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62038,8 +62038,8 @@
   {
     "name": "iNatAg-mini/ziziphus_mucronata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62059,8 +62059,8 @@
   {
     "name": "iNatAg-mini/zornia_diphylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62080,8 +62080,8 @@
   {
     "name": "iNatAg-mini/zornia_glochidiata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62101,8 +62101,8 @@
   {
     "name": "iNatAg-mini/zornia_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62122,8 +62122,8 @@
   {
     "name": "iNatAg-mini/zostera_marina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62143,8 +62143,8 @@
   {
     "name": "iNatAg-mini/zoysia_matrella",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62164,8 +62164,8 @@
   {
     "name": "iNatAg-mini/zygophyllum_fabago",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62185,8 +62185,8 @@
   {
     "name": "iNatAg/abelmoschus_esculentus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62206,8 +62206,8 @@
   {
     "name": "iNatAg/abelmoschus_manihot",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62227,8 +62227,8 @@
   {
     "name": "iNatAg/abelmoschus_moschatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62248,8 +62248,8 @@
   {
     "name": "iNatAg/abies_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62269,8 +62269,8 @@
   {
     "name": "iNatAg/abies_amabilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62290,8 +62290,8 @@
   {
     "name": "iNatAg/abies_balsamea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62311,8 +62311,8 @@
   {
     "name": "iNatAg/abies_concolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62332,8 +62332,8 @@
   {
     "name": "iNatAg/abies_pindrow",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62353,8 +62353,8 @@
   {
     "name": "iNatAg/abroma_augustum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62374,8 +62374,8 @@
   {
     "name": "iNatAg/abrus_pecatorius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62395,8 +62395,8 @@
   {
     "name": "iNatAg/abrus_precatorius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62416,8 +62416,8 @@
   {
     "name": "iNatAg/abutilon_theophrasti",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62437,8 +62437,8 @@
   {
     "name": "iNatAg/acacia_abyssinica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62458,8 +62458,8 @@
   {
     "name": "iNatAg/acacia_acradenia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62479,8 +62479,8 @@
   {
     "name": "iNatAg/acacia_acuminata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62500,8 +62500,8 @@
   {
     "name": "iNatAg/acacia_ampliceps",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62521,8 +62521,8 @@
   {
     "name": "iNatAg/acacia_anceps",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62542,8 +62542,8 @@
   {
     "name": "iNatAg/acacia_ancistrocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62563,8 +62563,8 @@
   {
     "name": "iNatAg/acacia_aneura",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62584,8 +62584,8 @@
   {
     "name": "iNatAg/acacia_angustissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62605,8 +62605,8 @@
   {
     "name": "iNatAg/acacia_ataxacantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62626,8 +62626,8 @@
   {
     "name": "iNatAg/acacia_aulacocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62647,8 +62647,8 @@
   {
     "name": "iNatAg/acacia_auriculiformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62668,8 +62668,8 @@
   {
     "name": "iNatAg/acacia_bidwillii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62689,8 +62689,8 @@
   {
     "name": "iNatAg/acacia_brachystachya",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62710,8 +62710,8 @@
   {
     "name": "iNatAg/acacia_brevispica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62731,8 +62731,8 @@
   {
     "name": "iNatAg/acacia_burkei",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62752,8 +62752,8 @@
   {
     "name": "iNatAg/acacia_caffra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62773,8 +62773,8 @@
   {
     "name": "iNatAg/acacia_cambagei",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62794,8 +62794,8 @@
   {
     "name": "iNatAg/acacia_catechu",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62815,8 +62815,8 @@
   {
     "name": "iNatAg/acacia_catenulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62836,8 +62836,8 @@
   {
     "name": "iNatAg/acacia_caven",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62857,8 +62857,8 @@
   {
     "name": "iNatAg/acacia_cincinnata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62878,8 +62878,8 @@
   {
     "name": "iNatAg/acacia_coriacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62899,8 +62899,8 @@
   {
     "name": "iNatAg/acacia_cowleana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62920,8 +62920,8 @@
   {
     "name": "iNatAg/acacia_crassicarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62941,8 +62941,8 @@
   {
     "name": "iNatAg/acacia_cyclops",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62962,8 +62962,8 @@
   {
     "name": "iNatAg/acacia_cyperophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -62983,8 +62983,8 @@
   {
     "name": "iNatAg/acacia_dealbata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63004,8 +63004,8 @@
   {
     "name": "iNatAg/acacia_deanei",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63025,8 +63025,8 @@
   {
     "name": "iNatAg/acacia_decurrens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63046,8 +63046,8 @@
   {
     "name": "iNatAg/acacia_difficilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63067,8 +63067,8 @@
   {
     "name": "iNatAg/acacia_doratoxylon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63088,8 +63088,8 @@
   {
     "name": "iNatAg/acacia_ehrenbergiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63109,8 +63109,8 @@
   {
     "name": "iNatAg/acacia_erioloba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63130,8 +63130,8 @@
   {
     "name": "iNatAg/acacia_estrophiolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63151,8 +63151,8 @@
   {
     "name": "iNatAg/acacia_excelsa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63172,8 +63172,8 @@
   {
     "name": "iNatAg/acacia_falciformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63193,8 +63193,8 @@
   {
     "name": "iNatAg/acacia_farnesiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63214,8 +63214,8 @@
   {
     "name": "iNatAg/acacia_fasciculifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63235,8 +63235,8 @@
   {
     "name": "iNatAg/acacia_flavescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63256,8 +63256,8 @@
   {
     "name": "iNatAg/acacia_georginae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63277,8 +63277,8 @@
   {
     "name": "iNatAg/acacia_gerrardii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63298,8 +63298,8 @@
   {
     "name": "iNatAg/acacia_glaucocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63319,8 +63319,8 @@
   {
     "name": "iNatAg/acacia_gourmaensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63340,8 +63340,8 @@
   {
     "name": "iNatAg/acacia_harpophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63361,8 +63361,8 @@
   {
     "name": "iNatAg/acacia_holosericea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63382,8 +63382,8 @@
   {
     "name": "iNatAg/acacia_irrorata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63403,8 +63403,8 @@
   {
     "name": "iNatAg/acacia_ixiophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63424,8 +63424,8 @@
   {
     "name": "iNatAg/acacia_karroo",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63445,8 +63445,8 @@
   {
     "name": "iNatAg/acacia_koa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63466,8 +63466,8 @@
   {
     "name": "iNatAg/acacia_leptocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63487,8 +63487,8 @@
   {
     "name": "iNatAg/acacia_leucophloea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63508,8 +63508,8 @@
   {
     "name": "iNatAg/acacia_ligulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63529,8 +63529,8 @@
   {
     "name": "iNatAg/acacia_maidenii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63550,8 +63550,8 @@
   {
     "name": "iNatAg/acacia_mangium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63571,8 +63571,8 @@
   {
     "name": "iNatAg/acacia_mearnsii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63592,8 +63592,8 @@
   {
     "name": "iNatAg/acacia_melanoxylon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63613,8 +63613,8 @@
   {
     "name": "iNatAg/acacia_mellifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63634,8 +63634,8 @@
   {
     "name": "iNatAg/acacia_murrayana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63655,8 +63655,8 @@
   {
     "name": "iNatAg/acacia_neriifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63676,8 +63676,8 @@
   {
     "name": "iNatAg/acacia_nigrescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63697,8 +63697,8 @@
   {
     "name": "iNatAg/acacia_nilotica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63718,8 +63718,8 @@
   {
     "name": "iNatAg/acacia_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63739,8 +63739,8 @@
   {
     "name": "iNatAg/acacia_oraria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63760,8 +63760,8 @@
   {
     "name": "iNatAg/acacia_oswaldii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63781,8 +63781,8 @@
   {
     "name": "iNatAg/acacia_pachycarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63802,8 +63802,8 @@
   {
     "name": "iNatAg/acacia_papyrocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63823,8 +63823,8 @@
   {
     "name": "iNatAg/acacia_paradoxa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63844,8 +63844,8 @@
   {
     "name": "iNatAg/acacia_pendula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63865,8 +63865,8 @@
   {
     "name": "iNatAg/acacia_peuce",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63886,8 +63886,8 @@
   {
     "name": "iNatAg/acacia_podalyriifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63907,8 +63907,8 @@
   {
     "name": "iNatAg/acacia_polyacantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63928,8 +63928,8 @@
   {
     "name": "iNatAg/acacia_polystachya",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63949,8 +63949,8 @@
   {
     "name": "iNatAg/acacia_pruinocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63970,8 +63970,8 @@
   {
     "name": "iNatAg/acacia_pycnantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -63991,8 +63991,8 @@
   {
     "name": "iNatAg/acacia_salicina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64012,8 +64012,8 @@
   {
     "name": "iNatAg/acacia_saligna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64033,8 +64033,8 @@
   {
     "name": "iNatAg/acacia_sclerosperma",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64054,8 +64054,8 @@
   {
     "name": "iNatAg/acacia_senegal",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64075,8 +64075,8 @@
   {
     "name": "iNatAg/acacia_seyal",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64096,8 +64096,8 @@
   {
     "name": "iNatAg/acacia_shirleyi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64117,8 +64117,8 @@
   {
     "name": "iNatAg/acacia_sieberiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64138,8 +64138,8 @@
   {
     "name": "iNatAg/acacia_silvestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64159,8 +64159,8 @@
   {
     "name": "iNatAg/acacia_simsii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64180,8 +64180,8 @@
   {
     "name": "iNatAg/acacia_stenophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64201,8 +64201,8 @@
   {
     "name": "iNatAg/acacia_tetragonophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64222,8 +64222,8 @@
   {
     "name": "iNatAg/acacia_tortilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64243,8 +64243,8 @@
   {
     "name": "iNatAg/acacia_torulosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64264,8 +64264,8 @@
   {
     "name": "iNatAg/acacia_trachycarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64285,8 +64285,8 @@
   {
     "name": "iNatAg/acacia_victoriae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64306,8 +64306,8 @@
   {
     "name": "iNatAg/acaena_novae-zelandiae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64327,8 +64327,8 @@
   {
     "name": "iNatAg/acalypha_rhomboidea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64348,8 +64348,8 @@
   {
     "name": "iNatAg/acalypha_virginica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64369,8 +64369,8 @@
   {
     "name": "iNatAg/acanthosicyos_horridus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64390,8 +64390,8 @@
   {
     "name": "iNatAg/acanthosicyos_naudinianus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64411,8 +64411,8 @@
   {
     "name": "iNatAg/acanthospermum_hispidum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64432,8 +64432,8 @@
   {
     "name": "iNatAg/acanthus_ilicifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64453,8 +64453,8 @@
   {
     "name": "iNatAg/acanthus_mollis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64474,8 +64474,8 @@
   {
     "name": "iNatAg/acca_sellowiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64495,8 +64495,8 @@
   {
     "name": "iNatAg/acer_caesium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64516,8 +64516,8 @@
   {
     "name": "iNatAg/acer_campestre",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64537,8 +64537,8 @@
   {
     "name": "iNatAg/acer_platanoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64558,8 +64558,8 @@
   {
     "name": "iNatAg/acer_pseudoplatanus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64579,8 +64579,8 @@
   {
     "name": "iNatAg/acer_saccharum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64600,8 +64600,8 @@
   {
     "name": "iNatAg/achillea_fragrantissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64621,8 +64621,8 @@
   {
     "name": "iNatAg/achillea_millefolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64642,8 +64642,8 @@
   {
     "name": "iNatAg/achillea_ptarmica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64663,8 +64663,8 @@
   {
     "name": "iNatAg/achnatherum_pekinense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64684,8 +64684,8 @@
   {
     "name": "iNatAg/achyranthes_aspera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64705,8 +64705,8 @@
   {
     "name": "iNatAg/acmena_smithii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64726,8 +64726,8 @@
   {
     "name": "iNatAg/aconitum_napellus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64747,8 +64747,8 @@
   {
     "name": "iNatAg/acorus_calamus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64768,8 +64768,8 @@
   {
     "name": "iNatAg/acrocarpus_fraxinifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64789,8 +64789,8 @@
   {
     "name": "iNatAg/acrocomia_aculeata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64810,8 +64810,8 @@
   {
     "name": "iNatAg/acrocomia_totai",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64831,8 +64831,8 @@
   {
     "name": "iNatAg/actaea_racemosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64852,8 +64852,8 @@
   {
     "name": "iNatAg/actinidia_arguta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64873,8 +64873,8 @@
   {
     "name": "iNatAg/actinidia_chinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64894,8 +64894,8 @@
   {
     "name": "iNatAg/adansonia_digitata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64915,8 +64915,8 @@
   {
     "name": "iNatAg/adansonia_grandidieri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64936,8 +64936,8 @@
   {
     "name": "iNatAg/adansonia_gregorii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64957,8 +64957,8 @@
   {
     "name": "iNatAg/adenanthera_pavonina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64978,8 +64978,8 @@
   {
     "name": "iNatAg/adesmia_bicolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -64999,8 +64999,8 @@
   {
     "name": "iNatAg/adesmia_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65020,8 +65020,8 @@
   {
     "name": "iNatAg/adesmia_punctata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65041,8 +65041,8 @@
   {
     "name": "iNatAg/adesmia_securigerifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65062,8 +65062,8 @@
   {
     "name": "iNatAg/adiantum_capillus-veneris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65083,8 +65083,8 @@
   {
     "name": "iNatAg/adina_cordifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65104,8 +65104,8 @@
   {
     "name": "iNatAg/adonis_annua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65125,8 +65125,8 @@
   {
     "name": "iNatAg/adonis_vernalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65146,8 +65146,8 @@
   {
     "name": "iNatAg/aechmea_magdalenae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65167,8 +65167,8 @@
   {
     "name": "iNatAg/aegiceras_corniculatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65188,8 +65188,8 @@
   {
     "name": "iNatAg/aegilops_biuncialis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65209,8 +65209,8 @@
   {
     "name": "iNatAg/aegilops_cylindrica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65230,8 +65230,8 @@
   {
     "name": "iNatAg/aegilops_geniculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65251,8 +65251,8 @@
   {
     "name": "iNatAg/aegilops_triuncialis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65272,8 +65272,8 @@
   {
     "name": "iNatAg/aegle_marmelos",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65293,8 +65293,8 @@
   {
     "name": "iNatAg/aegopodium_podagraria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65314,8 +65314,8 @@
   {
     "name": "iNatAg/aeschynomene_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65335,8 +65335,8 @@
   {
     "name": "iNatAg/aeschynomene_brasiliana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65356,8 +65356,8 @@
   {
     "name": "iNatAg/aeschynomene_falcata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65377,8 +65377,8 @@
   {
     "name": "iNatAg/aeschynomene_histrix",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65398,8 +65398,8 @@
   {
     "name": "iNatAg/aeschynomene_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65419,8 +65419,8 @@
   {
     "name": "iNatAg/aeschynomene_villosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65440,8 +65440,8 @@
   {
     "name": "iNatAg/aesculus_hippocastanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65461,8 +65461,8 @@
   {
     "name": "iNatAg/aesculus_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65482,8 +65482,8 @@
   {
     "name": "iNatAg/aethusa_cynapium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65503,8 +65503,8 @@
   {
     "name": "iNatAg/afzelia_africana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65524,8 +65524,8 @@
   {
     "name": "iNatAg/afzelia_quanzensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65545,8 +65545,8 @@
   {
     "name": "iNatAg/agathis_australis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65566,8 +65566,8 @@
   {
     "name": "iNatAg/agathis_dammara",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65587,8 +65587,8 @@
   {
     "name": "iNatAg/agathis_macrophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65608,8 +65608,8 @@
   {
     "name": "iNatAg/agathis_microstachya",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65629,8 +65629,8 @@
   {
     "name": "iNatAg/agathis_robusta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65650,8 +65650,8 @@
   {
     "name": "iNatAg/agave_fourcroydes",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65671,8 +65671,8 @@
   {
     "name": "iNatAg/agave_lecheguilla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65692,8 +65692,8 @@
   {
     "name": "iNatAg/agave_sisalana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65713,8 +65713,8 @@
   {
     "name": "iNatAg/ageratum_conyzoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65734,8 +65734,8 @@
   {
     "name": "iNatAg/agrimonia_eupatoria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65755,8 +65755,8 @@
   {
     "name": "iNatAg/agrimonia_gryposepala",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65776,8 +65776,8 @@
   {
     "name": "iNatAg/agrimonia_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65797,8 +65797,8 @@
   {
     "name": "iNatAg/agropyron_cristatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65818,8 +65818,8 @@
   {
     "name": "iNatAg/agropyron_dasyanthum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65839,8 +65839,8 @@
   {
     "name": "iNatAg/agropyron_desertorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65860,8 +65860,8 @@
   {
     "name": "iNatAg/agropyron_scabrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65881,8 +65881,8 @@
   {
     "name": "iNatAg/agrostemma_githago",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65902,8 +65902,8 @@
   {
     "name": "iNatAg/agrostis_canina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65923,8 +65923,8 @@
   {
     "name": "iNatAg/agrostis_capillaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65944,8 +65944,8 @@
   {
     "name": "iNatAg/agrostis_gigantea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65965,8 +65965,8 @@
   {
     "name": "iNatAg/agrostis_stolonifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -65986,8 +65986,8 @@
   {
     "name": "iNatAg/agrostis_tenuis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66007,8 +66007,8 @@
   {
     "name": "iNatAg/ailanthus_altissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66028,8 +66028,8 @@
   {
     "name": "iNatAg/ailanthus_excelsa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66049,8 +66049,8 @@
   {
     "name": "iNatAg/aiphanes_aculeata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66070,8 +66070,8 @@
   {
     "name": "iNatAg/aira_caryophyllea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66091,8 +66091,8 @@
   {
     "name": "iNatAg/ajuga_genevensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66112,8 +66112,8 @@
   {
     "name": "iNatAg/ajuga_reptans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66133,8 +66133,8 @@
   {
     "name": "iNatAg/alania_cunninghamii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66154,8 +66154,8 @@
   {
     "name": "iNatAg/albizia_adianthifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66175,8 +66175,8 @@
   {
     "name": "iNatAg/albizia_amara",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66196,8 +66196,8 @@
   {
     "name": "iNatAg/albizia_chinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66217,8 +66217,8 @@
   {
     "name": "iNatAg/albizia_falcataria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66238,8 +66238,8 @@
   {
     "name": "iNatAg/albizia_harveyi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66259,8 +66259,8 @@
   {
     "name": "iNatAg/albizia_lebbeck",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66280,8 +66280,8 @@
   {
     "name": "iNatAg/albizia_lophantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66301,8 +66301,8 @@
   {
     "name": "iNatAg/albizia_lucida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66322,8 +66322,8 @@
   {
     "name": "iNatAg/albizia_odoratissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66343,8 +66343,8 @@
   {
     "name": "iNatAg/albizia_procera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66364,8 +66364,8 @@
   {
     "name": "iNatAg/alcea_rosea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66385,8 +66385,8 @@
   {
     "name": "iNatAg/alchemilla_monticola",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66406,8 +66406,8 @@
   {
     "name": "iNatAg/alchemilla_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66427,8 +66427,8 @@
   {
     "name": "iNatAg/alchemilla_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66448,8 +66448,8 @@
   {
     "name": "iNatAg/alchemilla_xanthochlora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66469,8 +66469,8 @@
   {
     "name": "iNatAg/aleurites_fordii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66490,8 +66490,8 @@
   {
     "name": "iNatAg/aleurites_moluccana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66511,8 +66511,8 @@
   {
     "name": "iNatAg/alisma_gramineum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66532,8 +66532,8 @@
   {
     "name": "iNatAg/alisma_lanceolatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66553,8 +66553,8 @@
   {
     "name": "iNatAg/alisma_plantago-aquatica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66574,8 +66574,8 @@
   {
     "name": "iNatAg/alkanna_tinctoria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66595,8 +66595,8 @@
   {
     "name": "iNatAg/alliaria_petiolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66616,8 +66616,8 @@
   {
     "name": "iNatAg/allionia_incarnata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66637,8 +66637,8 @@
   {
     "name": "iNatAg/allium_ampeloprasum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66658,8 +66658,8 @@
   {
     "name": "iNatAg/allium_canadense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66679,8 +66679,8 @@
   {
     "name": "iNatAg/allium_cepa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66700,8 +66700,8 @@
   {
     "name": "iNatAg/allium_chinense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66721,8 +66721,8 @@
   {
     "name": "iNatAg/allium_fistulosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66742,8 +66742,8 @@
   {
     "name": "iNatAg/allium_paniculatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66763,8 +66763,8 @@
   {
     "name": "iNatAg/allium_sativum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66784,8 +66784,8 @@
   {
     "name": "iNatAg/allium_schoenoprasum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66805,8 +66805,8 @@
   {
     "name": "iNatAg/allium_triquetrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66826,8 +66826,8 @@
   {
     "name": "iNatAg/allium_tuberosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66847,8 +66847,8 @@
   {
     "name": "iNatAg/allium_ursinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66868,8 +66868,8 @@
   {
     "name": "iNatAg/allocasuarina_campestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66889,8 +66889,8 @@
   {
     "name": "iNatAg/allocasuarina_decaisneana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66910,8 +66910,8 @@
   {
     "name": "iNatAg/allocasuarina_fraseriana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66931,8 +66931,8 @@
   {
     "name": "iNatAg/allocasuarina_huegeliana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66952,8 +66952,8 @@
   {
     "name": "iNatAg/allocasuarina_littoralis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66973,8 +66973,8 @@
   {
     "name": "iNatAg/allocasuarina_luehmannii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -66994,8 +66994,8 @@
   {
     "name": "iNatAg/allocasuarina_torulosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67015,8 +67015,8 @@
   {
     "name": "iNatAg/alloteropsis_semialata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67036,8 +67036,8 @@
   {
     "name": "iNatAg/alnus_acuminata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67057,8 +67057,8 @@
   {
     "name": "iNatAg/alnus_glutinosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67078,8 +67078,8 @@
   {
     "name": "iNatAg/alnus_japonica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67099,8 +67099,8 @@
   {
     "name": "iNatAg/alnus_maritima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67120,8 +67120,8 @@
   {
     "name": "iNatAg/alnus_nepalensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67141,8 +67141,8 @@
   {
     "name": "iNatAg/alnus_rubra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67162,8 +67162,8 @@
   {
     "name": "iNatAg/alocasia_macrorrhizos",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67183,8 +67183,8 @@
   {
     "name": "iNatAg/aloe_arborescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67204,8 +67204,8 @@
   {
     "name": "iNatAg/aloe_barbadensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67225,8 +67225,8 @@
   {
     "name": "iNatAg/aloe_ferox",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67246,8 +67246,8 @@
   {
     "name": "iNatAg/aloe_perryi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67267,8 +67267,8 @@
   {
     "name": "iNatAg/alopecurus_arundinaceus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67288,8 +67288,8 @@
   {
     "name": "iNatAg/alopecurus_carolinianus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67309,8 +67309,8 @@
   {
     "name": "iNatAg/alopecurus_geniculatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67330,8 +67330,8 @@
   {
     "name": "iNatAg/alopecurus_myosuroides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67351,8 +67351,8 @@
   {
     "name": "iNatAg/alopecurus_pratensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67372,8 +67372,8 @@
   {
     "name": "iNatAg/alopecurus_rendlei",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67393,8 +67393,8 @@
   {
     "name": "iNatAg/aloysia_triphylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67414,8 +67414,8 @@
   {
     "name": "iNatAg/alphitonia_excelsa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67435,8 +67435,8 @@
   {
     "name": "iNatAg/alpinia_galanga",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67456,8 +67456,8 @@
   {
     "name": "iNatAg/alstonia_scholaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67477,8 +67477,8 @@
   {
     "name": "iNatAg/alternanthera_pungens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67498,8 +67498,8 @@
   {
     "name": "iNatAg/althaea_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67519,8 +67519,8 @@
   {
     "name": "iNatAg/altingia_excelsa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67540,8 +67540,8 @@
   {
     "name": "iNatAg/alysicarpus_monilifer",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67561,8 +67561,8 @@
   {
     "name": "iNatAg/alysicarpus_ovalifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67582,8 +67582,8 @@
   {
     "name": "iNatAg/alysicarpus_rugosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67603,8 +67603,8 @@
   {
     "name": "iNatAg/alysicarpus_vaginalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67624,8 +67624,8 @@
   {
     "name": "iNatAg/alyssum_desertorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67645,8 +67645,8 @@
   {
     "name": "iNatAg/amaranthus_albus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67666,8 +67666,8 @@
   {
     "name": "iNatAg/amaranthus_blitum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67687,8 +67687,8 @@
   {
     "name": "iNatAg/amaranthus_caudatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67708,8 +67708,8 @@
   {
     "name": "iNatAg/amaranthus_cruentus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67729,8 +67729,8 @@
   {
     "name": "iNatAg/amaranthus_dubius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67750,8 +67750,8 @@
   {
     "name": "iNatAg/amaranthus_hybridus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67771,8 +67771,8 @@
   {
     "name": "iNatAg/amaranthus_hypochondriacus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67792,8 +67792,8 @@
   {
     "name": "iNatAg/amaranthus_lividus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67813,8 +67813,8 @@
   {
     "name": "iNatAg/amaranthus_retroflexus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67834,8 +67834,8 @@
   {
     "name": "iNatAg/amaranthus_speciosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67855,8 +67855,8 @@
   {
     "name": "iNatAg/amaranthus_spinosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67876,8 +67876,8 @@
   {
     "name": "iNatAg/amaranthus_tricolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67897,8 +67897,8 @@
   {
     "name": "iNatAg/amaranthus_viridis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67918,8 +67918,8 @@
   {
     "name": "iNatAg/ambelania_acida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67939,8 +67939,8 @@
   {
     "name": "iNatAg/ambrosia_acanthicarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67960,8 +67960,8 @@
   {
     "name": "iNatAg/ambrosia_artemisiifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -67981,8 +67981,8 @@
   {
     "name": "iNatAg/ambrosia_confertiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68002,8 +68002,8 @@
   {
     "name": "iNatAg/ambrosia_psilostachya",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68023,8 +68023,8 @@
   {
     "name": "iNatAg/ambrosia_tomentosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68044,8 +68044,8 @@
   {
     "name": "iNatAg/ambrosia_trifida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68065,8 +68065,8 @@
   {
     "name": "iNatAg/ammannia_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68086,8 +68086,8 @@
   {
     "name": "iNatAg/ammi_majus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68107,8 +68107,8 @@
   {
     "name": "iNatAg/ammophila_arenaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68128,8 +68128,8 @@
   {
     "name": "iNatAg/ammophila_breviligulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68149,8 +68149,8 @@
   {
     "name": "iNatAg/amorpha_fruticosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68170,8 +68170,8 @@
   {
     "name": "iNatAg/amorphophallus_paeoniifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68191,8 +68191,8 @@
   {
     "name": "iNatAg/amsinckia_douglasiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68212,8 +68212,8 @@
   {
     "name": "iNatAg/amsinckia_lycopsoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68233,8 +68233,8 @@
   {
     "name": "iNatAg/anacardium_occidentale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68254,8 +68254,8 @@
   {
     "name": "iNatAg/anagallis_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68275,8 +68275,8 @@
   {
     "name": "iNatAg/ananas_comosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68296,8 +68296,8 @@
   {
     "name": "iNatAg/anchusa_azurea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68317,8 +68317,8 @@
   {
     "name": "iNatAg/andrographis_paniculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68338,8 +68338,8 @@
   {
     "name": "iNatAg/andropogon_barbinodis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68359,8 +68359,8 @@
   {
     "name": "iNatAg/andropogon_bicornis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68380,8 +68380,8 @@
   {
     "name": "iNatAg/andropogon_brachystachyus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68401,8 +68401,8 @@
   {
     "name": "iNatAg/andropogon_gayanus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68422,8 +68422,8 @@
   {
     "name": "iNatAg/andropogon_gyrans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68443,8 +68443,8 @@
   {
     "name": "iNatAg/andropogon_hallii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68464,8 +68464,8 @@
   {
     "name": "iNatAg/andropogon_leucostachyus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68485,8 +68485,8 @@
   {
     "name": "iNatAg/andropogon_ternarius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68506,8 +68506,8 @@
   {
     "name": "iNatAg/androsace_septentrionalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68527,8 +68527,8 @@
   {
     "name": "iNatAg/anemone_hepatica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68548,8 +68548,8 @@
   {
     "name": "iNatAg/anemone_nemorosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68569,8 +68569,8 @@
   {
     "name": "iNatAg/anethum_graveolens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68590,8 +68590,8 @@
   {
     "name": "iNatAg/angelica_archangelica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68611,8 +68611,8 @@
   {
     "name": "iNatAg/angelica_atropurpurea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68632,8 +68632,8 @@
   {
     "name": "iNatAg/angelica_sylvestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68653,8 +68653,8 @@
   {
     "name": "iNatAg/angophora_costata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68674,8 +68674,8 @@
   {
     "name": "iNatAg/angophora_floribunda",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68695,8 +68695,8 @@
   {
     "name": "iNatAg/annona_atemoya",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68716,8 +68716,8 @@
   {
     "name": "iNatAg/annona_cherimola",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68737,8 +68737,8 @@
   {
     "name": "iNatAg/annona_diversifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68758,8 +68758,8 @@
   {
     "name": "iNatAg/annona_montana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68779,8 +68779,8 @@
   {
     "name": "iNatAg/annona_muricata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68800,8 +68800,8 @@
   {
     "name": "iNatAg/annona_purpurea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68821,8 +68821,8 @@
   {
     "name": "iNatAg/annona_reticulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68842,8 +68842,8 @@
   {
     "name": "iNatAg/annona_senegalensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68863,8 +68863,8 @@
   {
     "name": "iNatAg/annona_squamosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68884,8 +68884,8 @@
   {
     "name": "iNatAg/anogeissus_acuminata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68905,8 +68905,8 @@
   {
     "name": "iNatAg/anogeissus_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68926,8 +68926,8 @@
   {
     "name": "iNatAg/anogeissus_pendula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68947,8 +68947,8 @@
   {
     "name": "iNatAg/antennaria_dioica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68968,8 +68968,8 @@
   {
     "name": "iNatAg/anthemis_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -68989,8 +68989,8 @@
   {
     "name": "iNatAg/anthemis_cotula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69010,8 +69010,8 @@
   {
     "name": "iNatAg/anthemis_tinctoria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69031,8 +69031,8 @@
   {
     "name": "iNatAg/anthephora_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69052,8 +69052,8 @@
   {
     "name": "iNatAg/anthoxanthum_odoratum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69073,8 +69073,8 @@
   {
     "name": "iNatAg/anthriscus_cerefolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69094,8 +69094,8 @@
   {
     "name": "iNatAg/anthyllis_vulneraria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69115,8 +69115,8 @@
   {
     "name": "iNatAg/antidesma_bunius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69136,8 +69136,8 @@
   {
     "name": "iNatAg/antirrhinum_majus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69157,8 +69157,8 @@
   {
     "name": "iNatAg/aphandra_natalia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69178,8 +69178,8 @@
   {
     "name": "iNatAg/aphanes_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69199,8 +69199,8 @@
   {
     "name": "iNatAg/apios_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69220,8 +69220,8 @@
   {
     "name": "iNatAg/apium_graveolens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69241,8 +69241,8 @@
   {
     "name": "iNatAg/apocynum_cannabinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69262,8 +69262,8 @@
   {
     "name": "iNatAg/apocynum_sibiricum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69283,8 +69283,8 @@
   {
     "name": "iNatAg/aponogeton_distachyos",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69304,8 +69304,8 @@
   {
     "name": "iNatAg/aquilaria_malaccensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69325,8 +69325,8 @@
   {
     "name": "iNatAg/aquilegia_canadensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69346,8 +69346,8 @@
   {
     "name": "iNatAg/aquilegia_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69367,8 +69367,8 @@
   {
     "name": "iNatAg/arachis_glabrata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69388,8 +69388,8 @@
   {
     "name": "iNatAg/arachis_hypogaea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69409,8 +69409,8 @@
   {
     "name": "iNatAg/arachis_pintoi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69430,8 +69430,8 @@
   {
     "name": "iNatAg/arachis_villosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69451,8 +69451,8 @@
   {
     "name": "iNatAg/araucaria_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69472,8 +69472,8 @@
   {
     "name": "iNatAg/araucaria_bidwillii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69493,8 +69493,8 @@
   {
     "name": "iNatAg/araucaria_cunninghamii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69514,8 +69514,8 @@
   {
     "name": "iNatAg/araucaria_hunsteinii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69535,8 +69535,8 @@
   {
     "name": "iNatAg/arbutus_unedo",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69556,8 +69556,8 @@
   {
     "name": "iNatAg/archidendron_jiringa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69577,8 +69577,8 @@
   {
     "name": "iNatAg/arctium_lappa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69598,8 +69598,8 @@
   {
     "name": "iNatAg/arctostaphylos_glandulosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69619,8 +69619,8 @@
   {
     "name": "iNatAg/arctostaphylos_manzanita",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69640,8 +69640,8 @@
   {
     "name": "iNatAg/arctostaphylos_patula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69661,8 +69661,8 @@
   {
     "name": "iNatAg/arctostaphylos_uva-ursi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69682,8 +69682,8 @@
   {
     "name": "iNatAg/arctostaphylos_viscida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69703,8 +69703,8 @@
   {
     "name": "iNatAg/ardisia_crenata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69724,8 +69724,8 @@
   {
     "name": "iNatAg/areca_catechu",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69745,8 +69745,8 @@
   {
     "name": "iNatAg/arenaria_serpyllifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69766,8 +69766,8 @@
   {
     "name": "iNatAg/arenga_pinnata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69787,8 +69787,8 @@
   {
     "name": "iNatAg/argemone_mexicana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69808,8 +69808,8 @@
   {
     "name": "iNatAg/argyrodendron_actinophyllum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69829,8 +69829,8 @@
   {
     "name": "iNatAg/argyrodendron_peralatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69850,8 +69850,8 @@
   {
     "name": "iNatAg/aria_alnifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69871,8 +69871,8 @@
   {
     "name": "iNatAg/aristida_adscensionis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69892,8 +69892,8 @@
   {
     "name": "iNatAg/aristida_behriana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69913,8 +69913,8 @@
   {
     "name": "iNatAg/aristida_congesta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69934,8 +69934,8 @@
   {
     "name": "iNatAg/aristida_junciformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69955,8 +69955,8 @@
   {
     "name": "iNatAg/aristida_lanosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69976,8 +69976,8 @@
   {
     "name": "iNatAg/aristida_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -69997,8 +69997,8 @@
   {
     "name": "iNatAg/aristida_longispica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70018,8 +70018,8 @@
   {
     "name": "iNatAg/aristida_personata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70039,8 +70039,8 @@
   {
     "name": "iNatAg/aristida_purpurascens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70060,8 +70060,8 @@
   {
     "name": "iNatAg/aristida_schiedeana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70081,8 +70081,8 @@
   {
     "name": "iNatAg/aristida_transvaalensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70102,8 +70102,8 @@
   {
     "name": "iNatAg/aristolochia_rotunda",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70123,8 +70123,8 @@
   {
     "name": "iNatAg/armoracia_rusticana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70144,8 +70144,8 @@
   {
     "name": "iNatAg/arnica_montana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70165,8 +70165,8 @@
   {
     "name": "iNatAg/arrhenatherum_elatius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70186,8 +70186,8 @@
   {
     "name": "iNatAg/artemisia_abrotanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70207,8 +70207,8 @@
   {
     "name": "iNatAg/artemisia_absinthium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70228,8 +70228,8 @@
   {
     "name": "iNatAg/artemisia_afra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70249,8 +70249,8 @@
   {
     "name": "iNatAg/artemisia_annua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70270,8 +70270,8 @@
   {
     "name": "iNatAg/artemisia_campestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70291,8 +70291,8 @@
   {
     "name": "iNatAg/artemisia_dracunculus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70312,8 +70312,8 @@
   {
     "name": "iNatAg/artemisia_filifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70333,8 +70333,8 @@
   {
     "name": "iNatAg/artemisia_glacialis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70354,8 +70354,8 @@
   {
     "name": "iNatAg/artemisia_herba-alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70375,8 +70375,8 @@
   {
     "name": "iNatAg/artemisia_ludoviciana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70396,8 +70396,8 @@
   {
     "name": "iNatAg/artemisia_stelleriana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70417,8 +70417,8 @@
   {
     "name": "iNatAg/artemisia_tridentata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70438,8 +70438,8 @@
   {
     "name": "iNatAg/artemisia_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70459,8 +70459,8 @@
   {
     "name": "iNatAg/artocarpus_altilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70480,8 +70480,8 @@
   {
     "name": "iNatAg/artocarpus_heterophyllus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70501,8 +70501,8 @@
   {
     "name": "iNatAg/artocarpus_hirsutus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70522,8 +70522,8 @@
   {
     "name": "iNatAg/artocarpus_integer",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70543,8 +70543,8 @@
   {
     "name": "iNatAg/artocarpus_lakoocha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70564,8 +70564,8 @@
   {
     "name": "iNatAg/arundinella_hirta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70585,8 +70585,8 @@
   {
     "name": "iNatAg/arundo_donax",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70606,8 +70606,8 @@
   {
     "name": "iNatAg/asarina_stricta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70627,8 +70627,8 @@
   {
     "name": "iNatAg/asarum_europaeum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70648,8 +70648,8 @@
   {
     "name": "iNatAg/asclepias_curassavica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70669,8 +70669,8 @@
   {
     "name": "iNatAg/asclepias_fascicularis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70690,8 +70690,8 @@
   {
     "name": "iNatAg/asclepias_incarnata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70711,8 +70711,8 @@
   {
     "name": "iNatAg/asclepias_lanceolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70732,8 +70732,8 @@
   {
     "name": "iNatAg/asclepias_purpurascens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70753,8 +70753,8 @@
   {
     "name": "iNatAg/asclepias_speciosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70774,8 +70774,8 @@
   {
     "name": "iNatAg/asclepias_subverticillata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70795,8 +70795,8 @@
   {
     "name": "iNatAg/asclepias_tuberosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70816,8 +70816,8 @@
   {
     "name": "iNatAg/asclepias_verticillata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70837,8 +70837,8 @@
   {
     "name": "iNatAg/asclepias_viridiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70858,8 +70858,8 @@
   {
     "name": "iNatAg/asimina_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70879,8 +70879,8 @@
   {
     "name": "iNatAg/asimina_triloba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70900,8 +70900,8 @@
   {
     "name": "iNatAg/asparagus_densiflorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70921,8 +70921,8 @@
   {
     "name": "iNatAg/asparagus_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70942,8 +70942,8 @@
   {
     "name": "iNatAg/asperula_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70963,8 +70963,8 @@
   {
     "name": "iNatAg/asphodelus_albus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -70984,8 +70984,8 @@
   {
     "name": "iNatAg/asphodelus_tenuifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71005,8 +71005,8 @@
   {
     "name": "iNatAg/aspilia_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71026,8 +71026,8 @@
   {
     "name": "iNatAg/asplenium_ruta-muraria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71047,8 +71047,8 @@
   {
     "name": "iNatAg/aster_ericoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71068,8 +71068,8 @@
   {
     "name": "iNatAg/astragalus_adsurgens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71089,8 +71089,8 @@
   {
     "name": "iNatAg/astragalus_asymmetricus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71110,8 +71110,8 @@
   {
     "name": "iNatAg/astragalus_canadensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71131,8 +71131,8 @@
   {
     "name": "iNatAg/astragalus_cicer",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71152,8 +71152,8 @@
   {
     "name": "iNatAg/astragalus_gummifer",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71173,8 +71173,8 @@
   {
     "name": "iNatAg/astragalus_mollissimus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71194,8 +71194,8 @@
   {
     "name": "iNatAg/astragalus_nuttallianus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71215,8 +71215,8 @@
   {
     "name": "iNatAg/astragalus_sinicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71236,8 +71236,8 @@
   {
     "name": "iNatAg/astragalus_tweedyi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71257,8 +71257,8 @@
   {
     "name": "iNatAg/astrantia_major",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71278,8 +71278,8 @@
   {
     "name": "iNatAg/astrebla_lappacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71299,8 +71299,8 @@
   {
     "name": "iNatAg/astrebla_pectinata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71320,8 +71320,8 @@
   {
     "name": "iNatAg/astrebla_squarrosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71341,8 +71341,8 @@
   {
     "name": "iNatAg/astrocaryum_jauari",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71362,8 +71362,8 @@
   {
     "name": "iNatAg/astrocaryum_vulgare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71383,8 +71383,8 @@
   {
     "name": "iNatAg/asystasia_gangetica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71404,8 +71404,8 @@
   {
     "name": "iNatAg/atalaya_hemiglauca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71425,8 +71425,8 @@
   {
     "name": "iNatAg/atherosperma_moschatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71446,8 +71446,8 @@
   {
     "name": "iNatAg/athrotaxis_selaginoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71467,8 +71467,8 @@
   {
     "name": "iNatAg/atriplex_canescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71488,8 +71488,8 @@
   {
     "name": "iNatAg/atriplex_confertifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71509,8 +71509,8 @@
   {
     "name": "iNatAg/atriplex_gardneri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71530,8 +71530,8 @@
   {
     "name": "iNatAg/atriplex_glauca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71551,8 +71551,8 @@
   {
     "name": "iNatAg/atriplex_halimus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71572,8 +71572,8 @@
   {
     "name": "iNatAg/atriplex_hortensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71593,8 +71593,8 @@
   {
     "name": "iNatAg/atriplex_lentiformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71614,8 +71614,8 @@
   {
     "name": "iNatAg/atriplex_nummularia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71635,8 +71635,8 @@
   {
     "name": "iNatAg/atriplex_patula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71656,8 +71656,8 @@
   {
     "name": "iNatAg/atriplex_rosea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71677,8 +71677,8 @@
   {
     "name": "iNatAg/atriplex_semibaccata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71698,8 +71698,8 @@
   {
     "name": "iNatAg/atriplex_vesicaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71719,8 +71719,8 @@
   {
     "name": "iNatAg/atropa_belladonna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71740,8 +71740,8 @@
   {
     "name": "iNatAg/attalea_cohune",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71761,8 +71761,8 @@
   {
     "name": "iNatAg/avena_fatua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71782,8 +71782,8 @@
   {
     "name": "iNatAg/avena_sativa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71803,8 +71803,8 @@
   {
     "name": "iNatAg/avena_sterilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71824,8 +71824,8 @@
   {
     "name": "iNatAg/avenula_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71845,8 +71845,8 @@
   {
     "name": "iNatAg/averrhoa_bilimbi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71866,8 +71866,8 @@
   {
     "name": "iNatAg/averrhoa_carambola",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71887,8 +71887,8 @@
   {
     "name": "iNatAg/avicennia_germinans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71908,8 +71908,8 @@
   {
     "name": "iNatAg/avicennia_marina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71929,8 +71929,8 @@
   {
     "name": "iNatAg/avicennia_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71950,8 +71950,8 @@
   {
     "name": "iNatAg/axonopus_affinis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71971,8 +71971,8 @@
   {
     "name": "iNatAg/axonopus_compressus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -71992,8 +71992,8 @@
   {
     "name": "iNatAg/axonopus_fissifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72013,8 +72013,8 @@
   {
     "name": "iNatAg/axyris_amaranthoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72034,8 +72034,8 @@
   {
     "name": "iNatAg/azadirachta_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72055,8 +72055,8 @@
   {
     "name": "iNatAg/azanza_garckeana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72076,8 +72076,8 @@
   {
     "name": "iNatAg/azolla_filiculoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72097,8 +72097,8 @@
   {
     "name": "iNatAg/azolla_pinnata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72118,8 +72118,8 @@
   {
     "name": "iNatAg/baccaurea_motleyana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72139,8 +72139,8 @@
   {
     "name": "iNatAg/baccaurea_ramiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72160,8 +72160,8 @@
   {
     "name": "iNatAg/baccharis_glutinosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72181,8 +72181,8 @@
   {
     "name": "iNatAg/baccharis_pilularis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72202,8 +72202,8 @@
   {
     "name": "iNatAg/bactris_gasipaes",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72223,8 +72223,8 @@
   {
     "name": "iNatAg/baikiaea_plurijuga",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72244,8 +72244,8 @@
   {
     "name": "iNatAg/balanites_aegyptiaca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72265,8 +72265,8 @@
   {
     "name": "iNatAg/bambusa_arundinacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72286,8 +72286,8 @@
   {
     "name": "iNatAg/bambusa_balcooa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72307,8 +72307,8 @@
   {
     "name": "iNatAg/bambusa_blumeana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72328,8 +72328,8 @@
   {
     "name": "iNatAg/bambusa_tulda",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72349,8 +72349,8 @@
   {
     "name": "iNatAg/bambusa_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72370,8 +72370,8 @@
   {
     "name": "iNatAg/banksia_integrifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72391,8 +72391,8 @@
   {
     "name": "iNatAg/banksia_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72412,8 +72412,8 @@
   {
     "name": "iNatAg/baphia_nitida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72433,8 +72433,8 @@
   {
     "name": "iNatAg/barringtonia_racemosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72454,8 +72454,8 @@
   {
     "name": "iNatAg/basella_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72475,8 +72475,8 @@
   {
     "name": "iNatAg/bauhinia_aculeata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72496,8 +72496,8 @@
   {
     "name": "iNatAg/bauhinia_petersiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72517,8 +72517,8 @@
   {
     "name": "iNatAg/bauhinia_racemosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72538,8 +72538,8 @@
   {
     "name": "iNatAg/bauhinia_rufescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72559,8 +72559,8 @@
   {
     "name": "iNatAg/bauhinia_thonningii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72580,8 +72580,8 @@
   {
     "name": "iNatAg/bauhinia_tomentosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72601,8 +72601,8 @@
   {
     "name": "iNatAg/bauhinia_variegata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72622,8 +72622,8 @@
   {
     "name": "iNatAg/beckmannia_eruciformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72643,8 +72643,8 @@
   {
     "name": "iNatAg/beckmannia_syzigachne",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72664,8 +72664,8 @@
   {
     "name": "iNatAg/bellis_perennis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72685,8 +72685,8 @@
   {
     "name": "iNatAg/benincasa_hispida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72706,8 +72706,8 @@
   {
     "name": "iNatAg/berberis_aquifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72727,8 +72727,8 @@
   {
     "name": "iNatAg/berberis_thunbergii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72748,8 +72748,8 @@
   {
     "name": "iNatAg/berberis_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72769,8 +72769,8 @@
   {
     "name": "iNatAg/berchemia_discolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72790,8 +72790,8 @@
   {
     "name": "iNatAg/berrya_cordifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72811,8 +72811,8 @@
   {
     "name": "iNatAg/bersama_lucens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72832,8 +72832,8 @@
   {
     "name": "iNatAg/bertholletia_excelsa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72853,8 +72853,8 @@
   {
     "name": "iNatAg/beta_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72874,8 +72874,8 @@
   {
     "name": "iNatAg/betula_nigra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72895,8 +72895,8 @@
   {
     "name": "iNatAg/betula_pendula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72916,8 +72916,8 @@
   {
     "name": "iNatAg/betula_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72937,8 +72937,8 @@
   {
     "name": "iNatAg/bidens_bipinnata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72958,8 +72958,8 @@
   {
     "name": "iNatAg/bidens_cernua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -72979,8 +72979,8 @@
   {
     "name": "iNatAg/bidens_frondosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73000,8 +73000,8 @@
   {
     "name": "iNatAg/bidens_pilosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73021,8 +73021,8 @@
   {
     "name": "iNatAg/bidens_tripartita",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73042,8 +73042,8 @@
   {
     "name": "iNatAg/bignonia_capreolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73063,8 +73063,8 @@
   {
     "name": "iNatAg/biserrula_pelecinus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73084,8 +73084,8 @@
   {
     "name": "iNatAg/bixa_orellana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73105,8 +73105,8 @@
   {
     "name": "iNatAg/blighia_sapida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73126,8 +73126,8 @@
   {
     "name": "iNatAg/blumea_balsamifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73147,8 +73147,8 @@
   {
     "name": "iNatAg/bocconia_frutescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73168,8 +73168,8 @@
   {
     "name": "iNatAg/boehmeria_nivea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73189,8 +73189,8 @@
   {
     "name": "iNatAg/boerhavia_coccinea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73210,8 +73210,8 @@
   {
     "name": "iNatAg/boerhavia_diffusa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73231,8 +73231,8 @@
   {
     "name": "iNatAg/boerhavia_erecta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73252,8 +73252,8 @@
   {
     "name": "iNatAg/boesenbergia_rotunda",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73273,8 +73273,8 @@
   {
     "name": "iNatAg/bolusanthus_speciosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73294,8 +73294,8 @@
   {
     "name": "iNatAg/bombacopsis_quinata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73315,8 +73315,8 @@
   {
     "name": "iNatAg/bombax_ceiba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73336,8 +73336,8 @@
   {
     "name": "iNatAg/bombax_insigne",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73357,8 +73357,8 @@
   {
     "name": "iNatAg/borago_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73378,8 +73378,8 @@
   {
     "name": "iNatAg/borassus_aethiopum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73399,8 +73399,8 @@
   {
     "name": "iNatAg/borassus_flabellifer",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73420,8 +73420,8 @@
   {
     "name": "iNatAg/borojoa_patinoi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73441,8 +73441,8 @@
   {
     "name": "iNatAg/boronia_glabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73462,8 +73462,8 @@
   {
     "name": "iNatAg/boscia_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73483,8 +73483,8 @@
   {
     "name": "iNatAg/boswellia_serrata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73504,8 +73504,8 @@
   {
     "name": "iNatAg/bothriochloa_bladhii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73525,8 +73525,8 @@
   {
     "name": "iNatAg/bothriochloa_insculpta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73546,8 +73546,8 @@
   {
     "name": "iNatAg/bothriochloa_ischaemum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73567,8 +73567,8 @@
   {
     "name": "iNatAg/bothriochloa_pertusa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73588,8 +73588,8 @@
   {
     "name": "iNatAg/bougainvillea_glabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73609,8 +73609,8 @@
   {
     "name": "iNatAg/bouteloua_curtipendula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73630,8 +73630,8 @@
   {
     "name": "iNatAg/bouteloua_gracilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73651,8 +73651,8 @@
   {
     "name": "iNatAg/brachiaria_brizantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73672,8 +73672,8 @@
   {
     "name": "iNatAg/brachiaria_decumbens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73693,8 +73693,8 @@
   {
     "name": "iNatAg/brachiaria_deflexa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73714,8 +73714,8 @@
   {
     "name": "iNatAg/brachiaria_distachya",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73735,8 +73735,8 @@
   {
     "name": "iNatAg/brachiaria_humidicola",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73756,8 +73756,8 @@
   {
     "name": "iNatAg/brachiaria_mutica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73777,8 +73777,8 @@
   {
     "name": "iNatAg/brachiaria_ramosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73798,8 +73798,8 @@
   {
     "name": "iNatAg/brachiaria_serrata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73819,8 +73819,8 @@
   {
     "name": "iNatAg/brachychiton_acerifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73840,8 +73840,8 @@
   {
     "name": "iNatAg/brachychiton_populneus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73861,8 +73861,8 @@
   {
     "name": "iNatAg/brachylaena_huillensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73882,8 +73882,8 @@
   {
     "name": "iNatAg/brachystegia_spiciformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73903,8 +73903,8 @@
   {
     "name": "iNatAg/brassica_campestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73924,8 +73924,8 @@
   {
     "name": "iNatAg/brassica_chinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73945,8 +73945,8 @@
   {
     "name": "iNatAg/brassica_incana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73966,8 +73966,8 @@
   {
     "name": "iNatAg/brassica_juncea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -73987,8 +73987,8 @@
   {
     "name": "iNatAg/brassica_napus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74008,8 +74008,8 @@
   {
     "name": "iNatAg/brassica_nigra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74029,8 +74029,8 @@
   {
     "name": "iNatAg/brassica_rapa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74050,8 +74050,8 @@
   {
     "name": "iNatAg/brassica_tournefortii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74071,8 +74071,8 @@
   {
     "name": "iNatAg/bridelia_micrantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74092,8 +74092,8 @@
   {
     "name": "iNatAg/briza_maxima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74113,8 +74113,8 @@
   {
     "name": "iNatAg/briza_media",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74134,8 +74134,8 @@
   {
     "name": "iNatAg/briza_minor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74155,8 +74155,8 @@
   {
     "name": "iNatAg/bromus_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74176,8 +74176,8 @@
   {
     "name": "iNatAg/bromus_carinatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74197,8 +74197,8 @@
   {
     "name": "iNatAg/bromus_catharticus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74218,8 +74218,8 @@
   {
     "name": "iNatAg/bromus_diandrus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74239,8 +74239,8 @@
   {
     "name": "iNatAg/bromus_erectus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74260,8 +74260,8 @@
   {
     "name": "iNatAg/bromus_hordeaceus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74281,8 +74281,8 @@
   {
     "name": "iNatAg/bromus_inermis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74302,8 +74302,8 @@
   {
     "name": "iNatAg/bromus_madritensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74323,8 +74323,8 @@
   {
     "name": "iNatAg/bromus_marginatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74344,8 +74344,8 @@
   {
     "name": "iNatAg/bromus_racemosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74365,8 +74365,8 @@
   {
     "name": "iNatAg/bromus_rubens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74386,8 +74386,8 @@
   {
     "name": "iNatAg/bromus_secalinus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74407,8 +74407,8 @@
   {
     "name": "iNatAg/bromus_sterilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74428,8 +74428,8 @@
   {
     "name": "iNatAg/bromus_tectorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74449,8 +74449,8 @@
   {
     "name": "iNatAg/bromus_unioloides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74470,8 +74470,8 @@
   {
     "name": "iNatAg/bromus_willdenowii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74491,8 +74491,8 @@
   {
     "name": "iNatAg/brosimum_alicastrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74512,8 +74512,8 @@
   {
     "name": "iNatAg/broussonetia_papyrifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74533,8 +74533,8 @@
   {
     "name": "iNatAg/bruguiera_gymnorrhiza",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74554,8 +74554,8 @@
   {
     "name": "iNatAg/bryonia_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74575,8 +74575,8 @@
   {
     "name": "iNatAg/bryonia_cretica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74596,8 +74596,8 @@
   {
     "name": "iNatAg/buchloe_dactyloides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74617,8 +74617,8 @@
   {
     "name": "iNatAg/buckinghamia_celsissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74638,8 +74638,8 @@
   {
     "name": "iNatAg/bunias_erucago",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74659,8 +74659,8 @@
   {
     "name": "iNatAg/bunias_orientalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74680,8 +74680,8 @@
   {
     "name": "iNatAg/burkea_africana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74701,8 +74701,8 @@
   {
     "name": "iNatAg/bursera_simaruba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74722,8 +74722,8 @@
   {
     "name": "iNatAg/butea_monosperma",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74743,8 +74743,8 @@
   {
     "name": "iNatAg/butomus_umbellatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74764,8 +74764,8 @@
   {
     "name": "iNatAg/buxus_sempervirens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74785,8 +74785,8 @@
   {
     "name": "iNatAg/cacalia_atriplicifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74806,8 +74806,8 @@
   {
     "name": "iNatAg/caesalpinia_coriaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74827,8 +74827,8 @@
   {
     "name": "iNatAg/caesalpinia_sappan",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74848,8 +74848,8 @@
   {
     "name": "iNatAg/cajanus_cajan",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74869,8 +74869,8 @@
   {
     "name": "iNatAg/calamagrostis_epigeios",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74890,8 +74890,8 @@
   {
     "name": "iNatAg/calathea_allouia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74911,8 +74911,8 @@
   {
     "name": "iNatAg/calendula_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74932,8 +74932,8 @@
   {
     "name": "iNatAg/calendula_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74953,8 +74953,8 @@
   {
     "name": "iNatAg/calliandra_calothyrsus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74974,8 +74974,8 @@
   {
     "name": "iNatAg/calliandra_tweedii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -74995,8 +74995,8 @@
   {
     "name": "iNatAg/callisia_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75016,8 +75016,8 @@
   {
     "name": "iNatAg/callitriche_palustris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75037,8 +75037,8 @@
   {
     "name": "iNatAg/callitriche_stagnalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75058,8 +75058,8 @@
   {
     "name": "iNatAg/callitriche_verna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75079,8 +75079,8 @@
   {
     "name": "iNatAg/callitris_columellaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75100,8 +75100,8 @@
   {
     "name": "iNatAg/callitris_endlicheri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75121,8 +75121,8 @@
   {
     "name": "iNatAg/callitris_macleayana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75142,8 +75142,8 @@
   {
     "name": "iNatAg/calluna_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75163,8 +75163,8 @@
   {
     "name": "iNatAg/calodendrum_capense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75184,8 +75184,8 @@
   {
     "name": "iNatAg/calophyllum_apetalum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75205,8 +75205,8 @@
   {
     "name": "iNatAg/calophyllum_brasiliense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75226,8 +75226,8 @@
   {
     "name": "iNatAg/calophyllum_inophyllum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75247,8 +75247,8 @@
   {
     "name": "iNatAg/calopogonium_caeruleum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75268,8 +75268,8 @@
   {
     "name": "iNatAg/calopogonium_mucunoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75289,8 +75289,8 @@
   {
     "name": "iNatAg/calotropis_procera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75310,8 +75310,8 @@
   {
     "name": "iNatAg/caltha_palustris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75331,8 +75331,8 @@
   {
     "name": "iNatAg/calystegia_hederacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75352,8 +75352,8 @@
   {
     "name": "iNatAg/calystegia_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75373,8 +75373,8 @@
   {
     "name": "iNatAg/calystegia_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75394,8 +75394,8 @@
   {
     "name": "iNatAg/camelina_sativa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75415,8 +75415,8 @@
   {
     "name": "iNatAg/camellia_sinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75436,8 +75436,8 @@
   {
     "name": "iNatAg/campanula_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75457,8 +75457,8 @@
   {
     "name": "iNatAg/campanula_rapunculus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75478,8 +75478,8 @@
   {
     "name": "iNatAg/campanula_rotundifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75499,8 +75499,8 @@
   {
     "name": "iNatAg/cananga_odorata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75520,8 +75520,8 @@
   {
     "name": "iNatAg/canavalia_brasiliensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75541,8 +75541,8 @@
   {
     "name": "iNatAg/canavalia_ensiformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75562,8 +75562,8 @@
   {
     "name": "iNatAg/canavalia_gladiata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75583,8 +75583,8 @@
   {
     "name": "iNatAg/canna_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75604,8 +75604,8 @@
   {
     "name": "iNatAg/canthium_spinosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75625,8 +75625,8 @@
   {
     "name": "iNatAg/capparis_decidua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75646,8 +75646,8 @@
   {
     "name": "iNatAg/capparis_spinosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75667,8 +75667,8 @@
   {
     "name": "iNatAg/capparis_tomentosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75688,8 +75688,8 @@
   {
     "name": "iNatAg/capsella_bursa-pastoris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75709,8 +75709,8 @@
   {
     "name": "iNatAg/capsicum_annuum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75730,8 +75730,8 @@
   {
     "name": "iNatAg/capsicum_chinense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75751,8 +75751,8 @@
   {
     "name": "iNatAg/capsicum_frutescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75772,8 +75772,8 @@
   {
     "name": "iNatAg/capsicum_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75793,8 +75793,8 @@
   {
     "name": "iNatAg/caragana_arborescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75814,8 +75814,8 @@
   {
     "name": "iNatAg/caragana_microphylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75835,8 +75835,8 @@
   {
     "name": "iNatAg/carapa_guianensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75856,8 +75856,8 @@
   {
     "name": "iNatAg/cardamine_flexuosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75877,8 +75877,8 @@
   {
     "name": "iNatAg/cardamine_hirsuta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75898,8 +75898,8 @@
   {
     "name": "iNatAg/cardamine_impatiens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75919,8 +75919,8 @@
   {
     "name": "iNatAg/cardamine_oligosperma",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75940,8 +75940,8 @@
   {
     "name": "iNatAg/cardamine_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75961,8 +75961,8 @@
   {
     "name": "iNatAg/cardamine_pratensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -75982,8 +75982,8 @@
   {
     "name": "iNatAg/cardiospermum_halicacabum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76003,8 +76003,8 @@
   {
     "name": "iNatAg/carduus_acanthoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76024,8 +76024,8 @@
   {
     "name": "iNatAg/carduus_crispus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76045,8 +76045,8 @@
   {
     "name": "iNatAg/carduus_lanceolatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76066,8 +76066,8 @@
   {
     "name": "iNatAg/carduus_pycnocephalus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76087,8 +76087,8 @@
   {
     "name": "iNatAg/carex_nebrascensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76108,8 +76108,8 @@
   {
     "name": "iNatAg/carex_pallescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76129,8 +76129,8 @@
   {
     "name": "iNatAg/carica_cauliflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76150,8 +76150,8 @@
   {
     "name": "iNatAg/carica_papaya",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76171,8 +76171,8 @@
   {
     "name": "iNatAg/carica_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76192,8 +76192,8 @@
   {
     "name": "iNatAg/cariniana_pyriformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76213,8 +76213,8 @@
   {
     "name": "iNatAg/carissa_carandas",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76234,8 +76234,8 @@
   {
     "name": "iNatAg/carissa_edulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76255,8 +76255,8 @@
   {
     "name": "iNatAg/carissa_macrocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76276,8 +76276,8 @@
   {
     "name": "iNatAg/carlina_acaulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76297,8 +76297,8 @@
   {
     "name": "iNatAg/carludovica_palmata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76318,8 +76318,8 @@
   {
     "name": "iNatAg/caroxylon_aphyllum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76339,8 +76339,8 @@
   {
     "name": "iNatAg/carpinus_betulus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76360,8 +76360,8 @@
   {
     "name": "iNatAg/carthamus_creticus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76381,8 +76381,8 @@
   {
     "name": "iNatAg/carthamus_lanatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76402,8 +76402,8 @@
   {
     "name": "iNatAg/carthamus_tinctorius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76423,8 +76423,8 @@
   {
     "name": "iNatAg/carum_carvi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76444,8 +76444,8 @@
   {
     "name": "iNatAg/carya_illinoensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76465,8 +76465,8 @@
   {
     "name": "iNatAg/caryodendron_orinocense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76486,8 +76486,8 @@
   {
     "name": "iNatAg/caryota_urens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76507,8 +76507,8 @@
   {
     "name": "iNatAg/casimiroa_edulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76528,8 +76528,8 @@
   {
     "name": "iNatAg/cassia_articulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76549,8 +76549,8 @@
   {
     "name": "iNatAg/cassia_brewsteri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76570,8 +76570,8 @@
   {
     "name": "iNatAg/cassia_fistula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76591,8 +76591,8 @@
   {
     "name": "iNatAg/cassia_marilandica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76612,8 +76612,8 @@
   {
     "name": "iNatAg/cassia_nictitans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76633,8 +76633,8 @@
   {
     "name": "iNatAg/cassia_reticulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76654,8 +76654,8 @@
   {
     "name": "iNatAg/cassia_senna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76675,8 +76675,8 @@
   {
     "name": "iNatAg/cassia_siamea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76696,8 +76696,8 @@
   {
     "name": "iNatAg/cassia_sieberiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76717,8 +76717,8 @@
   {
     "name": "iNatAg/cassia_tomentosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76738,8 +76738,8 @@
   {
     "name": "iNatAg/cassia_tora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76759,8 +76759,8 @@
   {
     "name": "iNatAg/castanea_crenata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76780,8 +76780,8 @@
   {
     "name": "iNatAg/castanea_dentata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76801,8 +76801,8 @@
   {
     "name": "iNatAg/castanea_mollissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76822,8 +76822,8 @@
   {
     "name": "iNatAg/castanea_pumila",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76843,8 +76843,8 @@
   {
     "name": "iNatAg/castanea_sativa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76864,8 +76864,8 @@
   {
     "name": "iNatAg/castanospermum_australe",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76885,8 +76885,8 @@
   {
     "name": "iNatAg/castilla_elastica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76906,8 +76906,8 @@
   {
     "name": "iNatAg/castilleja_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76927,8 +76927,8 @@
   {
     "name": "iNatAg/castilleja_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76948,8 +76948,8 @@
   {
     "name": "iNatAg/casuarina_cristata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76969,8 +76969,8 @@
   {
     "name": "iNatAg/casuarina_cunninghamiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -76990,8 +76990,8 @@
   {
     "name": "iNatAg/casuarina_equisetifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77011,8 +77011,8 @@
   {
     "name": "iNatAg/casuarina_glauca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77032,8 +77032,8 @@
   {
     "name": "iNatAg/casuarina_junghuhniana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77053,8 +77053,8 @@
   {
     "name": "iNatAg/casuarina_obesa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77074,8 +77074,8 @@
   {
     "name": "iNatAg/catalpa_bignonioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77095,8 +77095,8 @@
   {
     "name": "iNatAg/catha_edulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77116,8 +77116,8 @@
   {
     "name": "iNatAg/catharanthus_roseus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77137,8 +77137,8 @@
   {
     "name": "iNatAg/ceanothus_americanus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77158,8 +77158,8 @@
   {
     "name": "iNatAg/ceanothus_prostratus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77179,8 +77179,8 @@
   {
     "name": "iNatAg/cedrela_odorata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77200,8 +77200,8 @@
   {
     "name": "iNatAg/cedrus_deodara",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77221,8 +77221,8 @@
   {
     "name": "iNatAg/ceiba_pentandra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77242,8 +77242,8 @@
   {
     "name": "iNatAg/celastrus_orbiculatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77263,8 +77263,8 @@
   {
     "name": "iNatAg/celastrus_scandens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77284,8 +77284,8 @@
   {
     "name": "iNatAg/celosia_argentea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77305,8 +77305,8 @@
   {
     "name": "iNatAg/celtis_australis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77326,8 +77326,8 @@
   {
     "name": "iNatAg/cenchrus_biflorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77347,8 +77347,8 @@
   {
     "name": "iNatAg/cenchrus_ciliaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77368,8 +77368,8 @@
   {
     "name": "iNatAg/cenchrus_echinatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77389,8 +77389,8 @@
   {
     "name": "iNatAg/cenchrus_setigerus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77410,8 +77410,8 @@
   {
     "name": "iNatAg/cenchrus_spinifex",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77431,8 +77431,8 @@
   {
     "name": "iNatAg/cenchrus_tribuloides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77452,8 +77452,8 @@
   {
     "name": "iNatAg/centaurea_biebersteinii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77473,8 +77473,8 @@
   {
     "name": "iNatAg/centaurea_calcitrapa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77494,8 +77494,8 @@
   {
     "name": "iNatAg/centaurea_cyanus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77515,8 +77515,8 @@
   {
     "name": "iNatAg/centaurea_diluta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77536,8 +77536,8 @@
   {
     "name": "iNatAg/centaurea_jacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77557,8 +77557,8 @@
   {
     "name": "iNatAg/centaurea_melitensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77578,8 +77578,8 @@
   {
     "name": "iNatAg/centaurea_nigra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77599,8 +77599,8 @@
   {
     "name": "iNatAg/centaurea_nigrescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77620,8 +77620,8 @@
   {
     "name": "iNatAg/centaurea_solstitalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77641,8 +77641,8 @@
   {
     "name": "iNatAg/centaurea_solstitialis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77662,8 +77662,8 @@
   {
     "name": "iNatAg/centaurea_stoebe",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77683,8 +77683,8 @@
   {
     "name": "iNatAg/centaurea_virgata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77704,8 +77704,8 @@
   {
     "name": "iNatAg/centella_asiatica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77725,8 +77725,8 @@
   {
     "name": "iNatAg/centropodia_glauca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77746,8 +77746,8 @@
   {
     "name": "iNatAg/centrosema_brasilianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77767,8 +77767,8 @@
   {
     "name": "iNatAg/centrosema_macrocarpum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77788,8 +77788,8 @@
   {
     "name": "iNatAg/centrosema_pascuorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77809,8 +77809,8 @@
   {
     "name": "iNatAg/centrosema_plumieri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77830,8 +77830,8 @@
   {
     "name": "iNatAg/centrosema_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77851,8 +77851,8 @@
   {
     "name": "iNatAg/centrosema_virginianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77872,8 +77872,8 @@
   {
     "name": "iNatAg/cephalanthus_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77893,8 +77893,8 @@
   {
     "name": "iNatAg/cerastium_arvense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77914,8 +77914,8 @@
   {
     "name": "iNatAg/cerastium_nutans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77935,8 +77935,8 @@
   {
     "name": "iNatAg/cerastium_vulgatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77956,8 +77956,8 @@
   {
     "name": "iNatAg/ceratonia_siliqua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77977,8 +77977,8 @@
   {
     "name": "iNatAg/ceratopetalum_apetalum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -77998,8 +77998,8 @@
   {
     "name": "iNatAg/ceratophyllum_demersum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78019,8 +78019,8 @@
   {
     "name": "iNatAg/ceratophyllum_echinatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78040,8 +78040,8 @@
   {
     "name": "iNatAg/ceriops_tagal",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78061,8 +78061,8 @@
   {
     "name": "iNatAg/cestrum_diurnum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78082,8 +78082,8 @@
   {
     "name": "iNatAg/ceterach_officinarum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78103,8 +78103,8 @@
   {
     "name": "iNatAg/chaerophyllum_tainturieri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78124,8 +78124,8 @@
   {
     "name": "iNatAg/chamaebatia_foliolosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78145,8 +78145,8 @@
   {
     "name": "iNatAg/chamaecrista_nictitans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78166,8 +78166,8 @@
   {
     "name": "iNatAg/chamaecrista_rotundifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78187,8 +78187,8 @@
   {
     "name": "iNatAg/chamaedorea_tepejilote",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78208,8 +78208,8 @@
   {
     "name": "iNatAg/chamaerops_humilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78229,8 +78229,8 @@
   {
     "name": "iNatAg/chara_intermedia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78250,8 +78250,8 @@
   {
     "name": "iNatAg/chelidonium_majus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78271,8 +78271,8 @@
   {
     "name": "iNatAg/chenopodium_album",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78292,8 +78292,8 @@
   {
     "name": "iNatAg/chenopodium_ambrosioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78313,8 +78313,8 @@
   {
     "name": "iNatAg/chenopodium_ambrosoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78334,8 +78334,8 @@
   {
     "name": "iNatAg/chenopodium_berlandieri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78355,8 +78355,8 @@
   {
     "name": "iNatAg/chenopodium_bonus-henricus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78376,8 +78376,8 @@
   {
     "name": "iNatAg/chenopodium_botrys",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78397,8 +78397,8 @@
   {
     "name": "iNatAg/chenopodium_ficifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78418,8 +78418,8 @@
   {
     "name": "iNatAg/chenopodium_gigantospermum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78439,8 +78439,8 @@
   {
     "name": "iNatAg/chenopodium_glaucum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78460,8 +78460,8 @@
   {
     "name": "iNatAg/chenopodium_missouriense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78481,8 +78481,8 @@
   {
     "name": "iNatAg/chenopodium_multifidum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78502,8 +78502,8 @@
   {
     "name": "iNatAg/chenopodium_murale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78523,8 +78523,8 @@
   {
     "name": "iNatAg/chenopodium_polyspermum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78544,8 +78544,8 @@
   {
     "name": "iNatAg/chenopodium_quinoa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78565,8 +78565,8 @@
   {
     "name": "iNatAg/chenopodium_rubrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78586,8 +78586,8 @@
   {
     "name": "iNatAg/chenopodium_urbicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78607,8 +78607,8 @@
   {
     "name": "iNatAg/chloris_ciliata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78628,8 +78628,8 @@
   {
     "name": "iNatAg/chloris_gayana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78649,8 +78649,8 @@
   {
     "name": "iNatAg/chloris_roxburghiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78670,8 +78670,8 @@
   {
     "name": "iNatAg/chloris_verticillata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78691,8 +78691,8 @@
   {
     "name": "iNatAg/chloris_virgata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78712,8 +78712,8 @@
   {
     "name": "iNatAg/chlorogalum_pomeridianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78733,8 +78733,8 @@
   {
     "name": "iNatAg/chlorophora_excelsa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78754,8 +78754,8 @@
   {
     "name": "iNatAg/chlorophytum_comosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78775,8 +78775,8 @@
   {
     "name": "iNatAg/chloroxylon_swietenia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78796,8 +78796,8 @@
   {
     "name": "iNatAg/chromolaena_odorata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78817,8 +78817,8 @@
   {
     "name": "iNatAg/chrysanthemum_coronarium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78838,8 +78838,8 @@
   {
     "name": "iNatAg/chrysanthemum_leucanthemum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78859,8 +78859,8 @@
   {
     "name": "iNatAg/chrysophyllum_cainito",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78880,8 +78880,8 @@
   {
     "name": "iNatAg/chrysopogon_aciculatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78901,8 +78901,8 @@
   {
     "name": "iNatAg/chukrasia_velutina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78922,8 +78922,8 @@
   {
     "name": "iNatAg/cicer_arietinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78943,8 +78943,8 @@
   {
     "name": "iNatAg/cichorium_endivia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78964,8 +78964,8 @@
   {
     "name": "iNatAg/cichorium_intybus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -78985,8 +78985,8 @@
   {
     "name": "iNatAg/cicuta_bulbifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79006,8 +79006,8 @@
   {
     "name": "iNatAg/cicuta_mackenzieana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79027,8 +79027,8 @@
   {
     "name": "iNatAg/cicuta_maculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79048,8 +79048,8 @@
   {
     "name": "iNatAg/cicuta_virosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79069,8 +79069,8 @@
   {
     "name": "iNatAg/cimicifuga_racemosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79090,8 +79090,8 @@
   {
     "name": "iNatAg/cinchona_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79111,8 +79111,8 @@
   {
     "name": "iNatAg/cinchona_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79132,8 +79132,8 @@
   {
     "name": "iNatAg/cinnamomum_burmannii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79153,8 +79153,8 @@
   {
     "name": "iNatAg/cinnamomum_camphora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79174,8 +79174,8 @@
   {
     "name": "iNatAg/cinnamomum_cassia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79195,8 +79195,8 @@
   {
     "name": "iNatAg/cinnamomum_verum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79216,8 +79216,8 @@
   {
     "name": "iNatAg/cistus_creticus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79237,8 +79237,8 @@
   {
     "name": "iNatAg/citrofortunella_microcarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79258,8 +79258,8 @@
   {
     "name": "iNatAg/citrullus_colocynthis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79279,8 +79279,8 @@
   {
     "name": "iNatAg/citrullus_lanatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79300,8 +79300,8 @@
   {
     "name": "iNatAg/citrus_aurantifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79321,8 +79321,8 @@
   {
     "name": "iNatAg/citrus_aurantium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79342,8 +79342,8 @@
   {
     "name": "iNatAg/citrus_deliciosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79363,8 +79363,8 @@
   {
     "name": "iNatAg/citrus_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79384,8 +79384,8 @@
   {
     "name": "iNatAg/citrus_limon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79405,8 +79405,8 @@
   {
     "name": "iNatAg/citrus_madurensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79426,8 +79426,8 @@
   {
     "name": "iNatAg/citrus_medica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79447,8 +79447,8 @@
   {
     "name": "iNatAg/citrus_paradisi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79468,8 +79468,8 @@
   {
     "name": "iNatAg/citrus_reticulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79489,8 +79489,8 @@
   {
     "name": "iNatAg/citrus_sinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79510,8 +79510,8 @@
   {
     "name": "iNatAg/citrus_unshiu",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79531,8 +79531,8 @@
   {
     "name": "iNatAg/clausena_lansium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79552,8 +79552,8 @@
   {
     "name": "iNatAg/claytonia_caroliniana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79573,8 +79573,8 @@
   {
     "name": "iNatAg/claytonia_virginica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79594,8 +79594,8 @@
   {
     "name": "iNatAg/cleistogenes_squarrosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79615,8 +79615,8 @@
   {
     "name": "iNatAg/clematis_ligusticifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79636,8 +79636,8 @@
   {
     "name": "iNatAg/clematis_orientalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79657,8 +79657,8 @@
   {
     "name": "iNatAg/clematis_virginiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79678,8 +79678,8 @@
   {
     "name": "iNatAg/clematis_vitalba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79699,8 +79699,8 @@
   {
     "name": "iNatAg/cleome_gynandra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79720,8 +79720,8 @@
   {
     "name": "iNatAg/cleome_hassleriana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79741,8 +79741,8 @@
   {
     "name": "iNatAg/cleome_viscosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79762,8 +79762,8 @@
   {
     "name": "iNatAg/clitoria_laurifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79783,8 +79783,8 @@
   {
     "name": "iNatAg/clitoria_ternatea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79804,8 +79804,8 @@
   {
     "name": "iNatAg/clusia_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79825,8 +79825,8 @@
   {
     "name": "iNatAg/cnicus_benedictus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79846,8 +79846,8 @@
   {
     "name": "iNatAg/coccoloba_uvifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79867,8 +79867,8 @@
   {
     "name": "iNatAg/cochlospermum_religiosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79888,8 +79888,8 @@
   {
     "name": "iNatAg/cocos_nucifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79909,8 +79909,8 @@
   {
     "name": "iNatAg/coffea_arabica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79930,8 +79930,8 @@
   {
     "name": "iNatAg/coffea_canephora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79951,8 +79951,8 @@
   {
     "name": "iNatAg/coffea_liberica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79972,8 +79972,8 @@
   {
     "name": "iNatAg/coix_lacryma-jobi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -79993,8 +79993,8 @@
   {
     "name": "iNatAg/cola_acuminata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80014,8 +80014,8 @@
   {
     "name": "iNatAg/cola_nitida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80035,8 +80035,8 @@
   {
     "name": "iNatAg/colchicum_autumnale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80056,8 +80056,8 @@
   {
     "name": "iNatAg/coleus_amboinicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80077,8 +80077,8 @@
   {
     "name": "iNatAg/colocasia_esculenta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80098,8 +80098,8 @@
   {
     "name": "iNatAg/colophospermum_mopane",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80119,8 +80119,8 @@
   {
     "name": "iNatAg/combretum_aculeatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80140,8 +80140,8 @@
   {
     "name": "iNatAg/combretum_micranthum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80161,8 +80161,8 @@
   {
     "name": "iNatAg/combretum_molle",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80182,8 +80182,8 @@
   {
     "name": "iNatAg/commelina_bengalensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80203,8 +80203,8 @@
   {
     "name": "iNatAg/commelina_benghalensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80224,8 +80224,8 @@
   {
     "name": "iNatAg/commelina_communis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80245,8 +80245,8 @@
   {
     "name": "iNatAg/commelina_erecta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80266,8 +80266,8 @@
   {
     "name": "iNatAg/commiphora_africana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80287,8 +80287,8 @@
   {
     "name": "iNatAg/conium_maculatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80308,8 +80308,8 @@
   {
     "name": "iNatAg/conocarpus_erectus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80329,8 +80329,8 @@
   {
     "name": "iNatAg/conocarpus_lancifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80350,8 +80350,8 @@
   {
     "name": "iNatAg/convallaria_majalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80371,8 +80371,8 @@
   {
     "name": "iNatAg/convolvulus_althaeoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80392,8 +80392,8 @@
   {
     "name": "iNatAg/convolvulus_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80413,8 +80413,8 @@
   {
     "name": "iNatAg/convolvulus_equitans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80434,8 +80434,8 @@
   {
     "name": "iNatAg/convolvulus_sepium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80455,8 +80455,8 @@
   {
     "name": "iNatAg/copaifera_langsdorffii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80476,8 +80476,8 @@
   {
     "name": "iNatAg/corchorus_aestuans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80497,8 +80497,8 @@
   {
     "name": "iNatAg/corchorus_capsularis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80518,8 +80518,8 @@
   {
     "name": "iNatAg/cordia_africana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80539,8 +80539,8 @@
   {
     "name": "iNatAg/cordia_alliodora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80560,8 +80560,8 @@
   {
     "name": "iNatAg/coreopsis_lanceolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80581,8 +80581,8 @@
   {
     "name": "iNatAg/coreopsis_tinctoria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80602,8 +80602,8 @@
   {
     "name": "iNatAg/coreopsis_verticillata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80623,8 +80623,8 @@
   {
     "name": "iNatAg/coriandrum_sativum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80644,8 +80644,8 @@
   {
     "name": "iNatAg/corispermum_hyssopifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80665,8 +80665,8 @@
   {
     "name": "iNatAg/corispermum_villosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80686,8 +80686,8 @@
   {
     "name": "iNatAg/cornus_canadensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80707,8 +80707,8 @@
   {
     "name": "iNatAg/cornus_florida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80728,8 +80728,8 @@
   {
     "name": "iNatAg/cornus_mas",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80749,8 +80749,8 @@
   {
     "name": "iNatAg/cornus_sanguinea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80770,8 +80770,8 @@
   {
     "name": "iNatAg/coronilla_varia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80791,8 +80791,8 @@
   {
     "name": "iNatAg/corylus_avellana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80812,8 +80812,8 @@
   {
     "name": "iNatAg/corylus_maxima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80833,8 +80833,8 @@
   {
     "name": "iNatAg/cotoneaster_franchetii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80854,8 +80854,8 @@
   {
     "name": "iNatAg/cotula_coronopifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80875,8 +80875,8 @@
   {
     "name": "iNatAg/crambe_cordifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80896,8 +80896,8 @@
   {
     "name": "iNatAg/crambe_maritima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80917,8 +80917,8 @@
   {
     "name": "iNatAg/crassula_sieberiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80938,8 +80938,8 @@
   {
     "name": "iNatAg/crataegus_crus-galli",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80959,8 +80959,8 @@
   {
     "name": "iNatAg/crataegus_crus-gallii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -80980,8 +80980,8 @@
   {
     "name": "iNatAg/crataegus_marshallii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81001,8 +81001,8 @@
   {
     "name": "iNatAg/crataegus_monogyna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81022,8 +81022,8 @@
   {
     "name": "iNatAg/crataegus_oxyacantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81043,8 +81043,8 @@
   {
     "name": "iNatAg/crataegus_rivularis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81064,8 +81064,8 @@
   {
     "name": "iNatAg/cratylia_argentea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81085,8 +81085,8 @@
   {
     "name": "iNatAg/crepis_biennis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81106,8 +81106,8 @@
   {
     "name": "iNatAg/crepis_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81127,8 +81127,8 @@
   {
     "name": "iNatAg/crepis_vesicaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81148,8 +81148,8 @@
   {
     "name": "iNatAg/cressa_truxillensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81169,8 +81169,8 @@
   {
     "name": "iNatAg/crinum_americanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81190,8 +81190,8 @@
   {
     "name": "iNatAg/crithmum_maritimum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81211,8 +81211,8 @@
   {
     "name": "iNatAg/crocus_sativus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81232,8 +81232,8 @@
   {
     "name": "iNatAg/crotalaria_juncea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81253,8 +81253,8 @@
   {
     "name": "iNatAg/crotalaria_lanceolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81274,8 +81274,8 @@
   {
     "name": "iNatAg/crotalaria_pallida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81295,8 +81295,8 @@
   {
     "name": "iNatAg/crotalaria_podocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81316,8 +81316,8 @@
   {
     "name": "iNatAg/crotalaria_retusa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81337,8 +81337,8 @@
   {
     "name": "iNatAg/crotalaria_sagittalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81358,8 +81358,8 @@
   {
     "name": "iNatAg/crotalaria_spectabilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81379,8 +81379,8 @@
   {
     "name": "iNatAg/croton_monanthogynus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81400,8 +81400,8 @@
   {
     "name": "iNatAg/crucianella_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81421,8 +81421,8 @@
   {
     "name": "iNatAg/cryptocarya_erythroxylon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81442,8 +81442,8 @@
   {
     "name": "iNatAg/cryptomeria_japonica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81463,8 +81463,8 @@
   {
     "name": "iNatAg/cryptotaenia_japonica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81484,8 +81484,8 @@
   {
     "name": "iNatAg/ctenium_concinnum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81505,8 +81505,8 @@
   {
     "name": "iNatAg/cucumis_anguria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81526,8 +81526,8 @@
   {
     "name": "iNatAg/cucumis_melo",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81547,8 +81547,8 @@
   {
     "name": "iNatAg/cucumis_sativus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81568,8 +81568,8 @@
   {
     "name": "iNatAg/cucurbita_argyrosperma",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81589,8 +81589,8 @@
   {
     "name": "iNatAg/cucurbita_digitata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81610,8 +81610,8 @@
   {
     "name": "iNatAg/cucurbita_ficifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81631,8 +81631,8 @@
   {
     "name": "iNatAg/cucurbita_foetidissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81652,8 +81652,8 @@
   {
     "name": "iNatAg/cucurbita_maxima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81673,8 +81673,8 @@
   {
     "name": "iNatAg/cucurbita_mixta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81694,8 +81694,8 @@
   {
     "name": "iNatAg/cucurbita_moschata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81715,8 +81715,8 @@
   {
     "name": "iNatAg/cucurbita_pepo",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81736,8 +81736,8 @@
   {
     "name": "iNatAg/cunninghamia_lanceolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81757,8 +81757,8 @@
   {
     "name": "iNatAg/cupania_auriculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81778,8 +81778,8 @@
   {
     "name": "iNatAg/cuphea_viscosissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81799,8 +81799,8 @@
   {
     "name": "iNatAg/cupressus_arizonica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81820,8 +81820,8 @@
   {
     "name": "iNatAg/cupressus_lusitanica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81841,8 +81841,8 @@
   {
     "name": "iNatAg/cupressus_macrocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81862,8 +81862,8 @@
   {
     "name": "iNatAg/cupressus_sempervirens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81883,8 +81883,8 @@
   {
     "name": "iNatAg/cupressus_torulosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81904,8 +81904,8 @@
   {
     "name": "iNatAg/curcuma_longa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81925,8 +81925,8 @@
   {
     "name": "iNatAg/curcuma_zedoaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81946,8 +81946,8 @@
   {
     "name": "iNatAg/cuscuta_approximata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81967,8 +81967,8 @@
   {
     "name": "iNatAg/cuscuta_epithymum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -81988,8 +81988,8 @@
   {
     "name": "iNatAg/cuscuta_obtusiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82009,8 +82009,8 @@
   {
     "name": "iNatAg/cuscuta_planiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82030,8 +82030,8 @@
   {
     "name": "iNatAg/cuscuta_sandwichiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82051,8 +82051,8 @@
   {
     "name": "iNatAg/cydonia_oblonga",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82072,8 +82072,8 @@
   {
     "name": "iNatAg/cymbalaria_muralis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82093,8 +82093,8 @@
   {
     "name": "iNatAg/cymbopogon_citratus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82114,8 +82114,8 @@
   {
     "name": "iNatAg/cynanchum_scoparium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82135,8 +82135,8 @@
   {
     "name": "iNatAg/cynara_cardunculus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82156,8 +82156,8 @@
   {
     "name": "iNatAg/cynara_scolymus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82177,8 +82177,8 @@
   {
     "name": "iNatAg/cynodon_dactylon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82198,8 +82198,8 @@
   {
     "name": "iNatAg/cynodon_nlemfuensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82219,8 +82219,8 @@
   {
     "name": "iNatAg/cynoglossum_officinale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82240,8 +82240,8 @@
   {
     "name": "iNatAg/cynometra_cauliflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82261,8 +82261,8 @@
   {
     "name": "iNatAg/cynosurus_cristatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82282,8 +82282,8 @@
   {
     "name": "iNatAg/cyperus_alopecuroides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82303,8 +82303,8 @@
   {
     "name": "iNatAg/cyperus_articulatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82324,8 +82324,8 @@
   {
     "name": "iNatAg/cyperus_compressus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82345,8 +82345,8 @@
   {
     "name": "iNatAg/cyperus_croceus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82366,8 +82366,8 @@
   {
     "name": "iNatAg/cyperus_cuspidatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82387,8 +82387,8 @@
   {
     "name": "iNatAg/cyperus_difformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82408,8 +82408,8 @@
   {
     "name": "iNatAg/cyperus_eragrostis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82429,8 +82429,8 @@
   {
     "name": "iNatAg/cyperus_erythrorhizos",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82450,8 +82450,8 @@
   {
     "name": "iNatAg/cyperus_esculentus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82471,8 +82471,8 @@
   {
     "name": "iNatAg/cyperus_flavescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82492,8 +82492,8 @@
   {
     "name": "iNatAg/cyperus_fuscus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82513,8 +82513,8 @@
   {
     "name": "iNatAg/cyperus_hyalinus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82534,8 +82534,8 @@
   {
     "name": "iNatAg/cyperus_involucratus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82555,8 +82555,8 @@
   {
     "name": "iNatAg/cyperus_iria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82576,8 +82576,8 @@
   {
     "name": "iNatAg/cyperus_lanceolatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82597,8 +82597,8 @@
   {
     "name": "iNatAg/cyperus_longus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82618,8 +82618,8 @@
   {
     "name": "iNatAg/cyperus_odoratus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82639,8 +82639,8 @@
   {
     "name": "iNatAg/cyperus_pilosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82660,8 +82660,8 @@
   {
     "name": "iNatAg/cyperus_prolifer",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82681,8 +82681,8 @@
   {
     "name": "iNatAg/cyperus_pseudovegetus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82702,8 +82702,8 @@
   {
     "name": "iNatAg/cyperus_rotundus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82723,8 +82723,8 @@
   {
     "name": "iNatAg/cyperus_sanguinolentus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82744,8 +82744,8 @@
   {
     "name": "iNatAg/cyperus_squarrosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82765,8 +82765,8 @@
   {
     "name": "iNatAg/cyperus_strigosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82786,8 +82786,8 @@
   {
     "name": "iNatAg/cyperus_subsquarrosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82807,8 +82807,8 @@
   {
     "name": "iNatAg/cyperus_surinamensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82828,8 +82828,8 @@
   {
     "name": "iNatAg/cyphomandra_betacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82849,8 +82849,8 @@
   {
     "name": "iNatAg/cytisus_albus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82870,8 +82870,8 @@
   {
     "name": "iNatAg/cytisus_proliferus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82891,8 +82891,8 @@
   {
     "name": "iNatAg/cytisus_supinus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82912,8 +82912,8 @@
   {
     "name": "iNatAg/dacrydium_franklinii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82933,8 +82933,8 @@
   {
     "name": "iNatAg/dactylis_glomerata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82954,8 +82954,8 @@
   {
     "name": "iNatAg/dactyloctenium_aegyptium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82975,8 +82975,8 @@
   {
     "name": "iNatAg/dactyloctenium_giganteum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -82996,8 +82996,8 @@
   {
     "name": "iNatAg/dalbergia_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83017,8 +83017,8 @@
   {
     "name": "iNatAg/dalbergia_melanoxylon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83038,8 +83038,8 @@
   {
     "name": "iNatAg/dalbergia_sissoo",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83059,8 +83059,8 @@
   {
     "name": "iNatAg/daphne_laureola",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83080,8 +83080,8 @@
   {
     "name": "iNatAg/daphne_mezereum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83101,8 +83101,8 @@
   {
     "name": "iNatAg/datura_ferox",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83122,8 +83122,8 @@
   {
     "name": "iNatAg/datura_quercifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83143,8 +83143,8 @@
   {
     "name": "iNatAg/datura_stramonium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83164,8 +83164,8 @@
   {
     "name": "iNatAg/daucus_carota",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83185,8 +83185,8 @@
   {
     "name": "iNatAg/daucus_carrota",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83206,8 +83206,8 @@
   {
     "name": "iNatAg/delairea_odorata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83227,8 +83227,8 @@
   {
     "name": "iNatAg/delonix_regia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83248,8 +83248,8 @@
   {
     "name": "iNatAg/delphinium_bicolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83269,8 +83269,8 @@
   {
     "name": "iNatAg/delphinium_carolinianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83290,8 +83290,8 @@
   {
     "name": "iNatAg/delphinium_menziesii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83311,8 +83311,8 @@
   {
     "name": "iNatAg/delphinium_trolliifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83332,8 +83332,8 @@
   {
     "name": "iNatAg/dendrocalamus_asper",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83353,8 +83353,8 @@
   {
     "name": "iNatAg/dendrocalamus_giganteus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83374,8 +83374,8 @@
   {
     "name": "iNatAg/dendrocalamus_strictus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83395,8 +83395,8 @@
   {
     "name": "iNatAg/dendrolobium_umbellatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83416,8 +83416,8 @@
   {
     "name": "iNatAg/derris_elliptica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83437,8 +83437,8 @@
   {
     "name": "iNatAg/deschampsia_caespitosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83458,8 +83458,8 @@
   {
     "name": "iNatAg/deschampsia_flexuosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83479,8 +83479,8 @@
   {
     "name": "iNatAg/desmanthus_leptophyllus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83500,8 +83500,8 @@
   {
     "name": "iNatAg/desmanthus_virgatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83521,8 +83521,8 @@
   {
     "name": "iNatAg/desmodium_affine",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83542,8 +83542,8 @@
   {
     "name": "iNatAg/desmodium_barbatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83563,8 +83563,8 @@
   {
     "name": "iNatAg/desmodium_cuneatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83584,8 +83584,8 @@
   {
     "name": "iNatAg/desmodium_cuspidatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83605,8 +83605,8 @@
   {
     "name": "iNatAg/desmodium_distortum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83626,8 +83626,8 @@
   {
     "name": "iNatAg/desmodium_gyroides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83647,8 +83647,8 @@
   {
     "name": "iNatAg/desmodium_heterophyllum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83668,8 +83668,8 @@
   {
     "name": "iNatAg/desmodium_incanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83689,8 +83689,8 @@
   {
     "name": "iNatAg/desmodium_intortum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83710,8 +83710,8 @@
   {
     "name": "iNatAg/desmodium_paniculatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83731,8 +83731,8 @@
   {
     "name": "iNatAg/desmodium_psilocarpum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83752,8 +83752,8 @@
   {
     "name": "iNatAg/desmodium_reticulatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83773,8 +83773,8 @@
   {
     "name": "iNatAg/desmodium_sandwicense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83794,8 +83794,8 @@
   {
     "name": "iNatAg/desmodium_scorpiurus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83815,8 +83815,8 @@
   {
     "name": "iNatAg/desmodium_tortuosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83836,8 +83836,8 @@
   {
     "name": "iNatAg/desmodium_triflorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83857,8 +83857,8 @@
   {
     "name": "iNatAg/desmodium_uncinatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83878,8 +83878,8 @@
   {
     "name": "iNatAg/desmodium_velutinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83899,8 +83899,8 @@
   {
     "name": "iNatAg/dialium_guineense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83920,8 +83920,8 @@
   {
     "name": "iNatAg/dianthus_armeria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83941,8 +83941,8 @@
   {
     "name": "iNatAg/dichanthium_annulatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83962,8 +83962,8 @@
   {
     "name": "iNatAg/dichanthium_aristatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -83983,8 +83983,8 @@
   {
     "name": "iNatAg/dichanthium_caricosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84004,8 +84004,8 @@
   {
     "name": "iNatAg/dichanthium_sericeum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84025,8 +84025,8 @@
   {
     "name": "iNatAg/dichondra_carolinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84046,8 +84046,8 @@
   {
     "name": "iNatAg/dichondra_micrantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84067,8 +84067,8 @@
   {
     "name": "iNatAg/dichrostachys_cinerea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84088,8 +84088,8 @@
   {
     "name": "iNatAg/dictamnus_albus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84109,8 +84109,8 @@
   {
     "name": "iNatAg/didymopanax_morototoni",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84130,8 +84130,8 @@
   {
     "name": "iNatAg/diervilla_lonicera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84151,8 +84151,8 @@
   {
     "name": "iNatAg/digitalis_lanata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84172,8 +84172,8 @@
   {
     "name": "iNatAg/digitalis_lutea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84193,8 +84193,8 @@
   {
     "name": "iNatAg/digitalis_purpurea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84214,8 +84214,8 @@
   {
     "name": "iNatAg/digitaria_argyrograpta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84235,8 +84235,8 @@
   {
     "name": "iNatAg/digitaria_ciliaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84256,8 +84256,8 @@
   {
     "name": "iNatAg/digitaria_decumbens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84277,8 +84277,8 @@
   {
     "name": "iNatAg/digitaria_didactyla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84298,8 +84298,8 @@
   {
     "name": "iNatAg/digitaria_eriantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84319,8 +84319,8 @@
   {
     "name": "iNatAg/digitaria_tricholaenoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84340,8 +84340,8 @@
   {
     "name": "iNatAg/digitaria_violascens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84361,8 +84361,8 @@
   {
     "name": "iNatAg/dillenia_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84382,8 +84382,8 @@
   {
     "name": "iNatAg/dillenia_pentagyna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84403,8 +84403,8 @@
   {
     "name": "iNatAg/diodia_virginiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84424,8 +84424,8 @@
   {
     "name": "iNatAg/dioscorea_alata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84445,8 +84445,8 @@
   {
     "name": "iNatAg/dioscorea_bulbifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84466,8 +84466,8 @@
   {
     "name": "iNatAg/dioscorea_esculenta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84487,8 +84487,8 @@
   {
     "name": "iNatAg/dioscorea_opposita",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84508,8 +84508,8 @@
   {
     "name": "iNatAg/dioscorea_oppositifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84529,8 +84529,8 @@
   {
     "name": "iNatAg/dioscorea_trifida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84550,8 +84550,8 @@
   {
     "name": "iNatAg/diospyros_digyna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84571,8 +84571,8 @@
   {
     "name": "iNatAg/diospyros_kaki",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84592,8 +84592,8 @@
   {
     "name": "iNatAg/diospyros_malabarica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84613,8 +84613,8 @@
   {
     "name": "iNatAg/diospyros_melanoxylon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84634,8 +84634,8 @@
   {
     "name": "iNatAg/diospyros_mespiliformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84655,8 +84655,8 @@
   {
     "name": "iNatAg/diospyros_virginiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84676,8 +84676,8 @@
   {
     "name": "iNatAg/diplachne_fusca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84697,8 +84697,8 @@
   {
     "name": "iNatAg/diploglottis_cunninghamii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84718,8 +84718,8 @@
   {
     "name": "iNatAg/dipsacus_fullonum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84739,8 +84739,8 @@
   {
     "name": "iNatAg/dipsacus_laciniatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84760,8 +84760,8 @@
   {
     "name": "iNatAg/dipsacus_sylvestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84781,8 +84781,8 @@
   {
     "name": "iNatAg/dipterocarpus_alatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84802,8 +84802,8 @@
   {
     "name": "iNatAg/dipterocarpus_indicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84823,8 +84823,8 @@
   {
     "name": "iNatAg/dipterocarpus_turbinatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84844,8 +84844,8 @@
   {
     "name": "iNatAg/dodonaea_viscosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84865,8 +84865,8 @@
   {
     "name": "iNatAg/dovyalis_caffra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84886,8 +84886,8 @@
   {
     "name": "iNatAg/dovyalis_hebecarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84907,8 +84907,8 @@
   {
     "name": "iNatAg/draba_nemorosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84928,8 +84928,8 @@
   {
     "name": "iNatAg/draba_verna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84949,8 +84949,8 @@
   {
     "name": "iNatAg/dracocephalum_parviflorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84970,8 +84970,8 @@
   {
     "name": "iNatAg/dracocephalum_thymiflorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -84991,8 +84991,8 @@
   {
     "name": "iNatAg/drosera_rotundifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85012,8 +85012,8 @@
   {
     "name": "iNatAg/dryopteris_filix-mas",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85033,8 +85033,8 @@
   {
     "name": "iNatAg/duboisia_myoporoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85054,8 +85054,8 @@
   {
     "name": "iNatAg/durio_zibethinus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85075,8 +85075,8 @@
   {
     "name": "iNatAg/dysoxylum_fraserianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85096,8 +85096,8 @@
   {
     "name": "iNatAg/ecballium_elaterium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85117,8 +85117,8 @@
   {
     "name": "iNatAg/echinacea_purpurea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85138,8 +85138,8 @@
   {
     "name": "iNatAg/echinochloa_colona",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85159,8 +85159,8 @@
   {
     "name": "iNatAg/echinochloa_crus-galli",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85180,8 +85180,8 @@
   {
     "name": "iNatAg/echinochloa_frumentacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85201,8 +85201,8 @@
   {
     "name": "iNatAg/echinochloa_polystachya",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85222,8 +85222,8 @@
   {
     "name": "iNatAg/echinochloa_pyramidalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85243,8 +85243,8 @@
   {
     "name": "iNatAg/echinops_sphaerocephalus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85264,8 +85264,8 @@
   {
     "name": "iNatAg/echium_plantagineum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85285,8 +85285,8 @@
   {
     "name": "iNatAg/echium_vulgare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85306,8 +85306,8 @@
   {
     "name": "iNatAg/ehrharta_calycina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85327,8 +85327,8 @@
   {
     "name": "iNatAg/ehrharta_erecta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85348,8 +85348,8 @@
   {
     "name": "iNatAg/ehrharta_longiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85369,8 +85369,8 @@
   {
     "name": "iNatAg/ehrharta_villosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85390,8 +85390,8 @@
   {
     "name": "iNatAg/eichhornia_crassipes",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85411,8 +85411,8 @@
   {
     "name": "iNatAg/ekebergia_capensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85432,8 +85432,8 @@
   {
     "name": "iNatAg/elaeagnus_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85453,8 +85453,8 @@
   {
     "name": "iNatAg/elaeagnus_multiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85474,8 +85474,8 @@
   {
     "name": "iNatAg/elaeis_guineensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85495,8 +85495,8 @@
   {
     "name": "iNatAg/elaeis_oleifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85516,8 +85516,8 @@
   {
     "name": "iNatAg/elaeocarpus_grandis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85537,8 +85537,8 @@
   {
     "name": "iNatAg/eleagnus_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85558,8 +85558,8 @@
   {
     "name": "iNatAg/elegia_cuspidata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85579,8 +85579,8 @@
   {
     "name": "iNatAg/eleocharis_cellulosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85600,8 +85600,8 @@
   {
     "name": "iNatAg/eleocharis_dulcis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85621,8 +85621,8 @@
   {
     "name": "iNatAg/eleocharis_macrostachya",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85642,8 +85642,8 @@
   {
     "name": "iNatAg/eleocharis_montevidensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85663,8 +85663,8 @@
   {
     "name": "iNatAg/eleocharis_vivipara",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85684,8 +85684,8 @@
   {
     "name": "iNatAg/elephantopus_mollis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85705,8 +85705,8 @@
   {
     "name": "iNatAg/elephantorrhiza_elephantina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85726,8 +85726,8 @@
   {
     "name": "iNatAg/elettaria_cardamomum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85747,8 +85747,8 @@
   {
     "name": "iNatAg/eleusine_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85768,8 +85768,8 @@
   {
     "name": "iNatAg/ellisia_nyctelea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85789,8 +85789,8 @@
   {
     "name": "iNatAg/elsholtzia_ciliata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85810,8 +85810,8 @@
   {
     "name": "iNatAg/elymus_canadensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85831,8 +85831,8 @@
   {
     "name": "iNatAg/elymus_caput-medusae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85852,8 +85852,8 @@
   {
     "name": "iNatAg/elymus_cinereus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85873,8 +85873,8 @@
   {
     "name": "iNatAg/elymus_condensatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85894,8 +85894,8 @@
   {
     "name": "iNatAg/elymus_dahuricus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85915,8 +85915,8 @@
   {
     "name": "iNatAg/elymus_glaucus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85936,8 +85936,8 @@
   {
     "name": "iNatAg/elymus_viginicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85957,8 +85957,8 @@
   {
     "name": "iNatAg/elymus_virginicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85978,8 +85978,8 @@
   {
     "name": "iNatAg/emilia_sonchifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -85999,8 +85999,8 @@
   {
     "name": "iNatAg/encalypta_intermedia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86020,8 +86020,8 @@
   {
     "name": "iNatAg/enneapogon_scoparius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86041,8 +86041,8 @@
   {
     "name": "iNatAg/ensete_ventricosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86062,8 +86062,8 @@
   {
     "name": "iNatAg/entada_abyssinica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86083,8 +86083,8 @@
   {
     "name": "iNatAg/entada_africana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86104,8 +86104,8 @@
   {
     "name": "iNatAg/enterolobium_cyclocarpum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86125,8 +86125,8 @@
   {
     "name": "iNatAg/epilobium_angustifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86146,8 +86146,8 @@
   {
     "name": "iNatAg/epilobium_ciliatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86167,8 +86167,8 @@
   {
     "name": "iNatAg/equisetum_arvense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86188,8 +86188,8 @@
   {
     "name": "iNatAg/equisetum_hyemale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86209,8 +86209,8 @@
   {
     "name": "iNatAg/equisetum_palustre",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86230,8 +86230,8 @@
   {
     "name": "iNatAg/equisetum_sylvaticum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86251,8 +86251,8 @@
   {
     "name": "iNatAg/equisetum_telmateia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86272,8 +86272,8 @@
   {
     "name": "iNatAg/eragrostis_amabilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86293,8 +86293,8 @@
   {
     "name": "iNatAg/eragrostis_barrelieri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86314,8 +86314,8 @@
   {
     "name": "iNatAg/eragrostis_capillaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86335,8 +86335,8 @@
   {
     "name": "iNatAg/eragrostis_chloromelas",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86356,8 +86356,8 @@
   {
     "name": "iNatAg/eragrostis_cilianensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86377,8 +86377,8 @@
   {
     "name": "iNatAg/eragrostis_curvula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86398,8 +86398,8 @@
   {
     "name": "iNatAg/eragrostis_interrupta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86419,8 +86419,8 @@
   {
     "name": "iNatAg/eragrostis_lehmanniana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86440,8 +86440,8 @@
   {
     "name": "iNatAg/eragrostis_minor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86461,8 +86461,8 @@
   {
     "name": "iNatAg/eragrostis_obtusa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86482,8 +86482,8 @@
   {
     "name": "iNatAg/eragrostis_pilosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86503,8 +86503,8 @@
   {
     "name": "iNatAg/eragrostis_racemosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86524,8 +86524,8 @@
   {
     "name": "iNatAg/eragrostis_superba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86545,8 +86545,8 @@
   {
     "name": "iNatAg/eragrostis_tef",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86566,8 +86566,8 @@
   {
     "name": "iNatAg/eragrostis_tremula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86587,8 +86587,8 @@
   {
     "name": "iNatAg/eragrostis_trichodes",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86608,8 +86608,8 @@
   {
     "name": "iNatAg/eragrostis_unioloides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86629,8 +86629,8 @@
   {
     "name": "iNatAg/eremochloa_ophiuroides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86650,8 +86650,8 @@
   {
     "name": "iNatAg/erigeron_canadensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86671,8 +86671,8 @@
   {
     "name": "iNatAg/erigeron_cascadensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86692,8 +86692,8 @@
   {
     "name": "iNatAg/erigeron_divaricatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86713,8 +86713,8 @@
   {
     "name": "iNatAg/erigeron_philadelphicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86734,8 +86734,8 @@
   {
     "name": "iNatAg/eriobotrya_japonica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86755,8 +86755,8 @@
   {
     "name": "iNatAg/eriochloa_punctata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86776,8 +86776,8 @@
   {
     "name": "iNatAg/eriogonum_deflexum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86797,8 +86797,8 @@
   {
     "name": "iNatAg/eriogonum_longifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86818,8 +86818,8 @@
   {
     "name": "iNatAg/eriosema_psoraleoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86839,8 +86839,8 @@
   {
     "name": "iNatAg/eruca_sativa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86860,8 +86860,8 @@
   {
     "name": "iNatAg/eryngium_campestre",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86881,8 +86881,8 @@
   {
     "name": "iNatAg/eryngium_yuccifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86902,8 +86902,8 @@
   {
     "name": "iNatAg/erysimum_cheiranthoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86923,8 +86923,8 @@
   {
     "name": "iNatAg/erysimum_hieracifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86944,8 +86944,8 @@
   {
     "name": "iNatAg/erysimum_hieraciifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86965,8 +86965,8 @@
   {
     "name": "iNatAg/erysimum_repandum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -86986,8 +86986,8 @@
   {
     "name": "iNatAg/erythrina_abyssinica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87007,8 +87007,8 @@
   {
     "name": "iNatAg/erythrina_caffra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87028,8 +87028,8 @@
   {
     "name": "iNatAg/erythrina_edulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87049,8 +87049,8 @@
   {
     "name": "iNatAg/erythrina_fusca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87070,8 +87070,8 @@
   {
     "name": "iNatAg/erythrina_poeppigiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87091,8 +87091,8 @@
   {
     "name": "iNatAg/erythrina_variegata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87112,8 +87112,8 @@
   {
     "name": "iNatAg/erythrina_vespertilio",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87133,8 +87133,8 @@
   {
     "name": "iNatAg/erythrophleum_chlorostachys",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87154,8 +87154,8 @@
   {
     "name": "iNatAg/erythroxylum_coca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87175,8 +87175,8 @@
   {
     "name": "iNatAg/eucalyptus_accedens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87196,8 +87196,8 @@
   {
     "name": "iNatAg/eucalyptus_agglomerata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87217,8 +87217,8 @@
   {
     "name": "iNatAg/eucalyptus_albens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87238,8 +87238,8 @@
   {
     "name": "iNatAg/eucalyptus_astringens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87259,8 +87259,8 @@
   {
     "name": "iNatAg/eucalyptus_bosistoana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87280,8 +87280,8 @@
   {
     "name": "iNatAg/eucalyptus_botryoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87301,8 +87301,8 @@
   {
     "name": "iNatAg/eucalyptus_brockwayi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87322,8 +87322,8 @@
   {
     "name": "iNatAg/eucalyptus_calophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87343,8 +87343,8 @@
   {
     "name": "iNatAg/eucalyptus_camaldulensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87364,8 +87364,8 @@
   {
     "name": "iNatAg/eucalyptus_cinerea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87385,8 +87385,8 @@
   {
     "name": "iNatAg/eucalyptus_citriodora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87406,8 +87406,8 @@
   {
     "name": "iNatAg/eucalyptus_cladocalyx",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87427,8 +87427,8 @@
   {
     "name": "iNatAg/eucalyptus_cloeziana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87448,8 +87448,8 @@
   {
     "name": "iNatAg/eucalyptus_consideniana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87469,8 +87469,8 @@
   {
     "name": "iNatAg/eucalyptus_cornuta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87490,8 +87490,8 @@
   {
     "name": "iNatAg/eucalyptus_crebra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87511,8 +87511,8 @@
   {
     "name": "iNatAg/eucalyptus_cypellocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87532,8 +87532,8 @@
   {
     "name": "iNatAg/eucalyptus_dalrympleana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87553,8 +87553,8 @@
   {
     "name": "iNatAg/eucalyptus_deglupta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87574,8 +87574,8 @@
   {
     "name": "iNatAg/eucalyptus_delegatensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87595,8 +87595,8 @@
   {
     "name": "iNatAg/eucalyptus_diversicolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87616,8 +87616,8 @@
   {
     "name": "iNatAg/eucalyptus_dumosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87637,8 +87637,8 @@
   {
     "name": "iNatAg/eucalyptus_elata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87658,8 +87658,8 @@
   {
     "name": "iNatAg/eucalyptus_eremophila",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87679,8 +87679,8 @@
   {
     "name": "iNatAg/eucalyptus_eugenioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87700,8 +87700,8 @@
   {
     "name": "iNatAg/eucalyptus_exserta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87721,8 +87721,8 @@
   {
     "name": "iNatAg/eucalyptus_fastigata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87742,8 +87742,8 @@
   {
     "name": "iNatAg/eucalyptus_fraxinoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87763,8 +87763,8 @@
   {
     "name": "iNatAg/eucalyptus_globoidea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87784,8 +87784,8 @@
   {
     "name": "iNatAg/eucalyptus_globulus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87805,8 +87805,8 @@
   {
     "name": "iNatAg/eucalyptus_gomphocephala",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87826,8 +87826,8 @@
   {
     "name": "iNatAg/eucalyptus_gongylocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87847,8 +87847,8 @@
   {
     "name": "iNatAg/eucalyptus_grandis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87868,8 +87868,8 @@
   {
     "name": "iNatAg/eucalyptus_guilfoylei",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87889,8 +87889,8 @@
   {
     "name": "iNatAg/eucalyptus_gummifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87910,8 +87910,8 @@
   {
     "name": "iNatAg/eucalyptus_intertexta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87931,8 +87931,8 @@
   {
     "name": "iNatAg/eucalyptus_jacksonii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87952,8 +87952,8 @@
   {
     "name": "iNatAg/eucalyptus_johnstonii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87973,8 +87973,8 @@
   {
     "name": "iNatAg/eucalyptus_kessellii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -87994,8 +87994,8 @@
   {
     "name": "iNatAg/eucalyptus_laophila",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88015,8 +88015,8 @@
   {
     "name": "iNatAg/eucalyptus_largiflorens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88036,8 +88036,8 @@
   {
     "name": "iNatAg/eucalyptus_leucoxylon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88057,8 +88057,8 @@
   {
     "name": "iNatAg/eucalyptus_longifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88078,8 +88078,8 @@
   {
     "name": "iNatAg/eucalyptus_loxophleba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88099,8 +88099,8 @@
   {
     "name": "iNatAg/eucalyptus_maculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88120,8 +88120,8 @@
   {
     "name": "iNatAg/eucalyptus_marginata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88141,8 +88141,8 @@
   {
     "name": "iNatAg/eucalyptus_melliodora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88162,8 +88162,8 @@
   {
     "name": "iNatAg/eucalyptus_microcarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88183,8 +88183,8 @@
   {
     "name": "iNatAg/eucalyptus_microcorys",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88204,8 +88204,8 @@
   {
     "name": "iNatAg/eucalyptus_microtheca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88225,8 +88225,8 @@
   {
     "name": "iNatAg/eucalyptus_mitchelliana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88246,8 +88246,8 @@
   {
     "name": "iNatAg/eucalyptus_moluccana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88267,8 +88267,8 @@
   {
     "name": "iNatAg/eucalyptus_muelleriana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88288,8 +88288,8 @@
   {
     "name": "iNatAg/eucalyptus_nigrifunda",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88309,8 +88309,8 @@
   {
     "name": "iNatAg/eucalyptus_niphophila",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88330,8 +88330,8 @@
   {
     "name": "iNatAg/eucalyptus_nitens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88351,8 +88351,8 @@
   {
     "name": "iNatAg/eucalyptus_obliqua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88372,8 +88372,8 @@
   {
     "name": "iNatAg/eucalyptus_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88393,8 +88393,8 @@
   {
     "name": "iNatAg/eucalyptus_ochrophloia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88414,8 +88414,8 @@
   {
     "name": "iNatAg/eucalyptus_oreades",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88435,8 +88435,8 @@
   {
     "name": "iNatAg/eucalyptus_paniculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88456,8 +88456,8 @@
   {
     "name": "iNatAg/eucalyptus_papuana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88477,8 +88477,8 @@
   {
     "name": "iNatAg/eucalyptus_patens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88498,8 +88498,8 @@
   {
     "name": "iNatAg/eucalyptus_pauciflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88519,8 +88519,8 @@
   {
     "name": "iNatAg/eucalyptus_pellita",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88540,8 +88540,8 @@
   {
     "name": "iNatAg/eucalyptus_phoenicea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88561,8 +88561,8 @@
   {
     "name": "iNatAg/eucalyptus_pilularis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88582,8 +88582,8 @@
   {
     "name": "iNatAg/eucalyptus_piperita",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88603,8 +88603,8 @@
   {
     "name": "iNatAg/eucalyptus_planchoniana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88624,8 +88624,8 @@
   {
     "name": "iNatAg/eucalyptus_pleurocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88645,8 +88645,8 @@
   {
     "name": "iNatAg/eucalyptus_polyanthemos",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88666,8 +88666,8 @@
   {
     "name": "iNatAg/eucalyptus_populnea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88687,8 +88687,8 @@
   {
     "name": "iNatAg/eucalyptus_propinqua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88708,8 +88708,8 @@
   {
     "name": "iNatAg/eucalyptus_pulchella",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88729,8 +88729,8 @@
   {
     "name": "iNatAg/eucalyptus_punctata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88750,8 +88750,8 @@
   {
     "name": "iNatAg/eucalyptus_pyrocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88771,8 +88771,8 @@
   {
     "name": "iNatAg/eucalyptus_quadrangulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88792,8 +88792,8 @@
   {
     "name": "iNatAg/eucalyptus_regnans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88813,8 +88813,8 @@
   {
     "name": "iNatAg/eucalyptus_resinifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88834,8 +88834,8 @@
   {
     "name": "iNatAg/eucalyptus_robusta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88855,8 +88855,8 @@
   {
     "name": "iNatAg/eucalyptus_rubida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88876,8 +88876,8 @@
   {
     "name": "iNatAg/eucalyptus_rudis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88897,8 +88897,8 @@
   {
     "name": "iNatAg/eucalyptus_saligna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88918,8 +88918,8 @@
   {
     "name": "iNatAg/eucalyptus_salmonophloia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88939,8 +88939,8 @@
   {
     "name": "iNatAg/eucalyptus_salubris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88960,8 +88960,8 @@
   {
     "name": "iNatAg/eucalyptus_sargentii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -88981,8 +88981,8 @@
   {
     "name": "iNatAg/eucalyptus_scias",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89002,8 +89002,8 @@
   {
     "name": "iNatAg/eucalyptus_sideroxylon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89023,8 +89023,8 @@
   {
     "name": "iNatAg/eucalyptus_sieberi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89044,8 +89044,8 @@
   {
     "name": "iNatAg/eucalyptus_socialis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89065,8 +89065,8 @@
   {
     "name": "iNatAg/eucalyptus_subcrenulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89086,8 +89086,8 @@
   {
     "name": "iNatAg/eucalyptus_tereticornis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89107,8 +89107,8 @@
   {
     "name": "iNatAg/eucalyptus_thozetiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89128,8 +89128,8 @@
   {
     "name": "iNatAg/eucalyptus_transcontinentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89149,8 +89149,8 @@
   {
     "name": "iNatAg/eucalyptus_trivalva",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89170,8 +89170,8 @@
   {
     "name": "iNatAg/eucalyptus_urnigera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89191,8 +89191,8 @@
   {
     "name": "iNatAg/eucalyptus_urophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89212,8 +89212,8 @@
   {
     "name": "iNatAg/eucalyptus_utilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89233,8 +89233,8 @@
   {
     "name": "iNatAg/eucalyptus_viminalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89254,8 +89254,8 @@
   {
     "name": "iNatAg/eucalyptus_wandoo",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89275,8 +89275,8 @@
   {
     "name": "iNatAg/eucalyptus_woollsiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89296,8 +89296,8 @@
   {
     "name": "iNatAg/eucryphia_lucida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89317,8 +89317,8 @@
   {
     "name": "iNatAg/eugenia_aromatica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89338,8 +89338,8 @@
   {
     "name": "iNatAg/eugenia_stipitata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89359,8 +89359,8 @@
   {
     "name": "iNatAg/eugenia_uniflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89380,8 +89380,8 @@
   {
     "name": "iNatAg/euonymus_atropurpureus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89401,8 +89401,8 @@
   {
     "name": "iNatAg/euonymus_europaeus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89422,8 +89422,8 @@
   {
     "name": "iNatAg/euonymus_japonicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89443,8 +89443,8 @@
   {
     "name": "iNatAg/eupatorium_album",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89464,8 +89464,8 @@
   {
     "name": "iNatAg/eupatorium_altissimum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89485,8 +89485,8 @@
   {
     "name": "iNatAg/eupatorium_cannabinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89506,8 +89506,8 @@
   {
     "name": "iNatAg/eupatorium_compositifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89527,8 +89527,8 @@
   {
     "name": "iNatAg/eupatorium_hyssopifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89548,8 +89548,8 @@
   {
     "name": "iNatAg/eupatorium_maculatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89569,8 +89569,8 @@
   {
     "name": "iNatAg/eupatorium_perfoliatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89590,8 +89590,8 @@
   {
     "name": "iNatAg/eupatorium_purpureum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89611,8 +89611,8 @@
   {
     "name": "iNatAg/eupatorium_serotinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89632,8 +89632,8 @@
   {
     "name": "iNatAg/euphorbia_cyathophora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89653,8 +89653,8 @@
   {
     "name": "iNatAg/euphorbia_cyparissias",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89674,8 +89674,8 @@
   {
     "name": "iNatAg/euphorbia_dendroides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89695,8 +89695,8 @@
   {
     "name": "iNatAg/euphorbia_epicyparissias",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89716,8 +89716,8 @@
   {
     "name": "iNatAg/euphorbia_esula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89737,8 +89737,8 @@
   {
     "name": "iNatAg/euphorbia_helioscopia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89758,8 +89758,8 @@
   {
     "name": "iNatAg/euphorbia_heterophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89779,8 +89779,8 @@
   {
     "name": "iNatAg/euphorbia_hirsuta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89800,8 +89800,8 @@
   {
     "name": "iNatAg/euphorbia_hirta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89821,8 +89821,8 @@
   {
     "name": "iNatAg/euphorbia_hyssopifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89842,8 +89842,8 @@
   {
     "name": "iNatAg/euphorbia_lathyris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89863,8 +89863,8 @@
   {
     "name": "iNatAg/euphorbia_lathyrus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89884,8 +89884,8 @@
   {
     "name": "iNatAg/euphorbia_maculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89905,8 +89905,8 @@
   {
     "name": "iNatAg/euphorbia_marginata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89926,8 +89926,8 @@
   {
     "name": "iNatAg/euphorbia_nutans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89947,8 +89947,8 @@
   {
     "name": "iNatAg/euphorbia_peplis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89968,8 +89968,8 @@
   {
     "name": "iNatAg/euphorbia_peplus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -89989,8 +89989,8 @@
   {
     "name": "iNatAg/euphorbia_platyphyllos",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90010,8 +90010,8 @@
   {
     "name": "iNatAg/euphorbia_prostata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90031,8 +90031,8 @@
   {
     "name": "iNatAg/euphorbia_prostrata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90052,8 +90052,8 @@
   {
     "name": "iNatAg/euphorbia_serphyllifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90073,8 +90073,8 @@
   {
     "name": "iNatAg/euphorbia_serpyllifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90094,8 +90094,8 @@
   {
     "name": "iNatAg/euphorbia_serrata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90115,8 +90115,8 @@
   {
     "name": "iNatAg/euphorbia_serrulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90136,8 +90136,8 @@
   {
     "name": "iNatAg/euphorbia_spathulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90157,8 +90157,8 @@
   {
     "name": "iNatAg/euphorbia_terracina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90178,8 +90178,8 @@
   {
     "name": "iNatAg/euphorbia_tirucalli",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90199,8 +90199,8 @@
   {
     "name": "iNatAg/euphorbia_vermiculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90220,8 +90220,8 @@
   {
     "name": "iNatAg/eurycoma_longifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90241,8 +90241,8 @@
   {
     "name": "iNatAg/eusideroxylon_zwageri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90262,8 +90262,8 @@
   {
     "name": "iNatAg/eustachys_paspaloides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90283,8 +90283,8 @@
   {
     "name": "iNatAg/euterpe_edulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90304,8 +90304,8 @@
   {
     "name": "iNatAg/euterpe_oleracea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90325,8 +90325,8 @@
   {
     "name": "iNatAg/euthamia_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90346,8 +90346,8 @@
   {
     "name": "iNatAg/evax_multicaulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90367,8 +90367,8 @@
   {
     "name": "iNatAg/evonymus_europaeus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90388,8 +90388,8 @@
   {
     "name": "iNatAg/excoecaria_agallocha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90409,8 +90409,8 @@
   {
     "name": "iNatAg/fagopyrum_esculentum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90430,8 +90430,8 @@
   {
     "name": "iNatAg/fagopyrum_tataricum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90451,8 +90451,8 @@
   {
     "name": "iNatAg/fagraea_fragrans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90472,8 +90472,8 @@
   {
     "name": "iNatAg/fagus_grandifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90493,8 +90493,8 @@
   {
     "name": "iNatAg/fagus_sylvatica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90514,8 +90514,8 @@
   {
     "name": "iNatAg/faidherbia_albida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90535,8 +90535,8 @@
   {
     "name": "iNatAg/faurea_saligna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90556,8 +90556,8 @@
   {
     "name": "iNatAg/feijoa_sellowiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90577,8 +90577,8 @@
   {
     "name": "iNatAg/festuca_arundinacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90598,8 +90598,8 @@
   {
     "name": "iNatAg/festuca_gigantea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90619,8 +90619,8 @@
   {
     "name": "iNatAg/festuca_idahoensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90640,8 +90640,8 @@
   {
     "name": "iNatAg/festuca_microstachys",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90661,8 +90661,8 @@
   {
     "name": "iNatAg/festuca_myuros",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90682,8 +90682,8 @@
   {
     "name": "iNatAg/festuca_ovina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90703,8 +90703,8 @@
   {
     "name": "iNatAg/festuca_pratensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90724,8 +90724,8 @@
   {
     "name": "iNatAg/festuca_rubra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90745,8 +90745,8 @@
   {
     "name": "iNatAg/festuca_scabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90766,8 +90766,8 @@
   {
     "name": "iNatAg/fibraurea_tinctoria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90787,8 +90787,8 @@
   {
     "name": "iNatAg/ficus_abutilifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90808,8 +90808,8 @@
   {
     "name": "iNatAg/ficus_auriculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90829,8 +90829,8 @@
   {
     "name": "iNatAg/ficus_benghalensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90850,8 +90850,8 @@
   {
     "name": "iNatAg/ficus_carica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90871,8 +90871,8 @@
   {
     "name": "iNatAg/ficus_elastica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90892,8 +90892,8 @@
   {
     "name": "iNatAg/ficus_glumosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90913,8 +90913,8 @@
   {
     "name": "iNatAg/ficus_macrophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90934,8 +90934,8 @@
   {
     "name": "iNatAg/ficus_sycomorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90955,8 +90955,8 @@
   {
     "name": "iNatAg/ficus_thonningii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90976,8 +90976,8 @@
   {
     "name": "iNatAg/filago_gallica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -90997,8 +90997,8 @@
   {
     "name": "iNatAg/filipendula_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91018,8 +91018,8 @@
   {
     "name": "iNatAg/flacourtia_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91039,8 +91039,8 @@
   {
     "name": "iNatAg/flemingia_macrophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91060,8 +91060,8 @@
   {
     "name": "iNatAg/flindersia_bourjotiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91081,8 +91081,8 @@
   {
     "name": "iNatAg/flindersia_brayleyana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91102,8 +91102,8 @@
   {
     "name": "iNatAg/flindersia_pimenteliana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91123,8 +91123,8 @@
   {
     "name": "iNatAg/foeniculum_vulgare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91144,8 +91144,8 @@
   {
     "name": "iNatAg/fortunella_hindsii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91165,8 +91165,8 @@
   {
     "name": "iNatAg/fortunella_japonica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91186,8 +91186,8 @@
   {
     "name": "iNatAg/fortunella_margarita",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91207,8 +91207,8 @@
   {
     "name": "iNatAg/fragaria_ananassa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91228,8 +91228,8 @@
   {
     "name": "iNatAg/fragaria_chiloensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91249,8 +91249,8 @@
   {
     "name": "iNatAg/fragaria_vesca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91270,8 +91270,8 @@
   {
     "name": "iNatAg/fragaria_virginiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91291,8 +91291,8 @@
   {
     "name": "iNatAg/frangula_alnus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91312,8 +91312,8 @@
   {
     "name": "iNatAg/fraxinus_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91333,8 +91333,8 @@
   {
     "name": "iNatAg/fraxinus_excelsior",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91354,8 +91354,8 @@
   {
     "name": "iNatAg/frithia_humilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91375,8 +91375,8 @@
   {
     "name": "iNatAg/fuirena_simplex",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91396,8 +91396,8 @@
   {
     "name": "iNatAg/fumaria_capreolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91417,8 +91417,8 @@
   {
     "name": "iNatAg/fumaria_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91438,8 +91438,8 @@
   {
     "name": "iNatAg/fumaria_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91459,8 +91459,8 @@
   {
     "name": "iNatAg/gaillardia_pulchella",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91480,8 +91480,8 @@
   {
     "name": "iNatAg/galactia_marginalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91501,8 +91501,8 @@
   {
     "name": "iNatAg/galactia_striata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91522,8 +91522,8 @@
   {
     "name": "iNatAg/galega_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91543,8 +91543,8 @@
   {
     "name": "iNatAg/galega_orientalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91564,8 +91564,8 @@
   {
     "name": "iNatAg/galeopsis_ladanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91585,8 +91585,8 @@
   {
     "name": "iNatAg/galeopsis_tetrahit",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91606,8 +91606,8 @@
   {
     "name": "iNatAg/galinsoga_quadriradiata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91627,8 +91627,8 @@
   {
     "name": "iNatAg/galium_aparine",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91648,8 +91648,8 @@
   {
     "name": "iNatAg/galium_mollugo",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91669,8 +91669,8 @@
   {
     "name": "iNatAg/galium_paniculatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91690,8 +91690,8 @@
   {
     "name": "iNatAg/galium_parisiense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91711,8 +91711,8 @@
   {
     "name": "iNatAg/galium_saxatile",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91732,8 +91732,8 @@
   {
     "name": "iNatAg/galium_spurium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91753,8 +91753,8 @@
   {
     "name": "iNatAg/galium_tricornutum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91774,8 +91774,8 @@
   {
     "name": "iNatAg/galium_verum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91795,8 +91795,8 @@
   {
     "name": "iNatAg/garcinia_dulcis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91816,8 +91816,8 @@
   {
     "name": "iNatAg/garcinia_mangostana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91837,8 +91837,8 @@
   {
     "name": "iNatAg/garcinia_multiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91858,8 +91858,8 @@
   {
     "name": "iNatAg/garcinia_xanthochymus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91879,8 +91879,8 @@
   {
     "name": "iNatAg/garuga_pinnata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91900,8 +91900,8 @@
   {
     "name": "iNatAg/gaultheria_procumbens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91921,8 +91921,8 @@
   {
     "name": "iNatAg/gaura_biennis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91942,8 +91942,8 @@
   {
     "name": "iNatAg/geissois_benthamii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91963,8 +91963,8 @@
   {
     "name": "iNatAg/genipa_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -91984,8 +91984,8 @@
   {
     "name": "iNatAg/genista_canariensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92005,8 +92005,8 @@
   {
     "name": "iNatAg/genista_tinctoria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92026,8 +92026,8 @@
   {
     "name": "iNatAg/gentiana_acaulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92047,8 +92047,8 @@
   {
     "name": "iNatAg/gentiana_lutea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92068,8 +92068,8 @@
   {
     "name": "iNatAg/geranium_carolinianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92089,8 +92089,8 @@
   {
     "name": "iNatAg/geranium_dissectum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92110,8 +92110,8 @@
   {
     "name": "iNatAg/geranium_molle",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92131,8 +92131,8 @@
   {
     "name": "iNatAg/geranium_pratense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92152,8 +92152,8 @@
   {
     "name": "iNatAg/geranium_pusillum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92173,8 +92173,8 @@
   {
     "name": "iNatAg/geranium_robertianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92194,8 +92194,8 @@
   {
     "name": "iNatAg/girardinia_diversifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92215,8 +92215,8 @@
   {
     "name": "iNatAg/glechoma_hederacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92236,8 +92236,8 @@
   {
     "name": "iNatAg/glecoma_hederacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92257,8 +92257,8 @@
   {
     "name": "iNatAg/gleditsia_triacanthos",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92278,8 +92278,8 @@
   {
     "name": "iNatAg/gliricidia_sepium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92299,8 +92299,8 @@
   {
     "name": "iNatAg/globularia_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92320,8 +92320,8 @@
   {
     "name": "iNatAg/glyceria_fluitans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92341,8 +92341,8 @@
   {
     "name": "iNatAg/glyceria_septentrionalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92362,8 +92362,8 @@
   {
     "name": "iNatAg/glycine_max",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92383,8 +92383,8 @@
   {
     "name": "iNatAg/glycyrrhiza_glabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92404,8 +92404,8 @@
   {
     "name": "iNatAg/glycyrrhiza_lepidota",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92425,8 +92425,8 @@
   {
     "name": "iNatAg/gmelina_arborea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92446,8 +92446,8 @@
   {
     "name": "iNatAg/gmelina_leichhardtii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92467,8 +92467,8 @@
   {
     "name": "iNatAg/gnaphalium_calviceps",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92488,8 +92488,8 @@
   {
     "name": "iNatAg/gnaphalium_luteo-album",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92509,8 +92509,8 @@
   {
     "name": "iNatAg/gnaphalium_luteoalbum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92530,8 +92530,8 @@
   {
     "name": "iNatAg/gnaphalium_palustre",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92551,8 +92551,8 @@
   {
     "name": "iNatAg/gnaphalium_pensylvanicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92572,8 +92572,8 @@
   {
     "name": "iNatAg/gnaphalium_purpureum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92593,8 +92593,8 @@
   {
     "name": "iNatAg/gnaphalium_uliginosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92614,8 +92614,8 @@
   {
     "name": "iNatAg/gossypium_barbadense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92635,8 +92635,8 @@
   {
     "name": "iNatAg/gossypium_herbaceum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92656,8 +92656,8 @@
   {
     "name": "iNatAg/gossypium_hirsutum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92677,8 +92677,8 @@
   {
     "name": "iNatAg/grevillea_parallela",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92698,8 +92698,8 @@
   {
     "name": "iNatAg/grevillea_robusta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92719,8 +92719,8 @@
   {
     "name": "iNatAg/grewia_asiatica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92740,8 +92740,8 @@
   {
     "name": "iNatAg/grewia_bicolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92761,8 +92761,8 @@
   {
     "name": "iNatAg/grewia_tiliifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92782,8 +92782,8 @@
   {
     "name": "iNatAg/guaiacum_officinale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92803,8 +92803,8 @@
   {
     "name": "iNatAg/guaiacum_sanctum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92824,8 +92824,8 @@
   {
     "name": "iNatAg/guazuma_ulmifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92845,8 +92845,8 @@
   {
     "name": "iNatAg/guizotia_abyssinica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92866,8 +92866,8 @@
   {
     "name": "iNatAg/gunnera_tinctoria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92887,8 +92887,8 @@
   {
     "name": "iNatAg/gypsophila_paniculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92908,8 +92908,8 @@
   {
     "name": "iNatAg/hagenia_abyssinica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92929,8 +92929,8 @@
   {
     "name": "iNatAg/hamamelis_virginiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92950,8 +92950,8 @@
   {
     "name": "iNatAg/hardwickia_binata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92971,8 +92971,8 @@
   {
     "name": "iNatAg/harpagophytum_procumbens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -92992,8 +92992,8 @@
   {
     "name": "iNatAg/harpochloa_falx",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93013,8 +93013,8 @@
   {
     "name": "iNatAg/harungana_madagascariensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93034,8 +93034,8 @@
   {
     "name": "iNatAg/hedera_helix",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93055,8 +93055,8 @@
   {
     "name": "iNatAg/hedysarum_coronarium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93076,8 +93076,8 @@
   {
     "name": "iNatAg/hedysarum_pallidum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93097,8 +93097,8 @@
   {
     "name": "iNatAg/hedysarum_spinosissimum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93118,8 +93118,8 @@
   {
     "name": "iNatAg/helenium_autumnale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93139,8 +93139,8 @@
   {
     "name": "iNatAg/helenium_tenuifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93160,8 +93160,8 @@
   {
     "name": "iNatAg/helianthus_annus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93181,8 +93181,8 @@
   {
     "name": "iNatAg/helianthus_annuus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93202,8 +93202,8 @@
   {
     "name": "iNatAg/helianthus_ciliaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93223,8 +93223,8 @@
   {
     "name": "iNatAg/helianthus_pauciflorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93244,8 +93244,8 @@
   {
     "name": "iNatAg/helianthus_petiolaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93265,8 +93265,8 @@
   {
     "name": "iNatAg/helianthus_tuberosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93286,8 +93286,8 @@
   {
     "name": "iNatAg/helictotrichon_turgidulum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93307,8 +93307,8 @@
   {
     "name": "iNatAg/heliotropium_amplexicaule",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93328,8 +93328,8 @@
   {
     "name": "iNatAg/heliotropium_curassavicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93349,8 +93349,8 @@
   {
     "name": "iNatAg/heliotropium_europaeum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93370,8 +93370,8 @@
   {
     "name": "iNatAg/hemarthria_altissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93391,8 +93391,8 @@
   {
     "name": "iNatAg/hemizonia_congesta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93412,8 +93412,8 @@
   {
     "name": "iNatAg/heracleum_sphondylium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93433,8 +93433,8 @@
   {
     "name": "iNatAg/heritiera_littoralis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93454,8 +93454,8 @@
   {
     "name": "iNatAg/heteropogon_contortus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93475,8 +93475,8 @@
   {
     "name": "iNatAg/heterotheca_grandiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93496,8 +93496,8 @@
   {
     "name": "iNatAg/heuchera_mexicana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93517,8 +93517,8 @@
   {
     "name": "iNatAg/hevea_brasiliensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93538,8 +93538,8 @@
   {
     "name": "iNatAg/hibiscus_cannabinus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93559,8 +93559,8 @@
   {
     "name": "iNatAg/hibiscus_sabdariffa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93580,8 +93580,8 @@
   {
     "name": "iNatAg/hibiscus_syriacus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93601,8 +93601,8 @@
   {
     "name": "iNatAg/hibiscus_tiliaceus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93622,8 +93622,8 @@
   {
     "name": "iNatAg/hibiscus_tilliaceus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93643,8 +93643,8 @@
   {
     "name": "iNatAg/hieracium_aurantiacum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93664,8 +93664,8 @@
   {
     "name": "iNatAg/hieracium_gronovii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93685,8 +93685,8 @@
   {
     "name": "iNatAg/hieracium_lachenalii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93706,8 +93706,8 @@
   {
     "name": "iNatAg/hieracium_laevigatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93727,8 +93727,8 @@
   {
     "name": "iNatAg/hieracium_murorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93748,8 +93748,8 @@
   {
     "name": "iNatAg/hieracium_pilosella",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93769,8 +93769,8 @@
   {
     "name": "iNatAg/hieracium_piloselloides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93790,8 +93790,8 @@
   {
     "name": "iNatAg/hieracium_umbellatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93811,8 +93811,8 @@
   {
     "name": "iNatAg/hieracium_venosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93832,8 +93832,8 @@
   {
     "name": "iNatAg/hieracium_vulgatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93853,8 +93853,8 @@
   {
     "name": "iNatAg/hierochloe_odorata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93874,8 +93874,8 @@
   {
     "name": "iNatAg/hilaria_jamesii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93895,8 +93895,8 @@
   {
     "name": "iNatAg/hilaria_mutica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93916,8 +93916,8 @@
   {
     "name": "iNatAg/hippophae_rhamnoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93937,8 +93937,8 @@
   {
     "name": "iNatAg/hippophae_salicifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93958,8 +93958,8 @@
   {
     "name": "iNatAg/hippuris_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -93979,8 +93979,8 @@
   {
     "name": "iNatAg/holcus_lanatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94000,8 +94000,8 @@
   {
     "name": "iNatAg/holcus_mollis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94021,8 +94021,8 @@
   {
     "name": "iNatAg/hopea_odorata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94042,8 +94042,8 @@
   {
     "name": "iNatAg/hopea_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94063,8 +94063,8 @@
   {
     "name": "iNatAg/hopea_wightiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94084,8 +94084,8 @@
   {
     "name": "iNatAg/hordeum_brachyantherum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94105,8 +94105,8 @@
   {
     "name": "iNatAg/hordeum_brevisubulatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94126,8 +94126,8 @@
   {
     "name": "iNatAg/hordeum_bulbosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94147,8 +94147,8 @@
   {
     "name": "iNatAg/hordeum_distichon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94168,8 +94168,8 @@
   {
     "name": "iNatAg/hordeum_geniculatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94189,8 +94189,8 @@
   {
     "name": "iNatAg/hordeum_jubatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94210,8 +94210,8 @@
   {
     "name": "iNatAg/hordeum_murinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94231,8 +94231,8 @@
   {
     "name": "iNatAg/hordeum_vulgare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94252,8 +94252,8 @@
   {
     "name": "iNatAg/houstonia_caerulea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94273,8 +94273,8 @@
   {
     "name": "iNatAg/humulus_lupulus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94294,8 +94294,8 @@
   {
     "name": "iNatAg/hydnocarpus_alpina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94315,8 +94315,8 @@
   {
     "name": "iNatAg/hydrocotyle_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94336,8 +94336,8 @@
   {
     "name": "iNatAg/hydrocotyle_mexicana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94357,8 +94357,8 @@
   {
     "name": "iNatAg/hydrocotyle_ranunculoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94378,8 +94378,8 @@
   {
     "name": "iNatAg/hydrocotyle_sibthorpioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94399,8 +94399,8 @@
   {
     "name": "iNatAg/hydrocotyle_umbellata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94420,8 +94420,8 @@
   {
     "name": "iNatAg/hydrocotyle_verticillata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94441,8 +94441,8 @@
   {
     "name": "iNatAg/hydrolea_uniflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94462,8 +94462,8 @@
   {
     "name": "iNatAg/hylocereus_undatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94483,8 +94483,8 @@
   {
     "name": "iNatAg/hymenaea_courbaril",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94504,8 +94504,8 @@
   {
     "name": "iNatAg/hymenopappus_scabiosaeus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94525,8 +94525,8 @@
   {
     "name": "iNatAg/hymenoxys_odorata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94546,8 +94546,8 @@
   {
     "name": "iNatAg/hyosciamus_niger",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94567,8 +94567,8 @@
   {
     "name": "iNatAg/hyoscyamus_niger",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94588,8 +94588,8 @@
   {
     "name": "iNatAg/hyparrhenia_dregeana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94609,8 +94609,8 @@
   {
     "name": "iNatAg/hyparrhenia_filipendula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94630,8 +94630,8 @@
   {
     "name": "iNatAg/hyparrhenia_hirta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94651,8 +94651,8 @@
   {
     "name": "iNatAg/hyparrhenia_rufa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94672,8 +94672,8 @@
   {
     "name": "iNatAg/hypericum_canadense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94693,8 +94693,8 @@
   {
     "name": "iNatAg/hypericum_canariense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94714,8 +94714,8 @@
   {
     "name": "iNatAg/hypericum_mutilum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94735,8 +94735,8 @@
   {
     "name": "iNatAg/hypericum_mutlium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94756,8 +94756,8 @@
   {
     "name": "iNatAg/hypericum_perforatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94777,8 +94777,8 @@
   {
     "name": "iNatAg/hypericum_prolificum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94798,8 +94798,8 @@
   {
     "name": "iNatAg/hypericum_punctatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94819,8 +94819,8 @@
   {
     "name": "iNatAg/hyperthelia_dissoluta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94840,8 +94840,8 @@
   {
     "name": "iNatAg/hyphaene_compressa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94861,8 +94861,8 @@
   {
     "name": "iNatAg/hyphaene_thebaica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94882,8 +94882,8 @@
   {
     "name": "iNatAg/hypochaeris_glabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94903,8 +94903,8 @@
   {
     "name": "iNatAg/hypochaeris_radicata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94924,8 +94924,8 @@
   {
     "name": "iNatAg/hypoxis_hemerocallidea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94945,8 +94945,8 @@
   {
     "name": "iNatAg/hyssopus_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94966,8 +94966,8 @@
   {
     "name": "iNatAg/ilex_aquifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -94987,8 +94987,8 @@
   {
     "name": "iNatAg/ilex_dipyrena",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95008,8 +95008,8 @@
   {
     "name": "iNatAg/ilex_paraguariensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95029,8 +95029,8 @@
   {
     "name": "iNatAg/impatiens_balsamina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95050,8 +95050,8 @@
   {
     "name": "iNatAg/impatiens_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95071,8 +95071,8 @@
   {
     "name": "iNatAg/imperata_brevifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95092,8 +95092,8 @@
   {
     "name": "iNatAg/imperata_cylindrica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95113,8 +95113,8 @@
   {
     "name": "iNatAg/indigofera_arrecta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95134,8 +95134,8 @@
   {
     "name": "iNatAg/indigofera_hirsuta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95155,8 +95155,8 @@
   {
     "name": "iNatAg/indigofera_oblongifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95176,8 +95176,8 @@
   {
     "name": "iNatAg/indigofera_schimperi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95197,8 +95197,8 @@
   {
     "name": "iNatAg/indigofera_spicata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95218,8 +95218,8 @@
   {
     "name": "iNatAg/indigofera_suffruticosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95239,8 +95239,8 @@
   {
     "name": "iNatAg/indigofera_tinctoria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95260,8 +95260,8 @@
   {
     "name": "iNatAg/inga_edulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95281,8 +95281,8 @@
   {
     "name": "iNatAg/inga_vera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95302,8 +95302,8 @@
   {
     "name": "iNatAg/intsia_bijuga",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95323,8 +95323,8 @@
   {
     "name": "iNatAg/inula_britannica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95344,8 +95344,8 @@
   {
     "name": "iNatAg/inula_helenium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95365,8 +95365,8 @@
   {
     "name": "iNatAg/ipomoea_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95386,8 +95386,8 @@
   {
     "name": "iNatAg/ipomoea_aquatica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95407,8 +95407,8 @@
   {
     "name": "iNatAg/ipomoea_batatas",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95428,8 +95428,8 @@
   {
     "name": "iNatAg/ipomoea_coccinea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95449,8 +95449,8 @@
   {
     "name": "iNatAg/ipomoea_hederifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95470,8 +95470,8 @@
   {
     "name": "iNatAg/ipomoea_lacunosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95491,8 +95491,8 @@
   {
     "name": "iNatAg/ipomoea_quamoclit",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95512,8 +95512,8 @@
   {
     "name": "iNatAg/ipomoea_tricolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95533,8 +95533,8 @@
   {
     "name": "iNatAg/ipomoea_triloba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95554,8 +95554,8 @@
   {
     "name": "iNatAg/ipomoea_turbinata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95575,8 +95575,8 @@
   {
     "name": "iNatAg/iris_germanica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95596,8 +95596,8 @@
   {
     "name": "iNatAg/iris_missouriensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95617,8 +95617,8 @@
   {
     "name": "iNatAg/iris_pseudacorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95638,8 +95638,8 @@
   {
     "name": "iNatAg/iris_pseudoacorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95659,8 +95659,8 @@
   {
     "name": "iNatAg/iris_virginica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95680,8 +95680,8 @@
   {
     "name": "iNatAg/isatis_tinctoria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95701,8 +95701,8 @@
   {
     "name": "iNatAg/ischaemum_ciliare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95722,8 +95722,8 @@
   {
     "name": "iNatAg/ischaemum_muticum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95743,8 +95743,8 @@
   {
     "name": "iNatAg/ischaemum_rugosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95764,8 +95764,8 @@
   {
     "name": "iNatAg/iseilema_vaginiflorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95785,8 +95785,8 @@
   {
     "name": "iNatAg/iva_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95806,8 +95806,8 @@
   {
     "name": "iNatAg/iva_annua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95827,8 +95827,8 @@
   {
     "name": "iNatAg/jacaranda_copaia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95848,8 +95848,8 @@
   {
     "name": "iNatAg/jacaranda_mimosifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95869,8 +95869,8 @@
   {
     "name": "iNatAg/jatropha_curcas",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95890,8 +95890,8 @@
   {
     "name": "iNatAg/jatropha_gossypifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95911,8 +95911,8 @@
   {
     "name": "iNatAg/jatropha_gossypiifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95932,8 +95932,8 @@
   {
     "name": "iNatAg/juglans_hindsii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95953,8 +95953,8 @@
   {
     "name": "iNatAg/juglans_nigra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95974,8 +95974,8 @@
   {
     "name": "iNatAg/juglans_regia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -95995,8 +95995,8 @@
   {
     "name": "iNatAg/juncus_bufonius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96016,8 +96016,8 @@
   {
     "name": "iNatAg/juncus_effusus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96037,8 +96037,8 @@
   {
     "name": "iNatAg/juniperus_communis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96058,8 +96058,8 @@
   {
     "name": "iNatAg/juniperus_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96079,8 +96079,8 @@
   {
     "name": "iNatAg/juniperus_pinchotii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96100,8 +96100,8 @@
   {
     "name": "iNatAg/juniperus_procera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96121,8 +96121,8 @@
   {
     "name": "iNatAg/juniperus_sabina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96142,8 +96142,8 @@
   {
     "name": "iNatAg/justicia_adhatoda",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96163,8 +96163,8 @@
   {
     "name": "iNatAg/kalmia_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96184,8 +96184,8 @@
   {
     "name": "iNatAg/khaya_anthotheca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96205,8 +96205,8 @@
   {
     "name": "iNatAg/khaya_senegalensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96226,8 +96226,8 @@
   {
     "name": "iNatAg/kigelia_pinnata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96247,8 +96247,8 @@
   {
     "name": "iNatAg/kyllinga_gracillima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96268,8 +96268,8 @@
   {
     "name": "iNatAg/kyllinga_odorata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96289,8 +96289,8 @@
   {
     "name": "iNatAg/lablab_purpureus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96310,8 +96310,8 @@
   {
     "name": "iNatAg/lactuca_canadensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96331,8 +96331,8 @@
   {
     "name": "iNatAg/lactuca_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96352,8 +96352,8 @@
   {
     "name": "iNatAg/lactuca_saligna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96373,8 +96373,8 @@
   {
     "name": "iNatAg/lactuca_serriola",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96394,8 +96394,8 @@
   {
     "name": "iNatAg/lactuca_virosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96415,8 +96415,8 @@
   {
     "name": "iNatAg/lagascea_mollis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96436,8 +96436,8 @@
   {
     "name": "iNatAg/lagenaria_siceraria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96457,8 +96457,8 @@
   {
     "name": "iNatAg/lagerstroemia_flos-reginae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96478,8 +96478,8 @@
   {
     "name": "iNatAg/lagerstroemia_lanceolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96499,8 +96499,8 @@
   {
     "name": "iNatAg/lagerstroemia_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96520,8 +96520,8 @@
   {
     "name": "iNatAg/laguncularia_racemosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96541,8 +96541,8 @@
   {
     "name": "iNatAg/lamium_album",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96562,8 +96562,8 @@
   {
     "name": "iNatAg/lamium_amplexicaule",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96583,8 +96583,8 @@
   {
     "name": "iNatAg/lamium_maculatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96604,8 +96604,8 @@
   {
     "name": "iNatAg/lamium_purpureum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96625,8 +96625,8 @@
   {
     "name": "iNatAg/lannea_coromandelica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96646,8 +96646,8 @@
   {
     "name": "iNatAg/lannea_edulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96667,8 +96667,8 @@
   {
     "name": "iNatAg/lansium_domesticum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96688,8 +96688,8 @@
   {
     "name": "iNatAg/lantana_camara",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96709,8 +96709,8 @@
   {
     "name": "iNatAg/lapsana_communis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96730,8 +96730,8 @@
   {
     "name": "iNatAg/larix_decidua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96751,8 +96751,8 @@
   {
     "name": "iNatAg/larrea_divaricata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96772,8 +96772,8 @@
   {
     "name": "iNatAg/lathyrus_angulatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96793,8 +96793,8 @@
   {
     "name": "iNatAg/lathyrus_cicera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96814,8 +96814,8 @@
   {
     "name": "iNatAg/lathyrus_hirsutus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96835,8 +96835,8 @@
   {
     "name": "iNatAg/lathyrus_latifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96856,8 +96856,8 @@
   {
     "name": "iNatAg/lathyrus_ochrus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96877,8 +96877,8 @@
   {
     "name": "iNatAg/lathyrus_odoratus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96898,8 +96898,8 @@
   {
     "name": "iNatAg/lathyrus_palustris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96919,8 +96919,8 @@
   {
     "name": "iNatAg/lathyrus_pratensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96940,8 +96940,8 @@
   {
     "name": "iNatAg/lathyrus_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96961,8 +96961,8 @@
   {
     "name": "iNatAg/lathyrus_sativus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -96982,8 +96982,8 @@
   {
     "name": "iNatAg/lathyrus_tingitanus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97003,8 +97003,8 @@
   {
     "name": "iNatAg/lathyrus_tuberosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97024,8 +97024,8 @@
   {
     "name": "iNatAg/laurus_nobilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97045,8 +97045,8 @@
   {
     "name": "iNatAg/lavandula_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97066,8 +97066,8 @@
   {
     "name": "iNatAg/lavandula_dentata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97087,8 +97087,8 @@
   {
     "name": "iNatAg/lavandula_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97108,8 +97108,8 @@
   {
     "name": "iNatAg/lawsonia_inermis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97129,8 +97129,8 @@
   {
     "name": "iNatAg/ledum_groenlandicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97150,8 +97150,8 @@
   {
     "name": "iNatAg/leersia_hexandra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97171,8 +97171,8 @@
   {
     "name": "iNatAg/leersia_lenticularis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97192,8 +97192,8 @@
   {
     "name": "iNatAg/lemna_aequinoctialis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97213,8 +97213,8 @@
   {
     "name": "iNatAg/lemna_gibba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97234,8 +97234,8 @@
   {
     "name": "iNatAg/lemna_minor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97255,8 +97255,8 @@
   {
     "name": "iNatAg/lemna_trisulca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97276,8 +97276,8 @@
   {
     "name": "iNatAg/lens_culinaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97297,8 +97297,8 @@
   {
     "name": "iNatAg/leontodon_autumnale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97318,8 +97318,8 @@
   {
     "name": "iNatAg/leontodon_autumnalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97339,8 +97339,8 @@
   {
     "name": "iNatAg/leontodon_hirtus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97360,8 +97360,8 @@
   {
     "name": "iNatAg/leontodon_saxatilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97381,8 +97381,8 @@
   {
     "name": "iNatAg/leontopodium_alpinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97402,8 +97402,8 @@
   {
     "name": "iNatAg/leonurus_cardiaca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97423,8 +97423,8 @@
   {
     "name": "iNatAg/leonurus_marrubiastrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97444,8 +97444,8 @@
   {
     "name": "iNatAg/leonurus_sibericus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97465,8 +97465,8 @@
   {
     "name": "iNatAg/leonurus_sibiricus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97486,8 +97486,8 @@
   {
     "name": "iNatAg/lepidium_austrinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97507,8 +97507,8 @@
   {
     "name": "iNatAg/lepidium_chalepense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97528,8 +97528,8 @@
   {
     "name": "iNatAg/lepidium_didymum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97549,8 +97549,8 @@
   {
     "name": "iNatAg/lepidium_draba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97570,8 +97570,8 @@
   {
     "name": "iNatAg/lepidium_lasiocarpum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97591,8 +97591,8 @@
   {
     "name": "iNatAg/lepidium_latifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97612,8 +97612,8 @@
   {
     "name": "iNatAg/lepidium_perfoliatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97633,8 +97633,8 @@
   {
     "name": "iNatAg/lepidium_ruderale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97654,8 +97654,8 @@
   {
     "name": "iNatAg/lepidium_sativum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97675,8 +97675,8 @@
   {
     "name": "iNatAg/lepidium_virginicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97696,8 +97696,8 @@
   {
     "name": "iNatAg/leptochloa_chinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97717,8 +97717,8 @@
   {
     "name": "iNatAg/leptochloa_fusca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97738,8 +97738,8 @@
   {
     "name": "iNatAg/leptochloa_nealleyi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97759,8 +97759,8 @@
   {
     "name": "iNatAg/lespedeza_cuneata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97780,8 +97780,8 @@
   {
     "name": "iNatAg/lespedeza_striata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97801,8 +97801,8 @@
   {
     "name": "iNatAg/lesquerella_fendleri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97822,8 +97822,8 @@
   {
     "name": "iNatAg/leucaena_diversifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97843,8 +97843,8 @@
   {
     "name": "iNatAg/leucaena_leucocephala",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97864,8 +97864,8 @@
   {
     "name": "iNatAg/leucanthemum_vulgare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97885,8 +97885,8 @@
   {
     "name": "iNatAg/leucojum_aestivum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97906,8 +97906,8 @@
   {
     "name": "iNatAg/levisticum_officinale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97927,8 +97927,8 @@
   {
     "name": "iNatAg/liatris_mucronata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97948,8 +97948,8 @@
   {
     "name": "iNatAg/licuala_ramsayi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97969,8 +97969,8 @@
   {
     "name": "iNatAg/ligustrum_ovalifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -97990,8 +97990,8 @@
   {
     "name": "iNatAg/ligustrum_vulgare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98011,8 +98011,8 @@
   {
     "name": "iNatAg/lilium_canadense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98032,8 +98032,8 @@
   {
     "name": "iNatAg/lilium_candidum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98053,8 +98053,8 @@
   {
     "name": "iNatAg/limnanthes_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98074,8 +98074,8 @@
   {
     "name": "iNatAg/limnophila_sessiliflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98095,8 +98095,8 @@
   {
     "name": "iNatAg/linaria_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98116,8 +98116,8 @@
   {
     "name": "iNatAg/lindernia_grandiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98137,8 +98137,8 @@
   {
     "name": "iNatAg/linum_usitatissimum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98158,8 +98158,8 @@
   {
     "name": "iNatAg/lippia_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98179,8 +98179,8 @@
   {
     "name": "iNatAg/liquidambar_styraciflua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98200,8 +98200,8 @@
   {
     "name": "iNatAg/liriodendron_tulipifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98221,8 +98221,8 @@
   {
     "name": "iNatAg/litchi_chinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98242,8 +98242,8 @@
   {
     "name": "iNatAg/lithospermum_arvense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98263,8 +98263,8 @@
   {
     "name": "iNatAg/lithospermum_officinale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98284,8 +98284,8 @@
   {
     "name": "iNatAg/livistona_australis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98305,8 +98305,8 @@
   {
     "name": "iNatAg/lobelia_inflata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98326,8 +98326,8 @@
   {
     "name": "iNatAg/lobelia_siphilitica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98347,8 +98347,8 @@
   {
     "name": "iNatAg/lolium_multiflorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98368,8 +98368,8 @@
   {
     "name": "iNatAg/lolium_perenne",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98389,8 +98389,8 @@
   {
     "name": "iNatAg/lolium_rigidum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98410,8 +98410,8 @@
   {
     "name": "iNatAg/lolium_temulentum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98431,8 +98431,8 @@
   {
     "name": "iNatAg/lonchocarpus_laxiflorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98452,8 +98452,8 @@
   {
     "name": "iNatAg/lonicera_caerulea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98473,8 +98473,8 @@
   {
     "name": "iNatAg/lonicera_caprifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98494,8 +98494,8 @@
   {
     "name": "iNatAg/lonicera_periclymenum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98515,8 +98515,8 @@
   {
     "name": "iNatAg/lonicera_sempervirens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98536,8 +98536,8 @@
   {
     "name": "iNatAg/lonicera_tartarica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98557,8 +98557,8 @@
   {
     "name": "iNatAg/lonicera_tatarica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98578,8 +98578,8 @@
   {
     "name": "iNatAg/lonicera_xylosteum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98599,8 +98599,8 @@
   {
     "name": "iNatAg/lophostemon_suaveolens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98620,8 +98620,8 @@
   {
     "name": "iNatAg/lotus_corniculatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98641,8 +98641,8 @@
   {
     "name": "iNatAg/lotus_creticus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98662,8 +98662,8 @@
   {
     "name": "iNatAg/lotus_edulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98683,8 +98683,8 @@
   {
     "name": "iNatAg/lotus_halophilus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98704,8 +98704,8 @@
   {
     "name": "iNatAg/lotus_parviflorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98725,8 +98725,8 @@
   {
     "name": "iNatAg/lotus_tenuis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98746,8 +98746,8 @@
   {
     "name": "iNatAg/lotus_uliginosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98767,8 +98767,8 @@
   {
     "name": "iNatAg/loudetia_simplex",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98788,8 +98788,8 @@
   {
     "name": "iNatAg/ludwigia_adscendens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98809,8 +98809,8 @@
   {
     "name": "iNatAg/ludwigia_alternifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98830,8 +98830,8 @@
   {
     "name": "iNatAg/luffa_acutangula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98851,8 +98851,8 @@
   {
     "name": "iNatAg/luffa_cylindrica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98872,8 +98872,8 @@
   {
     "name": "iNatAg/lumnitzera_littorea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98893,8 +98893,8 @@
   {
     "name": "iNatAg/lumnitzera_racemosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98914,8 +98914,8 @@
   {
     "name": "iNatAg/lunaria_annua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98935,8 +98935,8 @@
   {
     "name": "iNatAg/lupinus_albus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98956,8 +98956,8 @@
   {
     "name": "iNatAg/lupinus_angustifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98977,8 +98977,8 @@
   {
     "name": "iNatAg/lupinus_arboreus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -98998,8 +98998,8 @@
   {
     "name": "iNatAg/lupinus_cosentinii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99019,8 +99019,8 @@
   {
     "name": "iNatAg/lupinus_luteus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99040,8 +99040,8 @@
   {
     "name": "iNatAg/lupinus_mutabilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99061,8 +99061,8 @@
   {
     "name": "iNatAg/lupinus_pilosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99082,8 +99082,8 @@
   {
     "name": "iNatAg/lychnis_chalcedonica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99103,8 +99103,8 @@
   {
     "name": "iNatAg/lychnis_flos-cuculi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99124,8 +99124,8 @@
   {
     "name": "iNatAg/lychnis_viscaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99145,8 +99145,8 @@
   {
     "name": "iNatAg/lycium_barbarum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99166,8 +99166,8 @@
   {
     "name": "iNatAg/lycium_berlandieri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99187,8 +99187,8 @@
   {
     "name": "iNatAg/lycium_chinense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99208,8 +99208,8 @@
   {
     "name": "iNatAg/lycium_ferocissimum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99229,8 +99229,8 @@
   {
     "name": "iNatAg/lycium_halimifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99250,8 +99250,8 @@
   {
     "name": "iNatAg/lycopersicon_esculentum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99271,8 +99271,8 @@
   {
     "name": "iNatAg/lycopodium_clavatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99292,8 +99292,8 @@
   {
     "name": "iNatAg/lycopus_europaeus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99313,8 +99313,8 @@
   {
     "name": "iNatAg/lysimachia_ciliata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99334,8 +99334,8 @@
   {
     "name": "iNatAg/lysimachia_nummularia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99355,8 +99355,8 @@
   {
     "name": "iNatAg/lysimachia_punctata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99376,8 +99376,8 @@
   {
     "name": "iNatAg/lysimachia_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99397,8 +99397,8 @@
   {
     "name": "iNatAg/lythrum_hyssopifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99418,8 +99418,8 @@
   {
     "name": "iNatAg/lythrum_salicaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99439,8 +99439,8 @@
   {
     "name": "iNatAg/lythrum_virgatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99460,8 +99460,8 @@
   {
     "name": "iNatAg/macadamia_integrifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99481,8 +99481,8 @@
   {
     "name": "iNatAg/macadamia_tetraphylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99502,8 +99502,8 @@
   {
     "name": "iNatAg/macaranga_tanarius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99523,8 +99523,8 @@
   {
     "name": "iNatAg/macroptilium_atropurpureum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99544,8 +99544,8 @@
   {
     "name": "iNatAg/macroptilium_erythroloma",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99565,8 +99565,8 @@
   {
     "name": "iNatAg/macroptilium_gracile",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99586,8 +99586,8 @@
   {
     "name": "iNatAg/macroptilium_lathyroides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99607,8 +99607,8 @@
   {
     "name": "iNatAg/macroptilium_longepedunculatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99628,8 +99628,8 @@
   {
     "name": "iNatAg/macrotyloma_axillare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99649,8 +99649,8 @@
   {
     "name": "iNatAg/maesopsis_eminii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99670,8 +99670,8 @@
   {
     "name": "iNatAg/maianthemum_canadense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99691,8 +99691,8 @@
   {
     "name": "iNatAg/majorana_hortensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99712,8 +99712,8 @@
   {
     "name": "iNatAg/malachra_alceifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99733,8 +99733,8 @@
   {
     "name": "iNatAg/mallotus_philippensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99754,8 +99754,8 @@
   {
     "name": "iNatAg/malpighia_glabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99775,8 +99775,8 @@
   {
     "name": "iNatAg/malus_domestica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99796,8 +99796,8 @@
   {
     "name": "iNatAg/malus_sylvestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99817,8 +99817,8 @@
   {
     "name": "iNatAg/malva_alcea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99838,8 +99838,8 @@
   {
     "name": "iNatAg/malva_moschata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99859,8 +99859,8 @@
   {
     "name": "iNatAg/malva_nicaeensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99880,8 +99880,8 @@
   {
     "name": "iNatAg/malva_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99901,8 +99901,8 @@
   {
     "name": "iNatAg/malva_pusilla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99922,8 +99922,8 @@
   {
     "name": "iNatAg/malva_rotundifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99943,8 +99943,8 @@
   {
     "name": "iNatAg/malva_silvestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99964,8 +99964,8 @@
   {
     "name": "iNatAg/malva_sylvestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -99985,8 +99985,8 @@
   {
     "name": "iNatAg/mammea_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100006,8 +100006,8 @@
   {
     "name": "iNatAg/mangifera_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100027,8 +100027,8 @@
   {
     "name": "iNatAg/manihot_esculenta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100048,8 +100048,8 @@
   {
     "name": "iNatAg/manilkara_zapota",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100069,8 +100069,8 @@
   {
     "name": "iNatAg/maranta_arundinacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100090,8 +100090,8 @@
   {
     "name": "iNatAg/markhamia_lutea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100111,8 +100111,8 @@
   {
     "name": "iNatAg/marrubium_vulgare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100132,8 +100132,8 @@
   {
     "name": "iNatAg/marsilea_quadrifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100153,8 +100153,8 @@
   {
     "name": "iNatAg/matricaria_chamomila",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100174,8 +100174,8 @@
   {
     "name": "iNatAg/matricaria_chamomilla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100195,8 +100195,8 @@
   {
     "name": "iNatAg/matricaria_discoidea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100216,8 +100216,8 @@
   {
     "name": "iNatAg/matricaria_perforata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100237,8 +100237,8 @@
   {
     "name": "iNatAg/matricaria_recutita",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100258,8 +100258,8 @@
   {
     "name": "iNatAg/mauritia_flexuosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100279,8 +100279,8 @@
   {
     "name": "iNatAg/mayaca_fluviatilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100300,8 +100300,8 @@
   {
     "name": "iNatAg/medicago_arabica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100321,8 +100321,8 @@
   {
     "name": "iNatAg/medicago_falcata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100342,8 +100342,8 @@
   {
     "name": "iNatAg/medicago_intertexta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100363,8 +100363,8 @@
   {
     "name": "iNatAg/medicago_laciniata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100384,8 +100384,8 @@
   {
     "name": "iNatAg/medicago_littoralis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100405,8 +100405,8 @@
   {
     "name": "iNatAg/medicago_lupulina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100426,8 +100426,8 @@
   {
     "name": "iNatAg/medicago_marina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100447,8 +100447,8 @@
   {
     "name": "iNatAg/medicago_minima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100468,8 +100468,8 @@
   {
     "name": "iNatAg/medicago_orbicularis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100489,8 +100489,8 @@
   {
     "name": "iNatAg/medicago_polymorpha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100510,8 +100510,8 @@
   {
     "name": "iNatAg/medicago_rigidula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100531,8 +100531,8 @@
   {
     "name": "iNatAg/medicago_rugosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100552,8 +100552,8 @@
   {
     "name": "iNatAg/medicago_sativa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100573,8 +100573,8 @@
   {
     "name": "iNatAg/medicago_scutellata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100594,8 +100594,8 @@
   {
     "name": "iNatAg/medicago_tornata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100615,8 +100615,8 @@
   {
     "name": "iNatAg/medicago_truncatula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100636,8 +100636,8 @@
   {
     "name": "iNatAg/medicago_turbinata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100657,8 +100657,8 @@
   {
     "name": "iNatAg/melaleuca_bracteata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100678,8 +100678,8 @@
   {
     "name": "iNatAg/melaleuca_cajuputi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100699,8 +100699,8 @@
   {
     "name": "iNatAg/melaleuca_dealbata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100720,8 +100720,8 @@
   {
     "name": "iNatAg/melaleuca_lanceolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100741,8 +100741,8 @@
   {
     "name": "iNatAg/melaleuca_leucadendron",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100762,8 +100762,8 @@
   {
     "name": "iNatAg/melaleuca_nervosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100783,8 +100783,8 @@
   {
     "name": "iNatAg/melaleuca_quinquenervia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100804,8 +100804,8 @@
   {
     "name": "iNatAg/melaleuca_viridiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100825,8 +100825,8 @@
   {
     "name": "iNatAg/melampyrum_lineare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100846,8 +100846,8 @@
   {
     "name": "iNatAg/melastoma_malabathricum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100867,8 +100867,8 @@
   {
     "name": "iNatAg/melastoma_melabathricum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100888,8 +100888,8 @@
   {
     "name": "iNatAg/melia_azedarach",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100909,8 +100909,8 @@
   {
     "name": "iNatAg/melica_decumbens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100930,8 +100930,8 @@
   {
     "name": "iNatAg/melicoccus_bijugatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100951,8 +100951,8 @@
   {
     "name": "iNatAg/melilotus_albus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100972,8 +100972,8 @@
   {
     "name": "iNatAg/melilotus_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -100993,8 +100993,8 @@
   {
     "name": "iNatAg/melilotus_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101014,8 +101014,8 @@
   {
     "name": "iNatAg/melilotus_suaveolens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101035,8 +101035,8 @@
   {
     "name": "iNatAg/melinis_minutiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101056,8 +101056,8 @@
   {
     "name": "iNatAg/melissa_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101077,8 +101077,8 @@
   {
     "name": "iNatAg/melochia_corchorifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101098,8 +101098,8 @@
   {
     "name": "iNatAg/melothria_pendula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101119,8 +101119,8 @@
   {
     "name": "iNatAg/mentha_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101140,8 +101140,8 @@
   {
     "name": "iNatAg/mentha_longifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101161,8 +101161,8 @@
   {
     "name": "iNatAg/mentha_piperita",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101182,8 +101182,8 @@
   {
     "name": "iNatAg/mentha_pulegium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101203,8 +101203,8 @@
   {
     "name": "iNatAg/mentha_rotundifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101224,8 +101224,8 @@
   {
     "name": "iNatAg/mentha_spicata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101245,8 +101245,8 @@
   {
     "name": "iNatAg/menyanthes_trifoliata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101266,8 +101266,8 @@
   {
     "name": "iNatAg/mercurialis_annua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101287,8 +101287,8 @@
   {
     "name": "iNatAg/mesembryanthemum_cristallinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101308,8 +101308,8 @@
   {
     "name": "iNatAg/mesembryanthemum_noctiflorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101329,8 +101329,8 @@
   {
     "name": "iNatAg/mespilus_germanica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101350,8 +101350,8 @@
   {
     "name": "iNatAg/mesua_ferrea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101371,8 +101371,8 @@
   {
     "name": "iNatAg/metroxylon_sagu",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101392,8 +101392,8 @@
   {
     "name": "iNatAg/michelia_champaca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101413,8 +101413,8 @@
   {
     "name": "iNatAg/microstegium_ciliatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101434,8 +101434,8 @@
   {
     "name": "iNatAg/miliusa_velutina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101455,8 +101455,8 @@
   {
     "name": "iNatAg/mimosa_casta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101476,8 +101476,8 @@
   {
     "name": "iNatAg/mimosa_dutrae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101497,8 +101497,8 @@
   {
     "name": "iNatAg/mimosa_pigra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101518,8 +101518,8 @@
   {
     "name": "iNatAg/mimosa_pudica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101539,8 +101539,8 @@
   {
     "name": "iNatAg/mirabilis_jalapa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101560,8 +101560,8 @@
   {
     "name": "iNatAg/molinia_caerulea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101581,8 +101581,8 @@
   {
     "name": "iNatAg/mollugo_verticillata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101602,8 +101602,8 @@
   {
     "name": "iNatAg/momordica_charantia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101623,8 +101623,8 @@
   {
     "name": "iNatAg/momordica_cochinchinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101644,8 +101644,8 @@
   {
     "name": "iNatAg/monarda_fistulosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101665,8 +101665,8 @@
   {
     "name": "iNatAg/monarda_punctata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101686,8 +101686,8 @@
   {
     "name": "iNatAg/monochoria_hastata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101707,8 +101707,8 @@
   {
     "name": "iNatAg/monochoria_vaginalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101728,8 +101728,8 @@
   {
     "name": "iNatAg/monocymbium_ceresiiforme",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101749,8 +101749,8 @@
   {
     "name": "iNatAg/monstera_deliciosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101770,8 +101770,8 @@
   {
     "name": "iNatAg/montanoa_hibiscifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101791,8 +101791,8 @@
   {
     "name": "iNatAg/morinda_citrifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101812,8 +101812,8 @@
   {
     "name": "iNatAg/moringa_oleifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101833,8 +101833,8 @@
   {
     "name": "iNatAg/morus_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101854,8 +101854,8 @@
   {
     "name": "iNatAg/morus_nigra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101875,8 +101875,8 @@
   {
     "name": "iNatAg/morus_rubra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101896,8 +101896,8 @@
   {
     "name": "iNatAg/mucuna_pruriens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101917,8 +101917,8 @@
   {
     "name": "iNatAg/muntingia_calabura",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101938,8 +101938,8 @@
   {
     "name": "iNatAg/murraya_koenigii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101959,8 +101959,8 @@
   {
     "name": "iNatAg/musa_acuminata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -101980,8 +101980,8 @@
   {
     "name": "iNatAg/musa_acuminata_×_balbisiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102001,8 +102001,8 @@
   {
     "name": "iNatAg/musa_balbisiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102022,8 +102022,8 @@
   {
     "name": "iNatAg/musa_sapientium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102043,8 +102043,8 @@
   {
     "name": "iNatAg/musanga_cecropioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102064,8 +102064,8 @@
   {
     "name": "iNatAg/muscari_comosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102085,8 +102085,8 @@
   {
     "name": "iNatAg/myosotis_alpestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102106,8 +102106,8 @@
   {
     "name": "iNatAg/myosurus_minimus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102127,8 +102127,8 @@
   {
     "name": "iNatAg/myrica_cerifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102148,8 +102148,8 @@
   {
     "name": "iNatAg/myriophyllum_heterophyllum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102169,8 +102169,8 @@
   {
     "name": "iNatAg/myriophyllum_implicatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102190,8 +102190,8 @@
   {
     "name": "iNatAg/myriophyllum_sibiricum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102211,8 +102211,8 @@
   {
     "name": "iNatAg/myriophyllum_spicatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102232,8 +102232,8 @@
   {
     "name": "iNatAg/myriophyllum_verticillatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102253,8 +102253,8 @@
   {
     "name": "iNatAg/myristica_fragrans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102274,8 +102274,8 @@
   {
     "name": "iNatAg/myroxylon_balsamum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102295,8 +102295,8 @@
   {
     "name": "iNatAg/myrsine_africana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102316,8 +102316,8 @@
   {
     "name": "iNatAg/myrtus_communis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102337,8 +102337,8 @@
   {
     "name": "iNatAg/nardus_stricta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102358,8 +102358,8 @@
   {
     "name": "iNatAg/nasturtium_officinale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102379,8 +102379,8 @@
   {
     "name": "iNatAg/nauclea_orientalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102400,8 +102400,8 @@
   {
     "name": "iNatAg/nelumbo_nucifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102421,8 +102421,8 @@
   {
     "name": "iNatAg/neofabricia_myrtifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102442,8 +102442,8 @@
   {
     "name": "iNatAg/neoglaziovia_variegata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102463,8 +102463,8 @@
   {
     "name": "iNatAg/neonotonia_wightii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102484,8 +102484,8 @@
   {
     "name": "iNatAg/nepeta_cataria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102505,8 +102505,8 @@
   {
     "name": "iNatAg/nephelium_lappaceum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102526,8 +102526,8 @@
   {
     "name": "iNatAg/nephelium_mutabile",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102547,8 +102547,8 @@
   {
     "name": "iNatAg/nerium_oleander",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102568,8 +102568,8 @@
   {
     "name": "iNatAg/nicotiana_quadrivalvis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102589,8 +102589,8 @@
   {
     "name": "iNatAg/nicotiana_rustica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102610,8 +102610,8 @@
   {
     "name": "iNatAg/nicotiana_trigonophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102631,8 +102631,8 @@
   {
     "name": "iNatAg/nigella_sativa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102652,8 +102652,8 @@
   {
     "name": "iNatAg/nothofagus_cunninghamii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102673,8 +102673,8 @@
   {
     "name": "iNatAg/nothofagus_moorei",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102694,8 +102694,8 @@
   {
     "name": "iNatAg/nothoscordum_borbonicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102715,8 +102715,8 @@
   {
     "name": "iNatAg/nuphar_advena",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102736,8 +102736,8 @@
   {
     "name": "iNatAg/nuphar_lutea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102757,8 +102757,8 @@
   {
     "name": "iNatAg/nymphaea_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102778,8 +102778,8 @@
   {
     "name": "iNatAg/nypa_fruticans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102799,8 +102799,8 @@
   {
     "name": "iNatAg/ochroma_pyramidale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102820,8 +102820,8 @@
   {
     "name": "iNatAg/ocimum_americanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102841,8 +102841,8 @@
   {
     "name": "iNatAg/ocimum_basilicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102862,8 +102862,8 @@
   {
     "name": "iNatAg/ocimum_tenuiflorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102883,8 +102883,8 @@
   {
     "name": "iNatAg/octomeles_sumatrana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102904,8 +102904,8 @@
   {
     "name": "iNatAg/oenanthe_javanica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102925,8 +102925,8 @@
   {
     "name": "iNatAg/oenothera_albicaulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102946,8 +102946,8 @@
   {
     "name": "iNatAg/oenothera_biennis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102967,8 +102967,8 @@
   {
     "name": "iNatAg/oenothera_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -102988,8 +102988,8 @@
   {
     "name": "iNatAg/oenothera_perennis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103009,8 +103009,8 @@
   {
     "name": "iNatAg/oldenlandia_corymbosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103030,8 +103030,8 @@
   {
     "name": "iNatAg/olea_africana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103051,8 +103051,8 @@
   {
     "name": "iNatAg/olea_capensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103072,8 +103072,8 @@
   {
     "name": "iNatAg/olea_europaea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103093,8 +103093,8 @@
   {
     "name": "iNatAg/olea_europea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103114,8 +103114,8 @@
   {
     "name": "iNatAg/oncosperma_tigillarium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103135,8 +103135,8 @@
   {
     "name": "iNatAg/onobrychis_viciifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103156,8 +103156,8 @@
   {
     "name": "iNatAg/ononis_alopecuroides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103177,8 +103177,8 @@
   {
     "name": "iNatAg/ononis_spinosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103198,8 +103198,8 @@
   {
     "name": "iNatAg/onopordum_acanthium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103219,8 +103219,8 @@
   {
     "name": "iNatAg/onopordum_illyricum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103240,8 +103240,8 @@
   {
     "name": "iNatAg/onosmodium_discolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103261,8 +103261,8 @@
   {
     "name": "iNatAg/opuntia_ficus-indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103282,8 +103282,8 @@
   {
     "name": "iNatAg/opuntia_leptocaulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103303,8 +103303,8 @@
   {
     "name": "iNatAg/opuntia_polyacantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103324,8 +103324,8 @@
   {
     "name": "iNatAg/opuntia_polycantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103345,8 +103345,8 @@
   {
     "name": "iNatAg/origanum_majorana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103366,8 +103366,8 @@
   {
     "name": "iNatAg/origanum_onites",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103387,8 +103387,8 @@
   {
     "name": "iNatAg/origanum_vulgare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103408,8 +103408,8 @@
   {
     "name": "iNatAg/ornithogalum_nutans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103429,8 +103429,8 @@
   {
     "name": "iNatAg/ornithogalum_umbellatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103450,8 +103450,8 @@
   {
     "name": "iNatAg/ornithopus_compressus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103471,8 +103471,8 @@
   {
     "name": "iNatAg/ornithopus_sativus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103492,8 +103492,8 @@
   {
     "name": "iNatAg/orobanche_flava",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103513,8 +103513,8 @@
   {
     "name": "iNatAg/orobanche_ludoviciana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103534,8 +103534,8 @@
   {
     "name": "iNatAg/orobanche_minor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103555,8 +103555,8 @@
   {
     "name": "iNatAg/orobanche_ramosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103576,8 +103576,8 @@
   {
     "name": "iNatAg/orontium_aquaticum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103597,8 +103597,8 @@
   {
     "name": "iNatAg/orthosiphon_aristatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103618,8 +103618,8 @@
   {
     "name": "iNatAg/oryza_sativa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103639,8 +103639,8 @@
   {
     "name": "iNatAg/oryzopsis_holciformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103660,8 +103660,8 @@
   {
     "name": "iNatAg/oryzopsis_miliacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103681,8 +103681,8 @@
   {
     "name": "iNatAg/osmorhiza_berteroi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103702,8 +103702,8 @@
   {
     "name": "iNatAg/osmunda_regalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103723,8 +103723,8 @@
   {
     "name": "iNatAg/ottochloa_nodosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103744,8 +103744,8 @@
   {
     "name": "iNatAg/oxalis_acetosella",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103765,8 +103765,8 @@
   {
     "name": "iNatAg/oxalis_corniculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103786,8 +103786,8 @@
   {
     "name": "iNatAg/oxalis_pes-caprae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103807,8 +103807,8 @@
   {
     "name": "iNatAg/oxalis_pescaprae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103828,8 +103828,8 @@
   {
     "name": "iNatAg/oxalis_stricta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103849,8 +103849,8 @@
   {
     "name": "iNatAg/oxalis_tuberosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103870,8 +103870,8 @@
   {
     "name": "iNatAg/oxytropis_lambertii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103891,8 +103891,8 @@
   {
     "name": "iNatAg/pachyrhizus_erosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103912,8 +103912,8 @@
   {
     "name": "iNatAg/paederia_cruddasiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103933,8 +103933,8 @@
   {
     "name": "iNatAg/paederia_foetida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103954,8 +103954,8 @@
   {
     "name": "iNatAg/paeonia_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103975,8 +103975,8 @@
   {
     "name": "iNatAg/panax_ginseng",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -103996,8 +103996,8 @@
   {
     "name": "iNatAg/panax_quinquefolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104017,8 +104017,8 @@
   {
     "name": "iNatAg/pangium_edule",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104038,8 +104038,8 @@
   {
     "name": "iNatAg/panicum_antidotale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104059,8 +104059,8 @@
   {
     "name": "iNatAg/panicum_capillare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104080,8 +104080,8 @@
   {
     "name": "iNatAg/panicum_coloratum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104101,8 +104101,8 @@
   {
     "name": "iNatAg/panicum_ecklonii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104122,8 +104122,8 @@
   {
     "name": "iNatAg/panicum_gattingeri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104143,8 +104143,8 @@
   {
     "name": "iNatAg/panicum_maximum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104164,8 +104164,8 @@
   {
     "name": "iNatAg/panicum_miliaceum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104185,8 +104185,8 @@
   {
     "name": "iNatAg/panicum_natalense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104206,8 +104206,8 @@
   {
     "name": "iNatAg/panicum_obtusum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104227,8 +104227,8 @@
   {
     "name": "iNatAg/panicum_pilosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104248,8 +104248,8 @@
   {
     "name": "iNatAg/panicum_racemosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104269,8 +104269,8 @@
   {
     "name": "iNatAg/panicum_repens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104290,8 +104290,8 @@
   {
     "name": "iNatAg/panicum_sphaerocarpon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104311,8 +104311,8 @@
   {
     "name": "iNatAg/panicum_trichocladum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104332,8 +104332,8 @@
   {
     "name": "iNatAg/panicum_turgidum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104353,8 +104353,8 @@
   {
     "name": "iNatAg/panicum_virgatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104374,8 +104374,8 @@
   {
     "name": "iNatAg/papaver_argemone",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104395,8 +104395,8 @@
   {
     "name": "iNatAg/papaver_bracteatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104416,8 +104416,8 @@
   {
     "name": "iNatAg/papaver_dubium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104437,8 +104437,8 @@
   {
     "name": "iNatAg/papaver_rhoeas",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104458,8 +104458,8 @@
   {
     "name": "iNatAg/papaver_somniferum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104479,8 +104479,8 @@
   {
     "name": "iNatAg/parietaria_floridana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104500,8 +104500,8 @@
   {
     "name": "iNatAg/parietaria_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104521,8 +104521,8 @@
   {
     "name": "iNatAg/parinari_curatellifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104542,8 +104542,8 @@
   {
     "name": "iNatAg/parkia_biglobosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104563,8 +104563,8 @@
   {
     "name": "iNatAg/parkia_speciosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104584,8 +104584,8 @@
   {
     "name": "iNatAg/parkinsonia_aculeata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104605,8 +104605,8 @@
   {
     "name": "iNatAg/parnassia_palustris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104626,8 +104626,8 @@
   {
     "name": "iNatAg/parsonsia_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104647,8 +104647,8 @@
   {
     "name": "iNatAg/parthenium_argentatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104668,8 +104668,8 @@
   {
     "name": "iNatAg/parthenium_hysterophorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104689,8 +104689,8 @@
   {
     "name": "iNatAg/paspalum_conjugatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104710,8 +104710,8 @@
   {
     "name": "iNatAg/paspalum_dilatatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104731,8 +104731,8 @@
   {
     "name": "iNatAg/paspalum_distichum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104752,8 +104752,8 @@
   {
     "name": "iNatAg/paspalum_nicorae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104773,8 +104773,8 @@
   {
     "name": "iNatAg/paspalum_notatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104794,8 +104794,8 @@
   {
     "name": "iNatAg/paspalum_plicatulum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104815,8 +104815,8 @@
   {
     "name": "iNatAg/paspalum_scrobiculatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104836,8 +104836,8 @@
   {
     "name": "iNatAg/paspalum_separatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104857,8 +104857,8 @@
   {
     "name": "iNatAg/paspalum_urvillei",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104878,8 +104878,8 @@
   {
     "name": "iNatAg/paspalum_vaginatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104899,8 +104899,8 @@
   {
     "name": "iNatAg/passiflora_bicornis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104920,8 +104920,8 @@
   {
     "name": "iNatAg/passiflora_edulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104941,8 +104941,8 @@
   {
     "name": "iNatAg/passiflora_foetida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104962,8 +104962,8 @@
   {
     "name": "iNatAg/passiflora_incarnata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -104983,8 +104983,8 @@
   {
     "name": "iNatAg/passiflora_laurifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105004,8 +105004,8 @@
   {
     "name": "iNatAg/passiflora_ligularis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105025,8 +105025,8 @@
   {
     "name": "iNatAg/passiflora_lutea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105046,8 +105046,8 @@
   {
     "name": "iNatAg/passiflora_mollissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105067,8 +105067,8 @@
   {
     "name": "iNatAg/passiflora_quadrangularis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105088,8 +105088,8 @@
   {
     "name": "iNatAg/passiflora_suberosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105109,8 +105109,8 @@
   {
     "name": "iNatAg/pastinaca_sativa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105130,8 +105130,8 @@
   {
     "name": "iNatAg/paullinia_cupana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105151,8 +105151,8 @@
   {
     "name": "iNatAg/paulownia_tomentosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105172,8 +105172,8 @@
   {
     "name": "iNatAg/peganum_harmala",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105193,8 +105193,8 @@
   {
     "name": "iNatAg/pelargonium_graveolens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105214,8 +105214,8 @@
   {
     "name": "iNatAg/peltandra_sagittifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105235,8 +105235,8 @@
   {
     "name": "iNatAg/peltandra_virginica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105256,8 +105256,8 @@
   {
     "name": "iNatAg/peltophorum_africanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105277,8 +105277,8 @@
   {
     "name": "iNatAg/peltophorum_pterocarpum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105298,8 +105298,8 @@
   {
     "name": "iNatAg/pennisetum_clandestinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105319,8 +105319,8 @@
   {
     "name": "iNatAg/pennisetum_glaucum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105340,8 +105340,8 @@
   {
     "name": "iNatAg/pennisetum_macrourum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105361,8 +105361,8 @@
   {
     "name": "iNatAg/pennisetum_pedicellatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105382,8 +105382,8 @@
   {
     "name": "iNatAg/pennisetum_polystachyon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105403,8 +105403,8 @@
   {
     "name": "iNatAg/pennisetum_purpureum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105424,8 +105424,8 @@
   {
     "name": "iNatAg/pennisetum_setaceum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105445,8 +105445,8 @@
   {
     "name": "iNatAg/pennisetum_villosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105466,8 +105466,8 @@
   {
     "name": "iNatAg/perilla_frutescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105487,8 +105487,8 @@
   {
     "name": "iNatAg/persea_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105508,8 +105508,8 @@
   {
     "name": "iNatAg/persicaria_maculosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105529,8 +105529,8 @@
   {
     "name": "iNatAg/persoonia_falcata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105550,8 +105550,8 @@
   {
     "name": "iNatAg/petalostigma_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105571,8 +105571,8 @@
   {
     "name": "iNatAg/petasites_albus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105592,8 +105592,8 @@
   {
     "name": "iNatAg/petasites_hybridus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105613,8 +105613,8 @@
   {
     "name": "iNatAg/petroselinum_crispum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105634,8 +105634,8 @@
   {
     "name": "iNatAg/petunia_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105655,8 +105655,8 @@
   {
     "name": "iNatAg/peucedanum_ostruthium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105676,8 +105676,8 @@
   {
     "name": "iNatAg/phalaris_aquatica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105697,8 +105697,8 @@
   {
     "name": "iNatAg/phalaris_arundinacea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105718,8 +105718,8 @@
   {
     "name": "iNatAg/phalaris_arundinaceae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105739,8 +105739,8 @@
   {
     "name": "iNatAg/phalaris_brachystachys",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105760,8 +105760,8 @@
   {
     "name": "iNatAg/phalaris_canariensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105781,8 +105781,8 @@
   {
     "name": "iNatAg/phalaris_caroliniana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105802,8 +105802,8 @@
   {
     "name": "iNatAg/phalaris_coerulescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105823,8 +105823,8 @@
   {
     "name": "iNatAg/phalaris_paradoxa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105844,8 +105844,8 @@
   {
     "name": "iNatAg/phaseolus_acutifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105865,8 +105865,8 @@
   {
     "name": "iNatAg/phaseolus_coccineus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105886,8 +105886,8 @@
   {
     "name": "iNatAg/phaseolus_lunatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105907,8 +105907,8 @@
   {
     "name": "iNatAg/phaseolus_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105928,8 +105928,8 @@
   {
     "name": "iNatAg/phleum_alpinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105949,8 +105949,8 @@
   {
     "name": "iNatAg/phleum_pratense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105970,8 +105970,8 @@
   {
     "name": "iNatAg/phoenix_dactylifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -105991,8 +105991,8 @@
   {
     "name": "iNatAg/phoenix_reclinata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106012,8 +106012,8 @@
   {
     "name": "iNatAg/phoenix_sylvestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106033,8 +106033,8 @@
   {
     "name": "iNatAg/phormium_tenax",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106054,8 +106054,8 @@
   {
     "name": "iNatAg/phragmites_australis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106075,8 +106075,8 @@
   {
     "name": "iNatAg/phragmites_communis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106096,8 +106096,8 @@
   {
     "name": "iNatAg/phragmites_karka",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106117,8 +106117,8 @@
   {
     "name": "iNatAg/phyllanthus_niruri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106138,8 +106138,8 @@
   {
     "name": "iNatAg/phyllanthus_tenellus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106159,8 +106159,8 @@
   {
     "name": "iNatAg/phyllanthus_urinaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106180,8 +106180,8 @@
   {
     "name": "iNatAg/phyllocladus_aspleniifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106201,8 +106201,8 @@
   {
     "name": "iNatAg/physalis_alkekengi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106222,8 +106222,8 @@
   {
     "name": "iNatAg/physalis_angulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106243,8 +106243,8 @@
   {
     "name": "iNatAg/physalis_heterophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106264,8 +106264,8 @@
   {
     "name": "iNatAg/physalis_lancifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106285,8 +106285,8 @@
   {
     "name": "iNatAg/physalis_peruviana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106306,8 +106306,8 @@
   {
     "name": "iNatAg/physalis_philadelphica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106327,8 +106327,8 @@
   {
     "name": "iNatAg/physalis_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106348,8 +106348,8 @@
   {
     "name": "iNatAg/physalis_virginiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106369,8 +106369,8 @@
   {
     "name": "iNatAg/physalis_viscosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106390,8 +106390,8 @@
   {
     "name": "iNatAg/phytolacca_acinosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106411,8 +106411,8 @@
   {
     "name": "iNatAg/phytolacca_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106432,8 +106432,8 @@
   {
     "name": "iNatAg/phytolacca_dioica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106453,8 +106453,8 @@
   {
     "name": "iNatAg/picea_abies",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106474,8 +106474,8 @@
   {
     "name": "iNatAg/picea_omorica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106495,8 +106495,8 @@
   {
     "name": "iNatAg/picea_omorika",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106516,8 +106516,8 @@
   {
     "name": "iNatAg/picris_echioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106537,8 +106537,8 @@
   {
     "name": "iNatAg/picris_hieracioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106558,8 +106558,8 @@
   {
     "name": "iNatAg/piliostigma_reticulatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106579,8 +106579,8 @@
   {
     "name": "iNatAg/piliostigma_thonningii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106600,8 +106600,8 @@
   {
     "name": "iNatAg/pimenta_dioica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106621,8 +106621,8 @@
   {
     "name": "iNatAg/pimenta_racemosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106642,8 +106642,8 @@
   {
     "name": "iNatAg/pimpinella_anisum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106663,8 +106663,8 @@
   {
     "name": "iNatAg/pimpinella_saxifraga",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106684,8 +106684,8 @@
   {
     "name": "iNatAg/pinguicula_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106705,8 +106705,8 @@
   {
     "name": "iNatAg/pinus_ayacahuite",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106726,8 +106726,8 @@
   {
     "name": "iNatAg/pinus_brutia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106747,8 +106747,8 @@
   {
     "name": "iNatAg/pinus_canariensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106768,8 +106768,8 @@
   {
     "name": "iNatAg/pinus_caribaea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106789,8 +106789,8 @@
   {
     "name": "iNatAg/pinus_chiapensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106810,8 +106810,8 @@
   {
     "name": "iNatAg/pinus_douglasiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106831,8 +106831,8 @@
   {
     "name": "iNatAg/pinus_durangensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106852,8 +106852,8 @@
   {
     "name": "iNatAg/pinus_greggii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106873,8 +106873,8 @@
   {
     "name": "iNatAg/pinus_halepensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106894,8 +106894,8 @@
   {
     "name": "iNatAg/pinus_hartwegii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106915,8 +106915,8 @@
   {
     "name": "iNatAg/pinus_kesiya",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106936,8 +106936,8 @@
   {
     "name": "iNatAg/pinus_merkusii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106957,8 +106957,8 @@
   {
     "name": "iNatAg/pinus_montezumae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106978,8 +106978,8 @@
   {
     "name": "iNatAg/pinus_mugo",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -106999,8 +106999,8 @@
   {
     "name": "iNatAg/pinus_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107020,8 +107020,8 @@
   {
     "name": "iNatAg/pinus_oocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107041,8 +107041,8 @@
   {
     "name": "iNatAg/pinus_palustris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107062,8 +107062,8 @@
   {
     "name": "iNatAg/pinus_patula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107083,8 +107083,8 @@
   {
     "name": "iNatAg/pinus_pinaster",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107104,8 +107104,8 @@
   {
     "name": "iNatAg/pinus_pinea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107125,8 +107125,8 @@
   {
     "name": "iNatAg/pinus_ponderosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107146,8 +107146,8 @@
   {
     "name": "iNatAg/pinus_pseudostrobus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107167,8 +107167,8 @@
   {
     "name": "iNatAg/pinus_radiata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107188,8 +107188,8 @@
   {
     "name": "iNatAg/pinus_roxburghii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107209,8 +107209,8 @@
   {
     "name": "iNatAg/pinus_sylvestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107230,8 +107230,8 @@
   {
     "name": "iNatAg/pinus_tabuliformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107251,8 +107251,8 @@
   {
     "name": "iNatAg/pinus_taeda",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107272,8 +107272,8 @@
   {
     "name": "iNatAg/pinus_teocote",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107293,8 +107293,8 @@
   {
     "name": "iNatAg/piper_aduncum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107314,8 +107314,8 @@
   {
     "name": "iNatAg/piper_betle",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107335,8 +107335,8 @@
   {
     "name": "iNatAg/piper_longum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107356,8 +107356,8 @@
   {
     "name": "iNatAg/piper_methysticum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107377,8 +107377,8 @@
   {
     "name": "iNatAg/piper_nigrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107398,8 +107398,8 @@
   {
     "name": "iNatAg/pistacia_atlantica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107419,8 +107419,8 @@
   {
     "name": "iNatAg/pistacia_lentiscus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107440,8 +107440,8 @@
   {
     "name": "iNatAg/pistacia_vera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107461,8 +107461,8 @@
   {
     "name": "iNatAg/pistia_stratiotes",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107482,8 +107482,8 @@
   {
     "name": "iNatAg/pisum_sativum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107503,8 +107503,8 @@
   {
     "name": "iNatAg/pithecellobium_dulce",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107524,8 +107524,8 @@
   {
     "name": "iNatAg/pittosporum_resiniferum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107545,8 +107545,8 @@
   {
     "name": "iNatAg/pittosporum_undulatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107566,8 +107566,8 @@
   {
     "name": "iNatAg/plagiobothrys_canescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107587,8 +107587,8 @@
   {
     "name": "iNatAg/plantago_coronopus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107608,8 +107608,8 @@
   {
     "name": "iNatAg/plantago_heterophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107629,8 +107629,8 @@
   {
     "name": "iNatAg/plantago_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107650,8 +107650,8 @@
   {
     "name": "iNatAg/plantago_lanceolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107671,8 +107671,8 @@
   {
     "name": "iNatAg/plantago_major",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107692,8 +107692,8 @@
   {
     "name": "iNatAg/plantago_media",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107713,8 +107713,8 @@
   {
     "name": "iNatAg/plantago_ovata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107734,8 +107734,8 @@
   {
     "name": "iNatAg/plantago_psyllium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107755,8 +107755,8 @@
   {
     "name": "iNatAg/plantago_virginica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107776,8 +107776,8 @@
   {
     "name": "iNatAg/platanus_orientalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107797,8 +107797,8 @@
   {
     "name": "iNatAg/poa_alpina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107818,8 +107818,8 @@
   {
     "name": "iNatAg/poa_annua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107839,8 +107839,8 @@
   {
     "name": "iNatAg/poa_bulbosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107860,8 +107860,8 @@
   {
     "name": "iNatAg/poa_compressa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107881,8 +107881,8 @@
   {
     "name": "iNatAg/poa_cuspidata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107902,8 +107902,8 @@
   {
     "name": "iNatAg/poa_fendleriana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107923,8 +107923,8 @@
   {
     "name": "iNatAg/poa_nemoralis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107944,8 +107944,8 @@
   {
     "name": "iNatAg/poa_pratensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107965,8 +107965,8 @@
   {
     "name": "iNatAg/poa_trivialis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -107986,8 +107986,8 @@
   {
     "name": "iNatAg/podocarpus_elatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108007,8 +108007,8 @@
   {
     "name": "iNatAg/podocarpus_falcatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108028,8 +108028,8 @@
   {
     "name": "iNatAg/poeciloneuron_indicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108049,8 +108049,8 @@
   {
     "name": "iNatAg/pogostemon_cablin",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108070,8 +108070,8 @@
   {
     "name": "iNatAg/polemonium_caeruleum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108091,8 +108091,8 @@
   {
     "name": "iNatAg/polemonium_micranthum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108112,8 +108112,8 @@
   {
     "name": "iNatAg/polyalthia_fragrans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108133,8 +108133,8 @@
   {
     "name": "iNatAg/polycarpon_tetraphyllum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108154,8 +108154,8 @@
   {
     "name": "iNatAg/polygonatum_orientale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108175,8 +108175,8 @@
   {
     "name": "iNatAg/polygonum_achoreum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108196,8 +108196,8 @@
   {
     "name": "iNatAg/polygonum_arenastrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108217,8 +108217,8 @@
   {
     "name": "iNatAg/polygonum_aviculare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108238,8 +108238,8 @@
   {
     "name": "iNatAg/polygonum_bistorta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108259,8 +108259,8 @@
   {
     "name": "iNatAg/polygonum_convolvulus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108280,8 +108280,8 @@
   {
     "name": "iNatAg/polygonum_equisetiforme",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108301,8 +108301,8 @@
   {
     "name": "iNatAg/polygonum_erectum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108322,8 +108322,8 @@
   {
     "name": "iNatAg/polygonum_hydropiper",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108343,8 +108343,8 @@
   {
     "name": "iNatAg/polygonum_hydropiperoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108364,8 +108364,8 @@
   {
     "name": "iNatAg/polygonum_lapathifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108385,8 +108385,8 @@
   {
     "name": "iNatAg/polygonum_orientale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108406,8 +108406,8 @@
   {
     "name": "iNatAg/polygonum_pensylvanicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108427,8 +108427,8 @@
   {
     "name": "iNatAg/polygonum_perfoliatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108448,8 +108448,8 @@
   {
     "name": "iNatAg/polygonum_persicaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108469,8 +108469,8 @@
   {
     "name": "iNatAg/polygonum_punctatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108490,8 +108490,8 @@
   {
     "name": "iNatAg/polygonum_ramosissimum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108511,8 +108511,8 @@
   {
     "name": "iNatAg/polygonum_scandens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108532,8 +108532,8 @@
   {
     "name": "iNatAg/polymnia_sonchifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108553,8 +108553,8 @@
   {
     "name": "iNatAg/polypodium_vulgare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108574,8 +108574,8 @@
   {
     "name": "iNatAg/polypogon_interruptus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108595,8 +108595,8 @@
   {
     "name": "iNatAg/polypremum_procumbens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108616,8 +108616,8 @@
   {
     "name": "iNatAg/polyscias_fulva",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108637,8 +108637,8 @@
   {
     "name": "iNatAg/polytrichum_commune",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108658,8 +108658,8 @@
   {
     "name": "iNatAg/pongamia_pinnata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108679,8 +108679,8 @@
   {
     "name": "iNatAg/pontederia_cordata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108700,8 +108700,8 @@
   {
     "name": "iNatAg/pontederia_rotundifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108721,8 +108721,8 @@
   {
     "name": "iNatAg/populus_balsamifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108742,8 +108742,8 @@
   {
     "name": "iNatAg/populus_ciliata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108763,8 +108763,8 @@
   {
     "name": "iNatAg/populus_deltoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108784,8 +108784,8 @@
   {
     "name": "iNatAg/populus_euphratica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108805,8 +108805,8 @@
   {
     "name": "iNatAg/populus_simonii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108826,8 +108826,8 @@
   {
     "name": "iNatAg/portulaca_oleracea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108847,8 +108847,8 @@
   {
     "name": "iNatAg/portulaca_pilosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108868,8 +108868,8 @@
   {
     "name": "iNatAg/portulaca_pilosa_pilosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108889,8 +108889,8 @@
   {
     "name": "iNatAg/portulaca_quadrifida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108910,8 +108910,8 @@
   {
     "name": "iNatAg/potamogeton_diversifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108931,8 +108931,8 @@
   {
     "name": "iNatAg/potamogeton_epihydrus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108952,8 +108952,8 @@
   {
     "name": "iNatAg/potamogeton_filiformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108973,8 +108973,8 @@
   {
     "name": "iNatAg/potamogeton_foliosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -108994,8 +108994,8 @@
   {
     "name": "iNatAg/potamogeton_friesii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109015,8 +109015,8 @@
   {
     "name": "iNatAg/potamogeton_gramineus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109036,8 +109036,8 @@
   {
     "name": "iNatAg/potamogeton_illinoensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109057,8 +109057,8 @@
   {
     "name": "iNatAg/potamogeton_natans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109078,8 +109078,8 @@
   {
     "name": "iNatAg/potamogeton_nodosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109099,8 +109099,8 @@
   {
     "name": "iNatAg/potamogeton_pectinatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109120,8 +109120,8 @@
   {
     "name": "iNatAg/potamogeton_praelongus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109141,8 +109141,8 @@
   {
     "name": "iNatAg/potamogeton_pusillus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109162,8 +109162,8 @@
   {
     "name": "iNatAg/potamogeton_zosteriformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109183,8 +109183,8 @@
   {
     "name": "iNatAg/potentilla_anserina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109204,8 +109204,8 @@
   {
     "name": "iNatAg/potentilla_argentea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109225,8 +109225,8 @@
   {
     "name": "iNatAg/potentilla_erecta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109246,8 +109246,8 @@
   {
     "name": "iNatAg/potentilla_fruticosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109267,8 +109267,8 @@
   {
     "name": "iNatAg/potentilla_intermedia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109288,8 +109288,8 @@
   {
     "name": "iNatAg/potentilla_norvegica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109309,8 +109309,8 @@
   {
     "name": "iNatAg/potentilla_norvegicae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109330,8 +109330,8 @@
   {
     "name": "iNatAg/potentilla_recta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109351,8 +109351,8 @@
   {
     "name": "iNatAg/potentilla_reptans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109372,8 +109372,8 @@
   {
     "name": "iNatAg/potentilla_tridentata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109393,8 +109393,8 @@
   {
     "name": "iNatAg/poterium_sanguisorba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109414,8 +109414,8 @@
   {
     "name": "iNatAg/pouteria_campechiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109435,8 +109435,8 @@
   {
     "name": "iNatAg/pouteria_lucuma",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109456,8 +109456,8 @@
   {
     "name": "iNatAg/pouteria_sapota",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109477,8 +109477,8 @@
   {
     "name": "iNatAg/prasophyllum_elatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109498,8 +109498,8 @@
   {
     "name": "iNatAg/primula_veris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109519,8 +109519,8 @@
   {
     "name": "iNatAg/proserpinaca_palustris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109540,8 +109540,8 @@
   {
     "name": "iNatAg/proserpinaca_pectinata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109561,8 +109561,8 @@
   {
     "name": "iNatAg/prosopis_affinis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109582,8 +109582,8 @@
   {
     "name": "iNatAg/prosopis_africana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109603,8 +109603,8 @@
   {
     "name": "iNatAg/prosopis_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109624,8 +109624,8 @@
   {
     "name": "iNatAg/prosopis_chilensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109645,8 +109645,8 @@
   {
     "name": "iNatAg/prosopis_cineraria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109666,8 +109666,8 @@
   {
     "name": "iNatAg/prosopis_glandulosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109687,8 +109687,8 @@
   {
     "name": "iNatAg/prosopis_juliflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109708,8 +109708,8 @@
   {
     "name": "iNatAg/prosopis_nigra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109729,8 +109729,8 @@
   {
     "name": "iNatAg/prosopis_pallida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109750,8 +109750,8 @@
   {
     "name": "iNatAg/prosopis_tamarugo",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109771,8 +109771,8 @@
   {
     "name": "iNatAg/prosopis_velutina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109792,8 +109792,8 @@
   {
     "name": "iNatAg/prunella_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109813,8 +109813,8 @@
   {
     "name": "iNatAg/prunus_africana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109834,8 +109834,8 @@
   {
     "name": "iNatAg/prunus_amygdalus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109855,8 +109855,8 @@
   {
     "name": "iNatAg/prunus_armeniaca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109876,8 +109876,8 @@
   {
     "name": "iNatAg/prunus_avium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109897,8 +109897,8 @@
   {
     "name": "iNatAg/prunus_capuli",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109918,8 +109918,8 @@
   {
     "name": "iNatAg/prunus_cerasus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109939,8 +109939,8 @@
   {
     "name": "iNatAg/prunus_domestica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109960,8 +109960,8 @@
   {
     "name": "iNatAg/prunus_laurocerasus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -109981,8 +109981,8 @@
   {
     "name": "iNatAg/prunus_mahaleb",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110002,8 +110002,8 @@
   {
     "name": "iNatAg/prunus_mume",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110023,8 +110023,8 @@
   {
     "name": "iNatAg/prunus_padus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110044,8 +110044,8 @@
   {
     "name": "iNatAg/prunus_pensylvanica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110065,8 +110065,8 @@
   {
     "name": "iNatAg/prunus_persica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110086,8 +110086,8 @@
   {
     "name": "iNatAg/prunus_salicina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110107,8 +110107,8 @@
   {
     "name": "iNatAg/prunus_spinosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110128,8 +110128,8 @@
   {
     "name": "iNatAg/prunus_virginiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110149,8 +110149,8 @@
   {
     "name": "iNatAg/psathyrostachys_juncea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110170,8 +110170,8 @@
   {
     "name": "iNatAg/psidium_cattleianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110191,8 +110191,8 @@
   {
     "name": "iNatAg/psidium_friedrichsthalianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110212,8 +110212,8 @@
   {
     "name": "iNatAg/psidium_guajava",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110233,8 +110233,8 @@
   {
     "name": "iNatAg/psophocarpus_tetragonolobus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110254,8 +110254,8 @@
   {
     "name": "iNatAg/psoralea_repens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110275,8 +110275,8 @@
   {
     "name": "iNatAg/ptelea_trifoliata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110296,8 +110296,8 @@
   {
     "name": "iNatAg/pterocarpus_angolensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110317,8 +110317,8 @@
   {
     "name": "iNatAg/pterocarpus_dalbergioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110338,8 +110338,8 @@
   {
     "name": "iNatAg/pterocarpus_erinaceus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110359,8 +110359,8 @@
   {
     "name": "iNatAg/pterocarpus_indicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110380,8 +110380,8 @@
   {
     "name": "iNatAg/pterocarpus_lucens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110401,8 +110401,8 @@
   {
     "name": "iNatAg/pterocarpus_macrocarpus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110422,8 +110422,8 @@
   {
     "name": "iNatAg/pterocarpus_marsupium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110443,8 +110443,8 @@
   {
     "name": "iNatAg/pterocarpus_santalinoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110464,8 +110464,8 @@
   {
     "name": "iNatAg/pterocarpus_santalinus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110485,8 +110485,8 @@
   {
     "name": "iNatAg/pueraria_lobata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110506,8 +110506,8 @@
   {
     "name": "iNatAg/pueraria_phaseoloides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110527,8 +110527,8 @@
   {
     "name": "iNatAg/pulmonaria_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110548,8 +110548,8 @@
   {
     "name": "iNatAg/punica_granatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110569,8 +110569,8 @@
   {
     "name": "iNatAg/pycnanthus_angolensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110590,8 +110590,8 @@
   {
     "name": "iNatAg/pyrola_rotundifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110611,8 +110611,8 @@
   {
     "name": "iNatAg/pyrus_communis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110632,8 +110632,8 @@
   {
     "name": "iNatAg/pyrus_pyrifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110653,8 +110653,8 @@
   {
     "name": "iNatAg/quercus_agrifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110674,8 +110674,8 @@
   {
     "name": "iNatAg/quercus_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110695,8 +110695,8 @@
   {
     "name": "iNatAg/quercus_bicolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110716,8 +110716,8 @@
   {
     "name": "iNatAg/quercus_chrysolepis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110737,8 +110737,8 @@
   {
     "name": "iNatAg/quercus_dumosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110758,8 +110758,8 @@
   {
     "name": "iNatAg/quercus_fusiformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110779,8 +110779,8 @@
   {
     "name": "iNatAg/quercus_ilex",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110800,8 +110800,8 @@
   {
     "name": "iNatAg/quercus_incana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110821,8 +110821,8 @@
   {
     "name": "iNatAg/quercus_lanata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110842,8 +110842,8 @@
   {
     "name": "iNatAg/quercus_nigra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110863,8 +110863,8 @@
   {
     "name": "iNatAg/quercus_phellos",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110884,8 +110884,8 @@
   {
     "name": "iNatAg/quercus_robur",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110905,8 +110905,8 @@
   {
     "name": "iNatAg/quercus_semecarpifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110926,8 +110926,8 @@
   {
     "name": "iNatAg/quercus_suber",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110947,8 +110947,8 @@
   {
     "name": "iNatAg/quercus_virginiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110968,8 +110968,8 @@
   {
     "name": "iNatAg/quisqualis_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -110989,8 +110989,8 @@
   {
     "name": "iNatAg/ranunculus_abortivus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111010,8 +111010,8 @@
   {
     "name": "iNatAg/ranunculus_acris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111031,8 +111031,8 @@
   {
     "name": "iNatAg/ranunculus_arbortivus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111052,8 +111052,8 @@
   {
     "name": "iNatAg/ranunculus_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111073,8 +111073,8 @@
   {
     "name": "iNatAg/ranunculus_bulbosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111094,8 +111094,8 @@
   {
     "name": "iNatAg/ranunculus_californicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111115,8 +111115,8 @@
   {
     "name": "iNatAg/ranunculus_cymbalaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111136,8 +111136,8 @@
   {
     "name": "iNatAg/ranunculus_ficaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111157,8 +111157,8 @@
   {
     "name": "iNatAg/ranunculus_flabellaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111178,8 +111178,8 @@
   {
     "name": "iNatAg/ranunculus_muricatulus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111199,8 +111199,8 @@
   {
     "name": "iNatAg/ranunculus_muricatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111220,8 +111220,8 @@
   {
     "name": "iNatAg/ranunculus_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111241,8 +111241,8 @@
   {
     "name": "iNatAg/ranunculus_orthorhynchus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111262,8 +111262,8 @@
   {
     "name": "iNatAg/ranunculus_parviflorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111283,8 +111283,8 @@
   {
     "name": "iNatAg/ranunculus_sceleratus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111304,8 +111304,8 @@
   {
     "name": "iNatAg/ranunculus_testiculatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111325,8 +111325,8 @@
   {
     "name": "iNatAg/ranunculus_trichophyllus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111346,8 +111346,8 @@
   {
     "name": "iNatAg/raphanus_raphanistrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111367,8 +111367,8 @@
   {
     "name": "iNatAg/raphanus_sativus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111388,8 +111388,8 @@
   {
     "name": "iNatAg/rauvolfia_caffra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111409,8 +111409,8 @@
   {
     "name": "iNatAg/rauvolfia_serpentina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111430,8 +111430,8 @@
   {
     "name": "iNatAg/reseda_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111451,8 +111451,8 @@
   {
     "name": "iNatAg/reseda_lutea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111472,8 +111472,8 @@
   {
     "name": "iNatAg/retama_monosperma",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111493,8 +111493,8 @@
   {
     "name": "iNatAg/rhamnus_cathartica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111514,8 +111514,8 @@
   {
     "name": "iNatAg/rhamnus_prinoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111535,8 +111535,8 @@
   {
     "name": "iNatAg/rheum_palmatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111556,8 +111556,8 @@
   {
     "name": "iNatAg/rheum_rhaponticum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111577,8 +111577,8 @@
   {
     "name": "iNatAg/rhigozum_trichotomum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111598,8 +111598,8 @@
   {
     "name": "iNatAg/rhinanthus_crista-galli",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111619,8 +111619,8 @@
   {
     "name": "iNatAg/rhinanthus_minor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111640,8 +111640,8 @@
   {
     "name": "iNatAg/rhizophora_mangle",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111661,8 +111661,8 @@
   {
     "name": "iNatAg/rhizophora_mucronata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111682,8 +111682,8 @@
   {
     "name": "iNatAg/rhizophora_stylosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111703,8 +111703,8 @@
   {
     "name": "iNatAg/rhodiola_rosea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111724,8 +111724,8 @@
   {
     "name": "iNatAg/rhododendron_ferrugineum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111745,8 +111745,8 @@
   {
     "name": "iNatAg/rhus_copallinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111766,8 +111766,8 @@
   {
     "name": "iNatAg/rhus_glabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111787,8 +111787,8 @@
   {
     "name": "iNatAg/rhus_typhina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111808,8 +111808,8 @@
   {
     "name": "iNatAg/rhynchosia_minima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111829,8 +111829,8 @@
   {
     "name": "iNatAg/rhynchosia_senna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111850,8 +111850,8 @@
   {
     "name": "iNatAg/rhynchosia_sublobata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111871,8 +111871,8 @@
   {
     "name": "iNatAg/ribes_hirtellum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111892,8 +111892,8 @@
   {
     "name": "iNatAg/ribes_nigrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111913,8 +111913,8 @@
   {
     "name": "iNatAg/ribes_rubrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111934,8 +111934,8 @@
   {
     "name": "iNatAg/ribes_uva-crispa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111955,8 +111955,8 @@
   {
     "name": "iNatAg/ribes_viscosissimum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111976,8 +111976,8 @@
   {
     "name": "iNatAg/richardia_brasiliensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -111997,8 +111997,8 @@
   {
     "name": "iNatAg/richardia_scabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112018,8 +112018,8 @@
   {
     "name": "iNatAg/ricinus_communis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112039,8 +112039,8 @@
   {
     "name": "iNatAg/ricinus_comunis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112060,8 +112060,8 @@
   {
     "name": "iNatAg/rivina_humilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112081,8 +112081,8 @@
   {
     "name": "iNatAg/robinia_pseudoacacia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112102,8 +112102,8 @@
   {
     "name": "iNatAg/roemeria_refracta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112123,8 +112123,8 @@
   {
     "name": "iNatAg/rosa_canina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112144,8 +112144,8 @@
   {
     "name": "iNatAg/rosa_cinnamomea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112165,8 +112165,8 @@
   {
     "name": "iNatAg/rosa_eglanteria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112186,8 +112186,8 @@
   {
     "name": "iNatAg/rosa_pendulina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112207,8 +112207,8 @@
   {
     "name": "iNatAg/rosa_pimpinellifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112228,8 +112228,8 @@
   {
     "name": "iNatAg/rosa_rubiginosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112249,8 +112249,8 @@
   {
     "name": "iNatAg/rosa_spinosissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112270,8 +112270,8 @@
   {
     "name": "iNatAg/roseodendron_donnell-smithii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112291,8 +112291,8 @@
   {
     "name": "iNatAg/rosmarinus_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112312,8 +112312,8 @@
   {
     "name": "iNatAg/rubia_tinctorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112333,8 +112333,8 @@
   {
     "name": "iNatAg/rubus_ellipticus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112354,8 +112354,8 @@
   {
     "name": "iNatAg/rubus_fructicosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112375,8 +112375,8 @@
   {
     "name": "iNatAg/rubus_fruticosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112396,8 +112396,8 @@
   {
     "name": "iNatAg/rubus_hispidus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112417,8 +112417,8 @@
   {
     "name": "iNatAg/rubus_idaeus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112438,8 +112438,8 @@
   {
     "name": "iNatAg/rubus_moluccanus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112459,8 +112459,8 @@
   {
     "name": "iNatAg/rubus_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112480,8 +112480,8 @@
   {
     "name": "iNatAg/rubus_pensilvanicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112501,8 +112501,8 @@
   {
     "name": "iNatAg/rudbeckia_amplexicaulis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112522,8 +112522,8 @@
   {
     "name": "iNatAg/rudbeckia_hirta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112543,8 +112543,8 @@
   {
     "name": "iNatAg/rudbeckia_laciniata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112564,8 +112564,8 @@
   {
     "name": "iNatAg/rudbeckia_triloba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112585,8 +112585,8 @@
   {
     "name": "iNatAg/rumex_acetosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112606,8 +112606,8 @@
   {
     "name": "iNatAg/rumex_acetosella",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112627,8 +112627,8 @@
   {
     "name": "iNatAg/rumex_aquaticus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112648,8 +112648,8 @@
   {
     "name": "iNatAg/rumex_crispus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112669,8 +112669,8 @@
   {
     "name": "iNatAg/rumex_dentatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112690,8 +112690,8 @@
   {
     "name": "iNatAg/rumex_hymenosepalus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112711,8 +112711,8 @@
   {
     "name": "iNatAg/rumex_longifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112732,8 +112732,8 @@
   {
     "name": "iNatAg/rumex_maritimus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112753,8 +112753,8 @@
   {
     "name": "iNatAg/rumex_obtusifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112774,8 +112774,8 @@
   {
     "name": "iNatAg/rumex_patienta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112795,8 +112795,8 @@
   {
     "name": "iNatAg/rumex_patientia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112816,8 +112816,8 @@
   {
     "name": "iNatAg/rumex_pseudonatronatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112837,8 +112837,8 @@
   {
     "name": "iNatAg/rumex_pulcher",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112858,8 +112858,8 @@
   {
     "name": "iNatAg/rumex_verticillatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112879,8 +112879,8 @@
   {
     "name": "iNatAg/ruppia_maritima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112900,8 +112900,8 @@
   {
     "name": "iNatAg/ruscus_aculeatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112921,8 +112921,8 @@
   {
     "name": "iNatAg/ruta_graveolens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112942,8 +112942,8 @@
   {
     "name": "iNatAg/saccharum_officinarum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112963,8 +112963,8 @@
   {
     "name": "iNatAg/saccharum_sinense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -112984,8 +112984,8 @@
   {
     "name": "iNatAg/saccharum_spontaneum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113005,8 +113005,8 @@
   {
     "name": "iNatAg/sacorstemma_cynanchoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113026,8 +113026,8 @@
   {
     "name": "iNatAg/sagina_procumbens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113047,8 +113047,8 @@
   {
     "name": "iNatAg/sagittaria_kurziana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113068,8 +113068,8 @@
   {
     "name": "iNatAg/sagittaria_lancifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113089,8 +113089,8 @@
   {
     "name": "iNatAg/sagittaria_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113110,8 +113110,8 @@
   {
     "name": "iNatAg/sagittaria_sagittifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113131,8 +113131,8 @@
   {
     "name": "iNatAg/salacca_wallichiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113152,8 +113152,8 @@
   {
     "name": "iNatAg/salacca_zalacca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113173,8 +113173,8 @@
   {
     "name": "iNatAg/salicornia_bigelovii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113194,8 +113194,8 @@
   {
     "name": "iNatAg/salix_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113215,8 +113215,8 @@
   {
     "name": "iNatAg/salix_caprea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113236,8 +113236,8 @@
   {
     "name": "iNatAg/salix_laevigata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113257,8 +113257,8 @@
   {
     "name": "iNatAg/salix_pentandra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113278,8 +113278,8 @@
   {
     "name": "iNatAg/salix_viminalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113299,8 +113299,8 @@
   {
     "name": "iNatAg/salsola_kali",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113320,8 +113320,8 @@
   {
     "name": "iNatAg/salsola_tragus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113341,8 +113341,8 @@
   {
     "name": "iNatAg/salsola_vermiculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113362,8 +113362,8 @@
   {
     "name": "iNatAg/salvadora_persica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113383,8 +113383,8 @@
   {
     "name": "iNatAg/salvia_lyrata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113404,8 +113404,8 @@
   {
     "name": "iNatAg/salvia_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113425,8 +113425,8 @@
   {
     "name": "iNatAg/salvia_sclarea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113446,8 +113446,8 @@
   {
     "name": "iNatAg/salvia_verticillata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113467,8 +113467,8 @@
   {
     "name": "iNatAg/salvinia_auriculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113488,8 +113488,8 @@
   {
     "name": "iNatAg/samanea_saman",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113509,8 +113509,8 @@
   {
     "name": "iNatAg/sambucus_canadensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113530,8 +113530,8 @@
   {
     "name": "iNatAg/sambucus_canadiensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113551,8 +113551,8 @@
   {
     "name": "iNatAg/sambucus_cerulea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113572,8 +113572,8 @@
   {
     "name": "iNatAg/sambucus_ebulus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113593,8 +113593,8 @@
   {
     "name": "iNatAg/sambucus_nigra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113614,8 +113614,8 @@
   {
     "name": "iNatAg/sambucus_racemosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113635,8 +113635,8 @@
   {
     "name": "iNatAg/samolus_parviflorus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113656,8 +113656,8 @@
   {
     "name": "iNatAg/samolus_valerandi",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113677,8 +113677,8 @@
   {
     "name": "iNatAg/sanguisorba_minor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113698,8 +113698,8 @@
   {
     "name": "iNatAg/sanguisorba_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113719,8 +113719,8 @@
   {
     "name": "iNatAg/sanicula_europaea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113740,8 +113740,8 @@
   {
     "name": "iNatAg/santalum_acuminatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113761,8 +113761,8 @@
   {
     "name": "iNatAg/santalum_album",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113782,8 +113782,8 @@
   {
     "name": "iNatAg/santolina_chamaecyparissus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113803,8 +113803,8 @@
   {
     "name": "iNatAg/sapindus_emarginatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113824,8 +113824,8 @@
   {
     "name": "iNatAg/sapindus_saponaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113845,8 +113845,8 @@
   {
     "name": "iNatAg/sapium_sebiferum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113866,8 +113866,8 @@
   {
     "name": "iNatAg/saponaria_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113887,8 +113887,8 @@
   {
     "name": "iNatAg/sarcostemma_cynanchoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113908,8 +113908,8 @@
   {
     "name": "iNatAg/satureja_hortensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113929,8 +113929,8 @@
   {
     "name": "iNatAg/satureja_montana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113950,8 +113950,8 @@
   {
     "name": "iNatAg/sauropus_androgynus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113971,8 +113971,8 @@
   {
     "name": "iNatAg/saururus_cernuus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -113992,8 +113992,8 @@
   {
     "name": "iNatAg/scandix_pecten-veneris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114013,8 +114013,8 @@
   {
     "name": "iNatAg/schima_wallichii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114034,8 +114034,8 @@
   {
     "name": "iNatAg/schinus_molle",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114055,8 +114055,8 @@
   {
     "name": "iNatAg/schinus_terebinthifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114076,8 +114076,8 @@
   {
     "name": "iNatAg/schinus_terebinthifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114097,8 +114097,8 @@
   {
     "name": "iNatAg/schismus_arabicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114118,8 +114118,8 @@
   {
     "name": "iNatAg/schizolobium_parahyba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114139,8 +114139,8 @@
   {
     "name": "iNatAg/schizomeria_ovata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114160,8 +114160,8 @@
   {
     "name": "iNatAg/schleichera_oleosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114181,8 +114181,8 @@
   {
     "name": "iNatAg/scirpus_lacustris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114202,8 +114202,8 @@
   {
     "name": "iNatAg/scleranthus_annuus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114223,8 +114223,8 @@
   {
     "name": "iNatAg/sclerocarya_caffra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114244,8 +114244,8 @@
   {
     "name": "iNatAg/scoparia_dulcis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114265,8 +114265,8 @@
   {
     "name": "iNatAg/scorzonera_laciniata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114286,8 +114286,8 @@
   {
     "name": "iNatAg/scrophularia_lanceolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114307,8 +114307,8 @@
   {
     "name": "iNatAg/searsia_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114328,8 +114328,8 @@
   {
     "name": "iNatAg/secale_cereale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114349,8 +114349,8 @@
   {
     "name": "iNatAg/secale_montanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114370,8 +114370,8 @@
   {
     "name": "iNatAg/sechium_edule",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114391,8 +114391,8 @@
   {
     "name": "iNatAg/securidaca_longepedunculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114412,8 +114412,8 @@
   {
     "name": "iNatAg/securidaca_longipedunculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114433,8 +114433,8 @@
   {
     "name": "iNatAg/sedum_acre",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114454,8 +114454,8 @@
   {
     "name": "iNatAg/sedum_telephium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114475,8 +114475,8 @@
   {
     "name": "iNatAg/sehima_nervosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114496,8 +114496,8 @@
   {
     "name": "iNatAg/sempervivum_arachnoideum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114517,8 +114517,8 @@
   {
     "name": "iNatAg/sempervivum_tectorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114538,8 +114538,8 @@
   {
     "name": "iNatAg/senecio_elegans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114559,8 +114559,8 @@
   {
     "name": "iNatAg/senecio_jacobaea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114580,8 +114580,8 @@
   {
     "name": "iNatAg/senecio_madagascariensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114601,8 +114601,8 @@
   {
     "name": "iNatAg/senecio_plattensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114622,8 +114622,8 @@
   {
     "name": "iNatAg/senecio_squalidus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114643,8 +114643,8 @@
   {
     "name": "iNatAg/senecio_sylvaticus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114664,8 +114664,8 @@
   {
     "name": "iNatAg/senecio_viscosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114685,8 +114685,8 @@
   {
     "name": "iNatAg/senecio_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114706,8 +114706,8 @@
   {
     "name": "iNatAg/senna_spectabilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114727,8 +114727,8 @@
   {
     "name": "iNatAg/serenoa_repens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114748,8 +114748,8 @@
   {
     "name": "iNatAg/sesamum_indicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114769,8 +114769,8 @@
   {
     "name": "iNatAg/sesbania_bispinosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114790,8 +114790,8 @@
   {
     "name": "iNatAg/sesbania_cannabina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114811,8 +114811,8 @@
   {
     "name": "iNatAg/sesbania_exaltata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114832,8 +114832,8 @@
   {
     "name": "iNatAg/sesbania_formosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114853,8 +114853,8 @@
   {
     "name": "iNatAg/sesbania_grandiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114874,8 +114874,8 @@
   {
     "name": "iNatAg/sesbania_pachycarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114895,8 +114895,8 @@
   {
     "name": "iNatAg/sesbania_sesban",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114916,8 +114916,8 @@
   {
     "name": "iNatAg/setaria_incrassata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114937,8 +114937,8 @@
   {
     "name": "iNatAg/setaria_italica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114958,8 +114958,8 @@
   {
     "name": "iNatAg/setaria_lindenbergiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -114979,8 +114979,8 @@
   {
     "name": "iNatAg/setaria_pumila",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115000,8 +115000,8 @@
   {
     "name": "iNatAg/seymeria_pectinata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115021,8 +115021,8 @@
   {
     "name": "iNatAg/shorea_robusta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115042,8 +115042,8 @@
   {
     "name": "iNatAg/shorea_talura",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115063,8 +115063,8 @@
   {
     "name": "iNatAg/sicyos_angulatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115084,8 +115084,8 @@
   {
     "name": "iNatAg/sida_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115105,8 +115105,8 @@
   {
     "name": "iNatAg/sida_cordifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115126,8 +115126,8 @@
   {
     "name": "iNatAg/sida_spinosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115147,8 +115147,8 @@
   {
     "name": "iNatAg/silene_antirrhina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115168,8 +115168,8 @@
   {
     "name": "iNatAg/silene_armeria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115189,8 +115189,8 @@
   {
     "name": "iNatAg/silene_conica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115210,8 +115210,8 @@
   {
     "name": "iNatAg/silene_conoidea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115231,8 +115231,8 @@
   {
     "name": "iNatAg/silene_gallica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115252,8 +115252,8 @@
   {
     "name": "iNatAg/silene_noctiflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115273,8 +115273,8 @@
   {
     "name": "iNatAg/silene_pendula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115294,8 +115294,8 @@
   {
     "name": "iNatAg/silphium_asperrimum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115315,8 +115315,8 @@
   {
     "name": "iNatAg/silphium_integrifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115336,8 +115336,8 @@
   {
     "name": "iNatAg/silphium_laciniatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115357,8 +115357,8 @@
   {
     "name": "iNatAg/silybum_marianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115378,8 +115378,8 @@
   {
     "name": "iNatAg/simarouba_glauca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115399,8 +115399,8 @@
   {
     "name": "iNatAg/simmondsia_chinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115420,8 +115420,8 @@
   {
     "name": "iNatAg/simsia_auriculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115441,8 +115441,8 @@
   {
     "name": "iNatAg/sinapis_alba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115462,8 +115462,8 @@
   {
     "name": "iNatAg/sinapis_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115483,8 +115483,8 @@
   {
     "name": "iNatAg/sinapis_incana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115504,8 +115504,8 @@
   {
     "name": "iNatAg/siphonochilus_aethiopicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115525,8 +115525,8 @@
   {
     "name": "iNatAg/sisymbrium_altissimum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115546,8 +115546,8 @@
   {
     "name": "iNatAg/sisymbrium_erysimoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115567,8 +115567,8 @@
   {
     "name": "iNatAg/sisymbrium_irio",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115588,8 +115588,8 @@
   {
     "name": "iNatAg/sisymbrium_officinale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115609,8 +115609,8 @@
   {
     "name": "iNatAg/sisymbrium_orientale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115630,8 +115630,8 @@
   {
     "name": "iNatAg/sisymbrium_sophia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115651,8 +115651,8 @@
   {
     "name": "iNatAg/sisyrinchium_montanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115672,8 +115672,8 @@
   {
     "name": "iNatAg/sloanea_woollsii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115693,8 +115693,8 @@
   {
     "name": "iNatAg/smilax_aspera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115714,8 +115714,8 @@
   {
     "name": "iNatAg/smilax_bona-nox",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115735,8 +115735,8 @@
   {
     "name": "iNatAg/smilax_laurifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115756,8 +115756,8 @@
   {
     "name": "iNatAg/smilax_rotundifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115777,8 +115777,8 @@
   {
     "name": "iNatAg/solanum_aethiopicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115798,8 +115798,8 @@
   {
     "name": "iNatAg/solanum_americanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115819,8 +115819,8 @@
   {
     "name": "iNatAg/solanum_capsicoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115840,8 +115840,8 @@
   {
     "name": "iNatAg/solanum_carolinense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115861,8 +115861,8 @@
   {
     "name": "iNatAg/solanum_coriaceum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115882,8 +115882,8 @@
   {
     "name": "iNatAg/solanum_dimidiatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115903,8 +115903,8 @@
   {
     "name": "iNatAg/solanum_diphyllum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115924,8 +115924,8 @@
   {
     "name": "iNatAg/solanum_dulcamara",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115945,8 +115945,8 @@
   {
     "name": "iNatAg/solanum_elaeagnifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115966,8 +115966,8 @@
   {
     "name": "iNatAg/solanum_eleagnifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -115987,8 +115987,8 @@
   {
     "name": "iNatAg/solanum_ellipticum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116008,8 +116008,8 @@
   {
     "name": "iNatAg/solanum_ferox",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116029,8 +116029,8 @@
   {
     "name": "iNatAg/solanum_heterodoxum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116050,8 +116050,8 @@
   {
     "name": "iNatAg/solanum_incanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116071,8 +116071,8 @@
   {
     "name": "iNatAg/solanum_jamaicense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116092,8 +116092,8 @@
   {
     "name": "iNatAg/solanum_lanceolatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116113,8 +116113,8 @@
   {
     "name": "iNatAg/solanum_macrocarpon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116134,8 +116134,8 @@
   {
     "name": "iNatAg/solanum_mammosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116155,8 +116155,8 @@
   {
     "name": "iNatAg/solanum_marginatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116176,8 +116176,8 @@
   {
     "name": "iNatAg/solanum_mauritianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116197,8 +116197,8 @@
   {
     "name": "iNatAg/solanum_melongena",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116218,8 +116218,8 @@
   {
     "name": "iNatAg/solanum_muricatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116239,8 +116239,8 @@
   {
     "name": "iNatAg/solanum_nigrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116260,8 +116260,8 @@
   {
     "name": "iNatAg/solanum_physalifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116281,8 +116281,8 @@
   {
     "name": "iNatAg/solanum_pseudo-capsicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116302,8 +116302,8 @@
   {
     "name": "iNatAg/solanum_pseudocapsicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116323,8 +116323,8 @@
   {
     "name": "iNatAg/solanum_quitoense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116344,8 +116344,8 @@
   {
     "name": "iNatAg/solanum_sisymbrifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116365,8 +116365,8 @@
   {
     "name": "iNatAg/solanum_sisymbriifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116386,8 +116386,8 @@
   {
     "name": "iNatAg/solanum_tampicense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116407,8 +116407,8 @@
   {
     "name": "iNatAg/solanum_torvum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116428,8 +116428,8 @@
   {
     "name": "iNatAg/solanum_tuberosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116449,8 +116449,8 @@
   {
     "name": "iNatAg/solanum_violaceum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116470,8 +116470,8 @@
   {
     "name": "iNatAg/soldanella_alpina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116491,8 +116491,8 @@
   {
     "name": "iNatAg/solidago_californica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116512,8 +116512,8 @@
   {
     "name": "iNatAg/solidago_canadensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116533,8 +116533,8 @@
   {
     "name": "iNatAg/solidago_fistulosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116554,8 +116554,8 @@
   {
     "name": "iNatAg/solidago_missouriensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116575,8 +116575,8 @@
   {
     "name": "iNatAg/solidago_nemoralis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116596,8 +116596,8 @@
   {
     "name": "iNatAg/solidago_rigida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116617,8 +116617,8 @@
   {
     "name": "iNatAg/solidago_sempervirens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116638,8 +116638,8 @@
   {
     "name": "iNatAg/solidago_virgaurea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116659,8 +116659,8 @@
   {
     "name": "iNatAg/sonchus_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116680,8 +116680,8 @@
   {
     "name": "iNatAg/sonchus_oleraceus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116701,8 +116701,8 @@
   {
     "name": "iNatAg/sonchus_palustris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116722,8 +116722,8 @@
   {
     "name": "iNatAg/sonneratia_apetala",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116743,8 +116743,8 @@
   {
     "name": "iNatAg/sonneratia_caseolaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116764,8 +116764,8 @@
   {
     "name": "iNatAg/sorbus_aucuparia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116785,8 +116785,8 @@
   {
     "name": "iNatAg/sorbus_domestica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116806,8 +116806,8 @@
   {
     "name": "iNatAg/sorghum_bicolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116827,8 +116827,8 @@
   {
     "name": "iNatAg/sorghum_drummondii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116848,8 +116848,8 @@
   {
     "name": "iNatAg/sorghum_halepense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116869,8 +116869,8 @@
   {
     "name": "iNatAg/soymida_febrifuga",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116890,8 +116890,8 @@
   {
     "name": "iNatAg/sparganium_americanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116911,8 +116911,8 @@
   {
     "name": "iNatAg/sparganium_erectum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116932,8 +116932,8 @@
   {
     "name": "iNatAg/spartina_pectinata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116953,8 +116953,8 @@
   {
     "name": "iNatAg/spartium_junceum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116974,8 +116974,8 @@
   {
     "name": "iNatAg/spathodea_campanulata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -116995,8 +116995,8 @@
   {
     "name": "iNatAg/spergula_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117016,8 +117016,8 @@
   {
     "name": "iNatAg/spermacoce_verticillata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117037,8 +117037,8 @@
   {
     "name": "iNatAg/spinacia_oleracea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117058,8 +117058,8 @@
   {
     "name": "iNatAg/spinifex_hirsutus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117079,8 +117079,8 @@
   {
     "name": "iNatAg/spirea_tomentosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117100,8 +117100,8 @@
   {
     "name": "iNatAg/spodiopogon_sibiricus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117121,8 +117121,8 @@
   {
     "name": "iNatAg/spondias_cythera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117142,8 +117142,8 @@
   {
     "name": "iNatAg/spondias_mombin",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117163,8 +117163,8 @@
   {
     "name": "iNatAg/spondias_purpurea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117184,8 +117184,8 @@
   {
     "name": "iNatAg/sporobolus_airoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117205,8 +117205,8 @@
   {
     "name": "iNatAg/sporobolus_fimbriatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117226,8 +117226,8 @@
   {
     "name": "iNatAg/sporobolus_maritimus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117247,8 +117247,8 @@
   {
     "name": "iNatAg/sporobolus_neglectus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117268,8 +117268,8 @@
   {
     "name": "iNatAg/sporobolus_spicatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117289,8 +117289,8 @@
   {
     "name": "iNatAg/sporobolus_virginicus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117310,8 +117310,8 @@
   {
     "name": "iNatAg/stachys_affinis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117331,8 +117331,8 @@
   {
     "name": "iNatAg/stachys_palustris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117352,8 +117352,8 @@
   {
     "name": "iNatAg/stachytarpheta_incana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117373,8 +117373,8 @@
   {
     "name": "iNatAg/stachytarpheta_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117394,8 +117394,8 @@
   {
     "name": "iNatAg/stellaria_graminea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117415,8 +117415,8 @@
   {
     "name": "iNatAg/stellaria_holostea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117436,8 +117436,8 @@
   {
     "name": "iNatAg/stellaria_media",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117457,8 +117457,8 @@
   {
     "name": "iNatAg/stenotaphrum_secundatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117478,8 +117478,8 @@
   {
     "name": "iNatAg/sterculia_foetida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117499,8 +117499,8 @@
   {
     "name": "iNatAg/sterculia_urens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117520,8 +117520,8 @@
   {
     "name": "iNatAg/sterculia_villosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117541,8 +117541,8 @@
   {
     "name": "iNatAg/stereospermum_kunthianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117562,8 +117562,8 @@
   {
     "name": "iNatAg/stevia_rebaudiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117583,8 +117583,8 @@
   {
     "name": "iNatAg/stipa_baicalensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117604,8 +117604,8 @@
   {
     "name": "iNatAg/stipa_brachychaeta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117625,8 +117625,8 @@
   {
     "name": "iNatAg/stipa_capillata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117646,8 +117646,8 @@
   {
     "name": "iNatAg/stipa_glareosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117667,8 +117667,8 @@
   {
     "name": "iNatAg/stipa_grandis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117688,8 +117688,8 @@
   {
     "name": "iNatAg/stipa_krylovii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117709,8 +117709,8 @@
   {
     "name": "iNatAg/stipa_lagascae",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117730,8 +117730,8 @@
   {
     "name": "iNatAg/stipa_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117751,8 +117751,8 @@
   {
     "name": "iNatAg/stipa_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117772,8 +117772,8 @@
   {
     "name": "iNatAg/stipa_tenacissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117793,8 +117793,8 @@
   {
     "name": "iNatAg/stipa_trichotoma",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117814,8 +117814,8 @@
   {
     "name": "iNatAg/stipagrostis_amabilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117835,8 +117835,8 @@
   {
     "name": "iNatAg/stipagrostis_zeyheri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117856,8 +117856,8 @@
   {
     "name": "iNatAg/stratiotes_aloides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117877,8 +117877,8 @@
   {
     "name": "iNatAg/strychnos_cocculoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117898,8 +117898,8 @@
   {
     "name": "iNatAg/strychnos_innocua",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117919,8 +117919,8 @@
   {
     "name": "iNatAg/strychnos_spinosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117940,8 +117940,8 @@
   {
     "name": "iNatAg/stylidium_desertorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117961,8 +117961,8 @@
   {
     "name": "iNatAg/stylosanthes_capitata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -117982,8 +117982,8 @@
   {
     "name": "iNatAg/stylosanthes_fruticosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118003,8 +118003,8 @@
   {
     "name": "iNatAg/stylosanthes_hamata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118024,8 +118024,8 @@
   {
     "name": "iNatAg/stylosanthes_humilis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118045,8 +118045,8 @@
   {
     "name": "iNatAg/stylosanthes_scabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118066,8 +118066,8 @@
   {
     "name": "iNatAg/stylosanthes_viscosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118087,8 +118087,8 @@
   {
     "name": "iNatAg/succisa_pratensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118108,8 +118108,8 @@
   {
     "name": "iNatAg/swertia_baicalensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118129,8 +118129,8 @@
   {
     "name": "iNatAg/swietenia_macrophylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118150,8 +118150,8 @@
   {
     "name": "iNatAg/swietenia_mahogani",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118171,8 +118171,8 @@
   {
     "name": "iNatAg/symphoricarpos_mollis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118192,8 +118192,8 @@
   {
     "name": "iNatAg/symphoricarpos_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118213,8 +118213,8 @@
   {
     "name": "iNatAg/symphoricarpos_orbiculatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118234,8 +118234,8 @@
   {
     "name": "iNatAg/symphoricarpos_rotundifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118255,8 +118255,8 @@
   {
     "name": "iNatAg/symphytum_officinale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118276,8 +118276,8 @@
   {
     "name": "iNatAg/syncarpia_glomulifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118297,8 +118297,8 @@
   {
     "name": "iNatAg/syncarpia_hillii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118318,8 +118318,8 @@
   {
     "name": "iNatAg/syzygium_cordatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118339,8 +118339,8 @@
   {
     "name": "iNatAg/syzygium_cumini",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118360,8 +118360,8 @@
   {
     "name": "iNatAg/syzygium_guineense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118381,8 +118381,8 @@
   {
     "name": "iNatAg/syzygium_malaccense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118402,8 +118402,8 @@
   {
     "name": "iNatAg/syzygium_taiwanicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118423,8 +118423,8 @@
   {
     "name": "iNatAg/tabebuia_rosea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118444,8 +118444,8 @@
   {
     "name": "iNatAg/tabebuia_serratifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118465,8 +118465,8 @@
   {
     "name": "iNatAg/tagetes_minuta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118486,8 +118486,8 @@
   {
     "name": "iNatAg/talinum_triangulare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118507,8 +118507,8 @@
   {
     "name": "iNatAg/tamarindus_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118528,8 +118528,8 @@
   {
     "name": "iNatAg/tamarix_aphylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118549,8 +118549,8 @@
   {
     "name": "iNatAg/tamarix_chinensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118570,8 +118570,8 @@
   {
     "name": "iNatAg/tamarix_gallica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118591,8 +118591,8 @@
   {
     "name": "iNatAg/tamarix_parviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118612,8 +118612,8 @@
   {
     "name": "iNatAg/tanacetum_balsamita",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118633,8 +118633,8 @@
   {
     "name": "iNatAg/tanacetum_vulgare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118654,8 +118654,8 @@
   {
     "name": "iNatAg/taraxacum_officinale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118675,8 +118675,8 @@
   {
     "name": "iNatAg/taraxia_breviflora",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118696,8 +118696,8 @@
   {
     "name": "iNatAg/tarchonanthus_camphoratus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118717,8 +118717,8 @@
   {
     "name": "iNatAg/taxodium_distichum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118738,8 +118738,8 @@
   {
     "name": "iNatAg/taxus_baccata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118759,8 +118759,8 @@
   {
     "name": "iNatAg/tecoma_stans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118780,8 +118780,8 @@
   {
     "name": "iNatAg/tectona_grandis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118801,8 +118801,8 @@
   {
     "name": "iNatAg/tephrosia_candida",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118822,8 +118822,8 @@
   {
     "name": "iNatAg/tephrosia_lupinifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118843,8 +118843,8 @@
   {
     "name": "iNatAg/tephrosia_obovata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118864,8 +118864,8 @@
   {
     "name": "iNatAg/tephrosia_purpurea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118885,8 +118885,8 @@
   {
     "name": "iNatAg/tephrosia_vogelii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118906,8 +118906,8 @@
   {
     "name": "iNatAg/teramnus_labialis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118927,8 +118927,8 @@
   {
     "name": "iNatAg/terminalia_arjuna",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118948,8 +118948,8 @@
   {
     "name": "iNatAg/terminalia_bellirica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118969,8 +118969,8 @@
   {
     "name": "iNatAg/terminalia_brownii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -118990,8 +118990,8 @@
   {
     "name": "iNatAg/terminalia_calamansanai",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119011,8 +119011,8 @@
   {
     "name": "iNatAg/terminalia_catappa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119032,8 +119032,8 @@
   {
     "name": "iNatAg/terminalia_chebula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119053,8 +119053,8 @@
   {
     "name": "iNatAg/terminalia_ivorensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119074,8 +119074,8 @@
   {
     "name": "iNatAg/terminalia_mantaly",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119095,8 +119095,8 @@
   {
     "name": "iNatAg/terminalia_myriocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119116,8 +119116,8 @@
   {
     "name": "iNatAg/terminalia_paniculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119137,8 +119137,8 @@
   {
     "name": "iNatAg/terminalia_prunioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119158,8 +119158,8 @@
   {
     "name": "iNatAg/terminalia_sericocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119179,8 +119179,8 @@
   {
     "name": "iNatAg/terminalia_tomentosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119200,8 +119200,8 @@
   {
     "name": "iNatAg/tetradymia_canescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119221,8 +119221,8 @@
   {
     "name": "iNatAg/tetragonia_tetragonioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119242,8 +119242,8 @@
   {
     "name": "iNatAg/teucrium_botrys",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119263,8 +119263,8 @@
   {
     "name": "iNatAg/teucrium_canadense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119284,8 +119284,8 @@
   {
     "name": "iNatAg/teucrium_chamaedrys",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119305,8 +119305,8 @@
   {
     "name": "iNatAg/teucrium_polium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119326,8 +119326,8 @@
   {
     "name": "iNatAg/thalia_geniculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119347,8 +119347,8 @@
   {
     "name": "iNatAg/thalictrum_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119368,8 +119368,8 @@
   {
     "name": "iNatAg/thaumatococcus_daniellii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119389,8 +119389,8 @@
   {
     "name": "iNatAg/themeda_australis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119410,8 +119410,8 @@
   {
     "name": "iNatAg/themeda_quadrivalvis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119431,8 +119431,8 @@
   {
     "name": "iNatAg/themeda_triandra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119452,8 +119452,8 @@
   {
     "name": "iNatAg/theobroma_bicolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119473,8 +119473,8 @@
   {
     "name": "iNatAg/theobroma_cacao",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119494,8 +119494,8 @@
   {
     "name": "iNatAg/theobroma_grandiflorum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119515,8 +119515,8 @@
   {
     "name": "iNatAg/thermopsis_montana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119536,8 +119536,8 @@
   {
     "name": "iNatAg/thermopsis_rhombifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119557,8 +119557,8 @@
   {
     "name": "iNatAg/thespesia_populnea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119578,8 +119578,8 @@
   {
     "name": "iNatAg/thlaspi_arvense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119599,8 +119599,8 @@
   {
     "name": "iNatAg/thlaspi_perfoliatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119620,8 +119620,8 @@
   {
     "name": "iNatAg/thuja_occidentalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119641,8 +119641,8 @@
   {
     "name": "iNatAg/thymus_serphyllum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119662,8 +119662,8 @@
   {
     "name": "iNatAg/thymus_serpyllum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119683,8 +119683,8 @@
   {
     "name": "iNatAg/thymus_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119704,8 +119704,8 @@
   {
     "name": "iNatAg/thyrsostachys_siamensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119725,8 +119725,8 @@
   {
     "name": "iNatAg/thysanolaena_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119746,8 +119746,8 @@
   {
     "name": "iNatAg/tilia_cordata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119767,8 +119767,8 @@
   {
     "name": "iNatAg/tilia_platyphyllos",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119788,8 +119788,8 @@
   {
     "name": "iNatAg/tipuana_tipu",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119809,8 +119809,8 @@
   {
     "name": "iNatAg/tithonia_diversifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119830,8 +119830,8 @@
   {
     "name": "iNatAg/toona_ciliata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119851,8 +119851,8 @@
   {
     "name": "iNatAg/torenia_glabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119872,8 +119872,8 @@
   {
     "name": "iNatAg/toxicodendron_pubescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119893,8 +119893,8 @@
   {
     "name": "iNatAg/trachypogon_spicatus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119914,8 +119914,8 @@
   {
     "name": "iNatAg/tradescantia_bracteata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119935,8 +119935,8 @@
   {
     "name": "iNatAg/tradescantia_fluminensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119956,8 +119956,8 @@
   {
     "name": "iNatAg/tradescantia_ohiensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119977,8 +119977,8 @@
   {
     "name": "iNatAg/tradescantia_virginiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -119998,8 +119998,8 @@
   {
     "name": "iNatAg/tragia_betonicifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120019,8 +120019,8 @@
   {
     "name": "iNatAg/tragopogon_lamottei",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120040,8 +120040,8 @@
   {
     "name": "iNatAg/tragopogon_porrifolius",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120061,8 +120061,8 @@
   {
     "name": "iNatAg/tragopogon_pratensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120082,8 +120082,8 @@
   {
     "name": "iNatAg/tragus_koelerioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120103,8 +120103,8 @@
   {
     "name": "iNatAg/trapa_natans",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120124,8 +120124,8 @@
   {
     "name": "iNatAg/trema_orientale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120145,8 +120145,8 @@
   {
     "name": "iNatAg/trianthema_portulacastrum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120166,8 +120166,8 @@
   {
     "name": "iNatAg/tribulus_cistoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120187,8 +120187,8 @@
   {
     "name": "iNatAg/tribulus_terrestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120208,8 +120208,8 @@
   {
     "name": "iNatAg/trichanthera_gigantea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120229,8 +120229,8 @@
   {
     "name": "iNatAg/trichoneura_grandiglumis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120250,8 +120250,8 @@
   {
     "name": "iNatAg/trichosanthes_cucumerina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120271,8 +120271,8 @@
   {
     "name": "iNatAg/trichostema_lanceolatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120292,8 +120292,8 @@
   {
     "name": "iNatAg/tridax_procumbens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120313,8 +120313,8 @@
   {
     "name": "iNatAg/trifolium_africanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120334,8 +120334,8 @@
   {
     "name": "iNatAg/trifolium_alexandrinum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120355,8 +120355,8 @@
   {
     "name": "iNatAg/trifolium_ambiguum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120376,8 +120376,8 @@
   {
     "name": "iNatAg/trifolium_angustifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120397,8 +120397,8 @@
   {
     "name": "iNatAg/trifolium_arvense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120418,8 +120418,8 @@
   {
     "name": "iNatAg/trifolium_burchellianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120439,8 +120439,8 @@
   {
     "name": "iNatAg/trifolium_campestre",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120460,8 +120460,8 @@
   {
     "name": "iNatAg/trifolium_carolinianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120481,8 +120481,8 @@
   {
     "name": "iNatAg/trifolium_cherleri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120502,8 +120502,8 @@
   {
     "name": "iNatAg/trifolium_dubium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120523,8 +120523,8 @@
   {
     "name": "iNatAg/trifolium_fragiferum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120544,8 +120544,8 @@
   {
     "name": "iNatAg/trifolium_glanduliferum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120565,8 +120565,8 @@
   {
     "name": "iNatAg/trifolium_glomeratum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120586,8 +120586,8 @@
   {
     "name": "iNatAg/trifolium_hirtum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120607,8 +120607,8 @@
   {
     "name": "iNatAg/trifolium_hybridum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120628,8 +120628,8 @@
   {
     "name": "iNatAg/trifolium_incarnatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120649,8 +120649,8 @@
   {
     "name": "iNatAg/trifolium_medium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120670,8 +120670,8 @@
   {
     "name": "iNatAg/trifolium_michelianum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120691,8 +120691,8 @@
   {
     "name": "iNatAg/trifolium_nigrescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120712,8 +120712,8 @@
   {
     "name": "iNatAg/trifolium_patens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120733,8 +120733,8 @@
   {
     "name": "iNatAg/trifolium_pilulare",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120754,8 +120754,8 @@
   {
     "name": "iNatAg/trifolium_polymorphum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120775,8 +120775,8 @@
   {
     "name": "iNatAg/trifolium_pratense",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120796,8 +120796,8 @@
   {
     "name": "iNatAg/trifolium_reflexum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120817,8 +120817,8 @@
   {
     "name": "iNatAg/trifolium_repens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120838,8 +120838,8 @@
   {
     "name": "iNatAg/trifolium_resupinatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120859,8 +120859,8 @@
   {
     "name": "iNatAg/trifolium_subterraneum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120880,8 +120880,8 @@
   {
     "name": "iNatAg/trifolium_tomentosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120901,8 +120901,8 @@
   {
     "name": "iNatAg/trifolium_variegatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120922,8 +120922,8 @@
   {
     "name": "iNatAg/trifolium_vesiculosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120943,8 +120943,8 @@
   {
     "name": "iNatAg/trifolium_wormskioldii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120964,8 +120964,8 @@
   {
     "name": "iNatAg/triglochin_maritima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -120985,8 +120985,8 @@
   {
     "name": "iNatAg/triglochin_maritimum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121006,8 +121006,8 @@
   {
     "name": "iNatAg/triglochin_palustre",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121027,8 +121027,8 @@
   {
     "name": "iNatAg/trigonella_foenum-graecum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121048,8 +121048,8 @@
   {
     "name": "iNatAg/tripsacum_dactyloides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121069,8 +121069,8 @@
   {
     "name": "iNatAg/trisetum_flavescens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121090,8 +121090,8 @@
   {
     "name": "iNatAg/tristachya_leucothrix",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121111,8 +121111,8 @@
   {
     "name": "iNatAg/triticum_aestivum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121132,8 +121132,8 @@
   {
     "name": "iNatAg/triticum_dicoccoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121153,8 +121153,8 @@
   {
     "name": "iNatAg/triticum_durum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121174,8 +121174,8 @@
   {
     "name": "iNatAg/triticum_spelta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121195,8 +121195,8 @@
   {
     "name": "iNatAg/triumfetta_rhomboidea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121216,8 +121216,8 @@
   {
     "name": "iNatAg/triumfetta_semitriloba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121237,8 +121237,8 @@
   {
     "name": "iNatAg/trollius_europaeus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121258,8 +121258,8 @@
   {
     "name": "iNatAg/tropaeolum_majus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121279,8 +121279,8 @@
   {
     "name": "iNatAg/tropaeolum_tuberosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121300,8 +121300,8 @@
   {
     "name": "iNatAg/tropidocarpum_gracile",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121321,8 +121321,8 @@
   {
     "name": "iNatAg/turritis_glabra",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121342,8 +121342,8 @@
   {
     "name": "iNatAg/tussilago_farfara",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121363,8 +121363,8 @@
   {
     "name": "iNatAg/tylosema_esculentum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121384,8 +121384,8 @@
   {
     "name": "iNatAg/typha_angustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121405,8 +121405,8 @@
   {
     "name": "iNatAg/typha_domingensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121426,8 +121426,8 @@
   {
     "name": "iNatAg/typha_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121447,8 +121447,8 @@
   {
     "name": "iNatAg/uapaca_kirkiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121468,8 +121468,8 @@
   {
     "name": "iNatAg/ulex_europaeus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121489,8 +121489,8 @@
   {
     "name": "iNatAg/ullucus_tuberosus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121510,8 +121510,8 @@
   {
     "name": "iNatAg/ulmus_procera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121531,8 +121531,8 @@
   {
     "name": "iNatAg/umbilicus_rupestris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121552,8 +121552,8 @@
   {
     "name": "iNatAg/uncaria_gambir",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121573,8 +121573,8 @@
   {
     "name": "iNatAg/urelytrum_agropyroides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121594,8 +121594,8 @@
   {
     "name": "iNatAg/urena_lobata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121615,8 +121615,8 @@
   {
     "name": "iNatAg/urochloa_mosambicensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121636,8 +121636,8 @@
   {
     "name": "iNatAg/urochloa_panicoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121657,8 +121657,8 @@
   {
     "name": "iNatAg/urtica_chamaedryoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121678,8 +121678,8 @@
   {
     "name": "iNatAg/urtica_dioica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121699,8 +121699,8 @@
   {
     "name": "iNatAg/urtica_urens",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121720,8 +121720,8 @@
   {
     "name": "iNatAg/utricularia_floridana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121741,8 +121741,8 @@
   {
     "name": "iNatAg/utricularia_foliosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121762,8 +121762,8 @@
   {
     "name": "iNatAg/utricularia_gibba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121783,8 +121783,8 @@
   {
     "name": "iNatAg/utricularia_purpurea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121804,8 +121804,8 @@
   {
     "name": "iNatAg/utricularia_radiata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121825,8 +121825,8 @@
   {
     "name": "iNatAg/utricularia_vulgaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121846,8 +121846,8 @@
   {
     "name": "iNatAg/uvaria_littoralis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121867,8 +121867,8 @@
   {
     "name": "iNatAg/uvularia_sessilifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121888,8 +121888,8 @@
   {
     "name": "iNatAg/vaccinium_angustifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121909,8 +121909,8 @@
   {
     "name": "iNatAg/vaccinium_corymbosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121930,8 +121930,8 @@
   {
     "name": "iNatAg/vaccinium_macrocarpon",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121951,8 +121951,8 @@
   {
     "name": "iNatAg/vaccinium_myrtillus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121972,8 +121972,8 @@
   {
     "name": "iNatAg/vaccinium_uliginosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -121993,8 +121993,8 @@
   {
     "name": "iNatAg/vaccinium_vitis-idaea",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122014,8 +122014,8 @@
   {
     "name": "iNatAg/valeriana_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122035,8 +122035,8 @@
   {
     "name": "iNatAg/valerianella_eriocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122056,8 +122056,8 @@
   {
     "name": "iNatAg/vallisneria_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122077,8 +122077,8 @@
   {
     "name": "iNatAg/vangueria_infausta",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122098,8 +122098,8 @@
   {
     "name": "iNatAg/vangueria_madagascariensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122119,8 +122119,8 @@
   {
     "name": "iNatAg/vanilla_planifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122140,8 +122140,8 @@
   {
     "name": "iNatAg/vateria_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122161,8 +122161,8 @@
   {
     "name": "iNatAg/ventilago_viminalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122182,8 +122182,8 @@
   {
     "name": "iNatAg/veratrum_album",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122203,8 +122203,8 @@
   {
     "name": "iNatAg/veratrum_californicum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122224,8 +122224,8 @@
   {
     "name": "iNatAg/verbascum_blattaria",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122245,8 +122245,8 @@
   {
     "name": "iNatAg/verbascum_lychnitis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122266,8 +122266,8 @@
   {
     "name": "iNatAg/verbascum_phlomoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122287,8 +122287,8 @@
   {
     "name": "iNatAg/verbascum_thapsus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122308,8 +122308,8 @@
   {
     "name": "iNatAg/verbascum_thaspus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122329,8 +122329,8 @@
   {
     "name": "iNatAg/verbena_bonariensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122350,8 +122350,8 @@
   {
     "name": "iNatAg/verbena_brasiliensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122371,8 +122371,8 @@
   {
     "name": "iNatAg/verbena_hastata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122392,8 +122392,8 @@
   {
     "name": "iNatAg/verbena_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122413,8 +122413,8 @@
   {
     "name": "iNatAg/verbena_urticifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122434,8 +122434,8 @@
   {
     "name": "iNatAg/vernonia_altissima",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122455,8 +122455,8 @@
   {
     "name": "iNatAg/vernonia_amygdalina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122476,8 +122476,8 @@
   {
     "name": "iNatAg/vernonia_baldwinii",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122497,8 +122497,8 @@
   {
     "name": "iNatAg/vernonia_chamaedrys",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122518,8 +122518,8 @@
   {
     "name": "iNatAg/vernonia_fasciculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122539,8 +122539,8 @@
   {
     "name": "iNatAg/veronica_agrestis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122560,8 +122560,8 @@
   {
     "name": "iNatAg/veronica_anagallis-aquatica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122581,8 +122581,8 @@
   {
     "name": "iNatAg/veronica_arvensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122602,8 +122602,8 @@
   {
     "name": "iNatAg/veronica_biloba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122623,8 +122623,8 @@
   {
     "name": "iNatAg/veronica_chamaedrys",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122644,8 +122644,8 @@
   {
     "name": "iNatAg/veronica_filiformis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122665,8 +122665,8 @@
   {
     "name": "iNatAg/veronica_hederaefolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122686,8 +122686,8 @@
   {
     "name": "iNatAg/veronica_hederifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122707,8 +122707,8 @@
   {
     "name": "iNatAg/veronica_longifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122728,8 +122728,8 @@
   {
     "name": "iNatAg/veronica_officinalis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122749,8 +122749,8 @@
   {
     "name": "iNatAg/veronica_peregrina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122770,8 +122770,8 @@
   {
     "name": "iNatAg/veronica_polita",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122791,8 +122791,8 @@
   {
     "name": "iNatAg/veronica_serpyllifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122812,8 +122812,8 @@
   {
     "name": "iNatAg/vetiveria_zizanioides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122833,8 +122833,8 @@
   {
     "name": "iNatAg/viburnum_cassinoides",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122854,8 +122854,8 @@
   {
     "name": "iNatAg/viburnum_lentago",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122875,8 +122875,8 @@
   {
     "name": "iNatAg/viburnum_prunifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122896,8 +122896,8 @@
   {
     "name": "iNatAg/viccia_cracca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122917,8 +122917,8 @@
   {
     "name": "iNatAg/vicia_augustifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122938,8 +122938,8 @@
   {
     "name": "iNatAg/vicia_benghalensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122959,8 +122959,8 @@
   {
     "name": "iNatAg/vicia_cracca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -122980,8 +122980,8 @@
   {
     "name": "iNatAg/vicia_ervilia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123001,8 +123001,8 @@
   {
     "name": "iNatAg/vicia_faba",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123022,8 +123022,8 @@
   {
     "name": "iNatAg/vicia_monantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123043,8 +123043,8 @@
   {
     "name": "iNatAg/vicia_narbonensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123064,8 +123064,8 @@
   {
     "name": "iNatAg/vicia_pannonica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123085,8 +123085,8 @@
   {
     "name": "iNatAg/vicia_sativa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123106,8 +123106,8 @@
   {
     "name": "iNatAg/vicia_sepium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123127,8 +123127,8 @@
   {
     "name": "iNatAg/vigna_adenantha",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123148,8 +123148,8 @@
   {
     "name": "iNatAg/vigna_angularis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123169,8 +123169,8 @@
   {
     "name": "iNatAg/vigna_hosei",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123190,8 +123190,8 @@
   {
     "name": "iNatAg/vigna_lanceolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123211,8 +123211,8 @@
   {
     "name": "iNatAg/vigna_longifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123232,8 +123232,8 @@
   {
     "name": "iNatAg/vigna_luteola",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123253,8 +123253,8 @@
   {
     "name": "iNatAg/vigna_parkeri",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123274,8 +123274,8 @@
   {
     "name": "iNatAg/vigna_radiata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123295,8 +123295,8 @@
   {
     "name": "iNatAg/vigna_trilobata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123316,8 +123316,8 @@
   {
     "name": "iNatAg/vigna_umbellata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123337,8 +123337,8 @@
   {
     "name": "iNatAg/vigna_unguiculata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123358,8 +123358,8 @@
   {
     "name": "iNatAg/vigna_vexillata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123379,8 +123379,8 @@
   {
     "name": "iNatAg/vinca_major",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123400,8 +123400,8 @@
   {
     "name": "iNatAg/vinca_minor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123421,8 +123421,8 @@
   {
     "name": "iNatAg/viola_lanceolata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123442,8 +123442,8 @@
   {
     "name": "iNatAg/viola_odorata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123463,8 +123463,8 @@
   {
     "name": "iNatAg/viola_tricolor",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123484,8 +123484,8 @@
   {
     "name": "iNatAg/viscum_album",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123505,8 +123505,8 @@
   {
     "name": "iNatAg/vitellaria_paradoxa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123526,8 +123526,8 @@
   {
     "name": "iNatAg/vitex_agnus-castus",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123547,8 +123547,8 @@
   {
     "name": "iNatAg/vitex_doniana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123568,8 +123568,8 @@
   {
     "name": "iNatAg/vitex_negundo",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123589,8 +123589,8 @@
   {
     "name": "iNatAg/vitis_labrusca",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123610,8 +123610,8 @@
   {
     "name": "iNatAg/vitis_rotundifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123631,8 +123631,8 @@
   {
     "name": "iNatAg/vitis_vinifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123652,8 +123652,8 @@
   {
     "name": "iNatAg/vitis_vulpina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123673,8 +123673,8 @@
   {
     "name": "iNatAg/waltheria_indica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123694,8 +123694,8 @@
   {
     "name": "iNatAg/warburgia_salutaris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123715,8 +123715,8 @@
   {
     "name": "iNatAg/warburgia_ugandensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123736,8 +123736,8 @@
   {
     "name": "iNatAg/withania_somnifera",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123757,8 +123757,8 @@
   {
     "name": "iNatAg/wrightia_tomentosa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123778,8 +123778,8 @@
   {
     "name": "iNatAg/xanthium_spinosum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123799,8 +123799,8 @@
   {
     "name": "iNatAg/xanthium_strumarium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123820,8 +123820,8 @@
   {
     "name": "iNatAg/xanthosoma_sagittifolium",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123841,8 +123841,8 @@
   {
     "name": "iNatAg/ximenia_americana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123862,8 +123862,8 @@
   {
     "name": "iNatAg/xylia_xylocarpa",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123883,8 +123883,8 @@
   {
     "name": "iNatAg/xylocarpus_granatum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123904,8 +123904,8 @@
   {
     "name": "iNatAg/xylocarpus_mekongensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123925,8 +123925,8 @@
   {
     "name": "iNatAg/xylocarpus_moluccensis",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123946,8 +123946,8 @@
   {
     "name": "iNatAg/xylorhiza_glabriuscula",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123967,8 +123967,8 @@
   {
     "name": "iNatAg/yucca_elephantipes",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -123988,8 +123988,8 @@
   {
     "name": "iNatAg/zannichellia_palustris",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -124009,8 +124009,8 @@
   {
     "name": "iNatAg/zanthoxylum_americanum",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -124030,8 +124030,8 @@
   {
     "name": "iNatAg/zea_mays",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -124051,8 +124051,8 @@
   {
     "name": "iNatAg/zingiber_officinale",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -124072,8 +124072,8 @@
   {
     "name": "iNatAg/zizania_aquatica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -124093,8 +124093,8 @@
   {
     "name": "iNatAg/zizania_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -124114,8 +124114,8 @@
   {
     "name": "iNatAg/ziziphus_abyssinica",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -124135,8 +124135,8 @@
   {
     "name": "iNatAg/ziziphus_mauritiana",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -124156,8 +124156,8 @@
   {
     "name": "iNatAg/ziziphus_mucronata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -124177,8 +124177,8 @@
   {
     "name": "iNatAg/zornia_diphylla",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -124198,8 +124198,8 @@
   {
     "name": "iNatAg/zornia_glochidiata",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -124219,8 +124219,8 @@
   {
     "name": "iNatAg/zornia_latifolia",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -124240,8 +124240,8 @@
   {
     "name": "iNatAg/zostera_marina",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -124261,8 +124261,8 @@
   {
     "name": "iNatAg/zoysia_matrella",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -124282,8 +124282,8 @@
   {
     "name": "iNatAg/zygophyllum_fabago",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "image_classification",
-    "location": "worldwide, worldwide",
+    "agricultural_task": "crop_classification",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -124303,8 +124303,8 @@
   {
     "name": "leaf_counting_denmark",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "leaf_counting",
-    "location": "Denmark, Europe",
+    "agricultural_task": "crop_classification",
+    "location": "Denmark",
     "sensor_modality": null,
     "real_or_synthetic": null,
     "platform": "mixed",
@@ -124332,8 +124332,8 @@
   {
     "name": "papaya_leaf_disease_classification",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "papaya_leaf_disease_classification",
-    "location": "Bangladesh, Asia",
+    "agricultural_task": "disease_classification",
+    "location": "Bangladesh",
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -124355,16 +124355,16 @@
   {
     "name": "susnato_plant_disease_detection_processed",
     "machine_learning_task": "object_detection",
-    "agricultural_task": "None",
-    "location": "None",
+    "agricultural_task": "disease_detection",
+    "location": null,
     "sensor_modality": "rgb",
     "real_or_synthetic": "real",
-    "platform": "None",
+    "platform": null,
     "input_data_format": "jpg",
     "annotation_format": "coco_json",
     "num_images": 2324,
     "documentation": "https://huggingface.co/datasets/susnato/plant_disease_detection_processed",
-    "classes": "None",
+    "classes": "",
     "stats_mean": null,
     "stats_std": null,
     "examples_image_url": null,
diff --git a/static/data/embeddings/meta.json b/static/data/embeddings/meta.json
index 8e3c660..3f892d9 100644
--- a/static/data/embeddings/meta.json
+++ b/static/data/embeddings/meta.json
@@ -1 +1 @@
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bescens","iNatAg-mini/averrhoa_bilimbi","iNatAg-mini/averrhoa_carambola","iNatAg-mini/avicennia_germinans","iNatAg-mini/avicennia_marina","iNatAg-mini/avicennia_officinalis","iNatAg-mini/axonopus_affinis","iNatAg-mini/axonopus_compressus","iNatAg-mini/axonopus_fissifolius","iNatAg-mini/axyris_amaranthoides","iNatAg-mini/azadirachta_indica","iNatAg-mini/azanza_garckeana","iNatAg-mini/azolla_filiculoides","iNatAg-mini/azolla_pinnata","iNatAg-mini/baccaurea_motleyana","iNatAg-mini/baccaurea_ramiflora","iNatAg-mini/baccharis_glutinosa","iNatAg-mini/baccharis_pilularis","iNatAg-mini/bactris_gasipaes","iNatAg-mini/baikiaea_plurijuga","iNatAg-mini/balanites_aegyptiaca","iNatAg-mini/bambusa_arundinacea","iNatAg-mini/bambusa_balcooa","iNatAg-mini/bambusa_blumeana","iNatAg-mini/bambusa_tulda","iNatAg-mini/bambusa_vulgaris","iNatAg-mini/banksia_integrifolia","iNatAg-mini/banksia_occidentalis","iNatAg-mini/baphia_nitida","iNatAg-mini/barringtonia_racemosa","iNatAg-mini/basella_alba","iNatAg-mini/bauhinia_aculeata","iNatAg-mini/bauhinia_petersiana","iNatAg-mini/bauhinia_racemosa","iNatAg-mini/bauhinia_rufescens","iNatAg-mini/bauhinia_thonningii","iNatAg-mini/bauhinia_tomentosa","iNatAg-mini/bauhinia_variegata","iNatAg-mini/beckmannia_eruciformis","iNatAg-mini/beckmannia_syzigachne","iNatAg-mini/bellis_perennis","iNatAg-mini/benincasa_hispida","iNatAg-mini/berberis_aquifolium","iNatAg-mini/berberis_thunbergii","iNatAg-mini/berberis_vulgaris","iNatAg-mini/berchemia_discolor","iNatAg-mini/berrya_cordifolia","iNatAg-mini/bersama_lucens","iNatAg-mini/bertholletia_excelsa","iNatAg-mini/beta_vulgaris","iNatAg-mini/betula_nigra","iNatAg-mini/betula_pendula","iNatAg-mini/betula_pubescens","iNatAg-mini/bidens_bipinnata","iNatAg-mini/bidens_cernua","iNatAg-mini/bidens_frondosa","iNatAg-mini/bidens_pilosa","iNatAg-mini/bidens_tripartita","iNatAg-mini/bignonia_capreolata","iNatAg-mini/biserrula_pelecinus","iNatAg-mini/bixa_orellana","iNatAg-mini/blighia_sapida","iNatAg-mini/blumea_balsamifera","iNatAg-mini/bocconia_frutescens","iNatAg-mini/boehmeria_nivea","iNatAg-mini/boerhavia_coccinea","iNatAg-mini/boerhavia_diffusa","iNatAg-mini/boerhavia_erecta","iNatAg-mini/boesenbergia_rotunda","iNatAg-mini/bolusanthus_speciosus","iNatAg-mini/bombacopsis_quinata","iNatAg-mini/bombax_ceiba","iNatAg-mini/bombax_insigne","iNatAg-mini/borago_officinalis","iNatAg-mini/borassus_aethiopum","iNatAg-mini/borassus_flabellifer","iNatAg-mini/borojoa_patinoi","iNatAg-mini/boronia_glabra","iNatAg-mini/boscia_angustifolia","iNatAg-mini/boswellia_serrata","iNatAg-mini/bothriochloa_bladhii","iNatAg-mini/bothriochloa_insculpta","iNatAg-mini/bothriochloa_ischaemum","iNatAg-mini/bothriochloa_pertusa","iNatAg-mini/bougainvillea_glabra","iNatAg-mini/bouteloua_curtipendula","iNatAg-mini/bouteloua_gracilis","iNatAg-mini/brachiaria_brizantha","iNatAg-mini/brachiaria_decumbens","iNatAg-mini/brachiaria_deflexa","iNatAg-mini/brachiaria_distachya","iNatAg-mini/brachiaria_humidicola","iNatAg-mini/brachiaria_mutica","iNatAg-mini/brachiaria_ramosa","iNatAg-mini/brachiaria_serrata","iNatAg-mini/brachychiton_acerifolius","iNatAg-mini/brachychiton_populneus","iNatAg-mini/brachylaena_huillensis","iNatAg-mini/brachystegia_spiciformis","iNatAg-mini/brassica_campestris","iNatAg-mini/brassica_chinensis","iNatAg-mini/brassica_incana","iNatAg-mini/brassica_juncea","iNatAg-mini/brassica_napus","iNatAg-mini/brassica_nigra","iNatAg-mini/brassica_rapa","iNatAg-mini/brassica_tournefortii","iNatAg-mini/bridelia_micrantha","iNatAg-mini/briza_maxima","iNatAg-mini/briza_media","iNatAg-mini/briza_minor","iNatAg-mini/bromus_arvensis","iNatAg-mini/bromus_carinatus","iNatAg-mini/bromus_catharticus","iNatAg-mini/bromus_diandrus","iNatAg-mini/bromus_erectus","iNatAg-mini/bromus_hordeaceus","iNatAg-mini/bromus_inermis","iNatAg-mini/bromus_madritensis","iNatAg-mini/bromus_marginatus","iNatAg-mini/bromus_racemosus","iNatAg-mini/bromus_rubens","iNatAg-mini/bromus_secalinus","iNatAg-mini/bromus_sterilis","iNatAg-mini/bromus_tectorum","iNatAg-mini/bromus_unioloides","iNatAg-mini/bromus_willdenowii","iNatAg-mini/brosimum_alicastrum","iNatAg-mini/broussonetia_papyrifera","iNatAg-mini/bruguiera_gymnorrhiza","iNatAg-mini/bryonia_alba","iNatAg-mini/bryonia_cretica","iNatAg-mini/buchloe_dactyloides","iNatAg-mini/buckinghamia_celsissima","iNatAg-mini/bunias_erucago","iNatAg-mini/bunias_orientalis","iNatAg-mini/burkea_africana","iNatAg-mini/bursera_simaruba","iNatAg-mini/butea_monosperma","iNatAg-mini/butomus_umbellatus","iNatAg-mini/buxus_sempervirens","iNatAg-mini/cacalia_atriplicifolia","iNatAg-mini/caesalpinia_coriaria","iNatAg-mini/caesalpinia_sappan","iNatAg-mini/cajanus_cajan","iNatAg-mini/calamagrostis_epigeios","iNatAg-mini/calathea_allouia","iNatAg-mini/calendula_arvensis","iNatAg-mini/calendula_officinalis","iNatAg-mini/calliandra_calothyrsus","iNatAg-mini/calliandra_tweedii","iNatAg-mini/callisia_angustifolia","iNatAg-mini/callitriche_palustris","iNatAg-mini/callitriche_stagnalis","iNatAg-mini/callitriche_verna","iNatAg-mini/callitris_columellaris","iNatAg-mini/callitris_endlicheri","iNatAg-mini/callitris_macleayana","iNatAg-mini/calluna_vulgaris","iNatAg-mini/calodendrum_capense","iNatAg-mini/calophyllum_apetalum","iNatAg-mini/calophyllum_brasiliense","iNatAg-mini/calophyllum_inophyllum","iNatAg-mini/calopogonium_caeruleum","iNatAg-mini/calopogonium_mucunoides","iNatAg-mini/calotropis_procera","iNatAg-mini/caltha_palustris","iNatAg-mini/calystegia_hederacea","iNatAg-mini/calystegia_occidentalis","iNatAg-mini/calystegia_pubescens","iNatAg-mini/camelina_sativa","iNatAg-mini/camellia_sinensis","iNatAg-mini/campanula_americana","iNatAg-mini/campanula_rapunculus","iNatAg-mini/campanula_rotundifolia","iNatAg-mini/cananga_odorata","iNatAg-mini/canavalia_brasiliensis","iNatAg-mini/canavalia_ensiformis","iNatAg-mini/canavalia_gladiata","iNatAg-mini/canna_indica","iNatAg-mini/canthium_spinosum","iNatAg-mini/capparis_decidua","iNatAg-mini/capparis_spinosa","iNatAg-mini/capparis_tomentosa","iNatAg-mini/capsella_bursa-pastoris","iNatAg-mini/capsicum_annuum","iNatAg-mini/capsicum_chinense","iNatAg-mini/capsicum_frutescens","iNatAg-mini/capsicum_pubescens","iNatAg-mini/caragana_arborescens","iNatAg-mini/caragana_microphylla","iNatAg-mini/carapa_guianensis","iNatAg-mini/cardamine_flexuosa","iNatAg-mini/cardamine_hirsuta","iNatAg-mini/cardamine_impatiens","iNatAg-mini/cardamine_oligosperma","iNatAg-mini/cardamine_parviflora","iNatAg-mini/cardamine_pratensis","iNatAg-mini/cardiospermum_halicacabum","iNatAg-mini/carduus_acanthoides","iNatAg-mini/carduus_crispus","iNatAg-mini/carduus_lanceolatus","iNatAg-mini/carduus_pycnocephalus","iNatAg-mini/carex_nebrascensis","iNatAg-mini/carex_pallescens","iNatAg-mini/carica_cauliflora","iNatAg-mini/carica_papaya","iNatAg-mini/carica_pubescens","iNatAg-mini/cariniana_pyriformis","iNatAg-mini/carissa_carandas","iNatAg-mini/carissa_edulis","iNatAg-mini/carissa_macrocarpa","iNatAg-mini/carlina_acaulis","iNatAg-mini/carludovica_palmata","iNatAg-mini/caroxylon_aphyllum","iNatAg-mini/carpinus_betulus","iNatAg-mini/carthamus_creticus","iNatAg-mini/carthamus_lanatus","iNatAg-mini/carthamus_tinctorius","iNatAg-mini/carum_carvi","iNatAg-mini/carya_illinoensis","iNatAg-mini/caryodendron_orinocense","iNatAg-mini/caryota_urens","iNatAg-mini/casimiroa_edulis","iNatAg-mini/cassia_articulata","iNatAg-mini/cassia_brewsteri","iNatAg-mini/cassia_fistula","iNatAg-mini/cassia_marilandica","iNatAg-mini/cassia_nictitans","iNatAg-mini/cassia_reticulata","iNatAg-mini/cassia_senna","iNatAg-mini/cassia_siamea","iNatAg-mini/cassia_sieberiana","iNatAg-mini/cassia_tomentosa","iNatAg-mini/cassia_tora","iNatAg-mini/castanea_crenata","iNatAg-mini/castanea_dentata","iNatAg-mini/castanea_mollissima","iNatAg-mini/castanea_pumila","iNatAg-mini/castanea_sativa","iNatAg-mini/castanospermum_australe","iNatAg-mini/castilla_elastica","iNatAg-mini/castilleja_angustifolia","iNatAg-mini/castilleja_occidentalis","iNatAg-mini/casuarina_cristata","iNatAg-mini/casuarina_cunninghamiana","iNatAg-mini/casuarina_equisetifolia","iNatAg-mini/casuarina_glauca","iNatAg-mini/casuarina_junghuhniana","iNatAg-mini/casuarina_obesa","iNatAg-mini/catalpa_bignonioides","iNatAg-mini/catha_edulis","iNatAg-mini/catharanthus_roseus","iNatAg-mini/ceanothus_americanus","iNatAg-mini/ceanothus_prostratus","iNatAg-mini/cedrela_odorata","iNatAg-mini/cedrus_deodara","iNatAg-mini/ceiba_pentandra","iNatAg-mini/celastrus_orbiculatus","iNatAg-mini/celastrus_scandens","iNatAg-mini/celosia_argentea","iNatAg-mini/celtis_australis","iNatAg-mini/cenchrus_biflorus","iNatAg-mini/cenchrus_ciliaris","iNatAg-mini/cenchrus_echinatus","iNatAg-mini/cenchrus_setigerus","iNatAg-mini/cenchrus_spinifex","iNatAg-mini/cenchrus_tribuloides","iNatAg-mini/centaurea_biebersteinii","iNatAg-mini/centaurea_calcitrapa","iNatAg-mini/centaurea_cyanus","iNatAg-mini/centaurea_diluta","iNatAg-mini/centaurea_jacea","iNatAg-mini/centaurea_melitensis","iNatAg-mini/centaurea_nigra","iNatAg-mini/centaurea_nigrescens","iNatAg-mini/centaurea_solstitalis","iNatAg-mini/centaurea_solstitialis","iNatAg-mini/centaurea_stoebe","iNatAg-mini/centaurea_virgata","iNatAg-mini/centella_asiatica","iNatAg-mini/centropodia_glauca","iNatAg-mini/centrosema_brasilianum","iNatAg-mini/centrosema_macrocarpum","iNatAg-mini/centrosema_pascuorum","iNatAg-mini/centrosema_plumieri","iNatAg-mini/centrosema_pubescens","iNatAg-mini/centrosema_virginianum","iNatAg-mini/cephalanthus_occidentalis","iNatAg-mini/cerastium_arvense","iNatAg-mini/cerastium_nutans","iNatAg-mini/cerastium_vulgatum","iNatAg-mini/ceratonia_siliqua","iNatAg-mini/ceratopetalum_apetalum","iNatAg-mini/ceratophyllum_demersum","iNatAg-mini/ceratophyllum_echinatum","iNatAg-mini/ceriops_tagal","iNatAg-mini/cestrum_diurnum","iNatAg-mini/ceterach_officinarum","iNatAg-mini/chaerophyllum_tainturieri","iNatAg-mini/chamaebatia_foliolosa","iNatAg-mini/chamaecrista_nictitans","iNatAg-mini/chamaecrista_rotundifolia","iNatAg-mini/chamaedorea_tepejilote","iNatAg-mini/chamae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riganum_onites","iNatAg-mini/origanum_vulgare","iNatAg-mini/ornithogalum_nutans","iNatAg-mini/ornithogalum_umbellatum","iNatAg-mini/ornithopus_compressus","iNatAg-mini/ornithopus_sativus","iNatAg-mini/orobanche_flava","iNatAg-mini/orobanche_ludoviciana","iNatAg-mini/orobanche_minor","iNatAg-mini/orobanche_ramosa","iNatAg-mini/orontium_aquaticum","iNatAg-mini/orthosiphon_aristatus","iNatAg-mini/oryza_sativa","iNatAg-mini/oryzopsis_holciformis","iNatAg-mini/oryzopsis_miliacea","iNatAg-mini/osmorhiza_berteroi","iNatAg-mini/osmunda_regalis","iNatAg-mini/ottochloa_nodosa","iNatAg-mini/oxalis_acetosella","iNatAg-mini/oxalis_corniculata","iNatAg-mini/oxalis_pes-caprae","iNatAg-mini/oxalis_pescaprae","iNatAg-mini/oxalis_stricta","iNatAg-mini/oxalis_tuberosa","iNatAg-mini/oxytropis_lambertii","iNatAg-mini/pachyrhizus_erosus","iNatAg-mini/paederia_cruddasiana","iNatAg-mini/paederia_foetida","iNatAg-mini/paeonia_officinalis","iNatAg-mini/panax_ginseng","iNatAg-mini/panax_quinquefolius","iNatAg-mini/pangium_edule","iNatAg-mini/panicum_antidotale","iNatAg-mini/panicum_capillare","iNatAg-mini/panicum_coloratum","iNatAg-mini/panicum_ecklonii","iNatAg-mini/panicum_gattingeri","iNatAg-mini/panicum_maximum","iNatAg-mini/panicum_miliaceum","iNatAg-mini/panicum_natalense","iNatAg-mini/panicum_obtusum","iNatAg-mini/panicum_pilosum","iNatAg-mini/panicum_racemosum","iNatAg-mini/panicum_repens","iNatAg-mini/panicum_sphaerocarpon","iNatAg-mini/panicum_trichocladum","iNatAg-mini/panicum_turgidum","iNatAg-mini/panicum_virgatum","iNatAg-mini/papaver_argemone","iNatAg-mini/papaver_bracteatum","iNatAg-mini/papaver_dubium","iNatAg-mini/papaver_rhoeas","iNatAg-mini/papaver_somniferum","iNatAg-mini/parietaria_floridana","iNatAg-mini/parietaria_officinalis","iNatAg-mini/parinari_curatellifolia","iNatAg-mini/parkia_biglobosa","iNatAg-mini/parkia_speciosa","iNatAg-mini/parkinsonia_aculeata","iNatAg-mini/parnassia_palustris","iNatAg-mini/parsonsia_latifolia","iNatAg-mini/parthenium_argentatum","iNatAg-mini/parthenium_hysterophorus","iNatAg-mini/paspalum_conjugatum","iNatAg-mini/paspalum_dilatatum","iNatAg-mini/paspalum_distichum","iNatAg-mini/paspalum_nicorae","iNatAg-mini/paspalum_notatum","iNatAg-mini/paspalum_plicatulum","iNatAg-mini/paspalum_scrobiculatum","iNatAg-mini/paspalum_separatum","iNatAg-mini/paspalum_urvillei","iNatAg-mini/paspalum_vaginatum","iNatAg-mini/passiflora_bicornis","iNatAg-mini/passiflora_edulis","iNatAg-mini/passiflora_foetida","iNatAg-mini/passiflora_incarnata","iNatAg-mini/passiflora_laurifolia","iNatAg-mini/passiflora_ligularis","iNatAg-mini/passiflora_lutea","iNatAg-mini/passiflora_mollissima","iNatAg-mini/passiflora_quadrangularis","iNatAg-mini/passiflora_suberosa","iNatAg-mini/pastinaca_sativa","iNatAg-mini/paullinia_cupana","iNatAg-mini/paulownia_tomentosa","iNatAg-mini/peganum_harmala","iNatAg-mini/pelargonium_graveolens","iNatAg-mini/peltandra_sagittifolia","iNatAg-mini/peltandra_virginica","iNatAg-mini/peltophorum_africanum","iNatAg-mini/peltophorum_pterocarpum","iNatAg-mini/pennisetum_clandestinum","iNatAg-mini/pennisetum_glaucum","iNatAg-mini/pennisetum_macrourum","iNatAg-mini/pennisetum_pedicellatum","iNatAg-mini/pennisetum_polystachyon","iNatAg-mini/pennisetum_purpureum","iNatAg-mini/pennisetum_setaceum","iNatAg-mini/pennisetum_villosum","iNatAg-mini/perilla_frutescens","iNatAg-mini/persea_americana","iNatAg-mini/persicaria_maculosa","iNatAg-mini/persoonia_falcata","iNatAg-mini/petalostigma_pubescens","iNatAg-mini/petasites_albus","iNatAg-mini/petasites_hybridus","iNatAg-mini/petroselinum_crispum","iNatAg-mini/petunia_parviflora","iNatAg-mini/peucedanum_ostruthium","iNatAg-mini/phalaris_aquatica","iNatAg-mini/phalaris_arundinacea","iNatAg-mini/phalaris_arundinaceae","iNatAg-mini/phalaris_brachystachys","iNatAg-mini/phalaris_canariensis","iNatAg-mini/phalaris_caroliniana","iNatAg-mini/phalaris_coerulescens","iNatAg-mini/phalaris_paradoxa","iNatAg-mini/phaseolus_acutifolius","iNatAg-mini/phaseolus_coccineus","iNatAg-mini/phaseolus_lunatus","iNatAg-mini/phaseolus_vulgaris","iNatAg-mini/phleum_alpinum","iNatAg-mini/phleum_pratense","iNatAg-mini/phoenix_dactylifera","iNatAg-mini/phoenix_reclinata","iNatAg-mini/phoenix_sylvestris","iNatAg-mini/phormium_tenax","iNatAg-mini/phragmites_australis","iNatAg-mini/phragmites_communis","iNatAg-mini/phragmites_karka","iNatAg-mini/phyllanthus_niruri","iNatAg-mini/phyllanthus_tenellus","iNatAg-mini/phyllanthus_urinaria","iNatAg-mini/phyllocladus_aspleniifolius","iNatAg-mini/physalis_alkekengi","iNatAg-mini/physalis_angulata","iNatAg-mini/physalis_heterophylla","iNatAg-mini/physalis_lancifolia","iNatAg-mini/physalis_peruviana","iNatAg-mini/physalis_philadelphica","iNatAg-mini/physalis_pubescens","iNatAg-mini/physalis_virginiana","iNatAg-mini/physalis_viscosa","iNatAg-mini/phytolacca_acinosa","iNatAg-mini/phytolacca_americana","iNatAg-mini/phytolacca_dioica","iNatAg-mini/picea_abies","iNatAg-mini/picea_omorica","iNatAg-mini/picea_omorika","iNatAg-mini/picris_echioides","iNa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umatococcus_daniellii","iNatAg-mini/themeda_australis","iNatAg-mini/themeda_quadrivalvis","iNatAg-mini/themeda_triandra","iNatAg-mini/theobroma_bicolor","iNatAg-mini/theobroma_cacao","iNatAg-mini/theobroma_grandiflorum","iNatAg-mini/thermopsis_montana","iNatAg-mini/thermopsis_rhombifolia","iNatAg-mini/thespesia_populnea","iNatAg-mini/thlaspi_arvense","iNatAg-mini/thlaspi_perfoliatum","iNatAg-mini/thuja_occidentalis","iNatAg-mini/thymus_serphyllum","iNatAg-mini/thymus_serpyllum","iNatAg-mini/thymus_vulgaris","iNatAg-mini/thyrsostachys_siamensis","iNatAg-mini/thysanolaena_latifolia","iNatAg-mini/tilia_cordata","iNatAg-mini/tilia_platyphyllos","iNatAg-mini/tipuana_tipu","iNatAg-mini/tithonia_diversifolia","iNatAg-mini/toona_ciliata","iNatAg-mini/torenia_glabra","iNatAg-mini/toxicodendron_pubescens","iNatAg-mini/trachypogon_spicatus","iNatAg-mini/tradescantia_bracteata","iNatAg-mini/tradescantia_fluminensis","iNatAg-mini/tradescantia_ohiensis","iNatAg-mini/tradescantia_virginiana","iNatAg-mini/tragia_betonicifolia","iNatAg-mini/tragopogon_lamottei","iNatAg-mini/tragopogon_porrifolius","iNatAg-mini/tragopogon_pratensis","iNatAg-mini/tragus_koelerioides","iNatAg-mini/trapa_natans","iNatAg-mini/trema_orientale","iNatAg-mini/trianthema_portulacastrum","iNatAg-mini/tribulus_cistoides","iNatAg-mini/tribulus_terrestris","iNatAg-mini/trichanthera_gigantea","iNatAg-mini/trichoneura_grandiglumis","iNatAg-mini/trichosanthes_cucumerina","iNatAg-mini/trichostema_lanceolatum","iNatAg-mini/tridax_procumbens","iNatAg-mini/trifolium_africanum","iNatAg-mini/trifolium_alexandrinum","iNatAg-mini/trifolium_ambiguum","iNatAg-mini/trifolium_angustifolium","iNatAg-mini/trifolium_arvense","iNatAg-mini/trifolium_burchellianum","iNatAg-mini/trifolium_campestre","iNatAg-mini/trifolium_carolinianum","iNatAg-mini/trifolium_cherleri","iNatAg-mini/trifolium_dubium","iNatAg-mini/trifolium_fragiferum","iNatAg-mini/trifolium_glanduliferum","iNatAg-mini/trifolium_glomeratum","iNatAg-mini/trifolium_hirtum","iNatAg-mini/trifolium_hybridum","iNatAg-mini/trifolium_incarnatum","iNatAg-mini/trifolium_medium","iNatAg-mini/trifolium_michelianum","iNatAg-mini/trifolium_nigrescens","iNatAg-mini/trifolium_patens","iNatAg-mini/trifolium_pilulare","iNatAg-mini/trifolium_polymorphum","iNatAg-mini/trifolium_pratense","iNatAg-mini/trifolium_reflexum","iNatAg-mini/trifolium_repens","iNatAg-mini/trifolium_resupinatum","iNatAg-mini/trifolium_subterraneum","iNatAg-mini/trifolium_tomentosum","iNatAg-mini/trifolium_variegatum","iNatAg-mini/trifolium_vesiculosum","iNatAg-mini/trifolium_wormskioldii","iNatAg-mini/triglochin_maritima","iNatAg-mini/triglochin_maritimum","iNatAg-mini/triglochin_palustre","iNatAg-mini/trigonella_foenum-graecum","iNatAg-mini/tripsacum_dactyloides","iNatAg-mini/trisetum_flavescens","iNatAg-mini/tristachya_leucothrix","iNatAg-mini/triticum_aestivum","iNatAg-mini/triticum_dicoccoides","iNatAg-mini/triticum_durum","iNatAg-mini/triticum_spelta","iNatAg-mini/triumfetta_rhomboidea","iNatAg-mini/triumfetta_semitriloba","iNatAg-mini/trollius_europaeus","iNatAg-mini/tropaeolum_majus","iNatAg-mini/tropaeolum_tuberosum","iNatAg-mini/tropidocarpum_gracile","iNatAg-mini/turritis_glabra","iNatAg-mini/tussilago_farfara","iNatAg-mini/tylosema_esculentum","iNatAg-mini/typha_angustifolia","iNatAg-mini/typha_domingensis","iNatAg-mini/typha_latifolia","iNatAg-mini/uapaca_kirkiana","iNatAg-mini/ulex_europaeus","iNatAg-mini/ullucus_tuberosus","iNatAg-mini/ulmus_procera","iNatAg-mini/umbilicus_rupestris","iNatAg-mini/uncaria_gambir","iNatAg-mini/urelytrum_agropyroides","iNatAg-mini/urena_lobata","iNatAg-mini/urochloa_mosambicensis","iNatAg-mini/urochloa_panicoides","iNatAg-mini/urtica_chamaedryoides","iNatAg-mini/urtica_dioica","iNatAg-mini/urtica_urens","iNatAg-mini/utricularia_floridana","iNatAg-mini/utricularia_foliosa","iNatAg-mini/utricularia_gibba","iNatAg-mini/utricularia_purpurea","iNatAg-mini/utricularia_radiata","iNatAg-mini/utricularia_vulgaris","iNatAg-mini/uvaria_littoralis","iNatAg-mini/uvularia_sessilifolia","iNatAg-mini/vaccinium_angustifolium","iNatAg-mini/vaccinium_corymbosum","iNatAg-mini/vaccinium_macrocarpon","iNatAg-mini/vaccinium_myrtillus","iNatAg-mini/vaccinium_uliginosum","iNatAg-mini/vaccinium_vitis-idaea","iNatAg-mini/valeriana_officinalis","iNatAg-mini/valerianella_eriocarpa","iNatAg-mini/vallisneria_americana","iNatAg-mini/vangueria_infausta","iNatAg-mini/vangueria_madagascariensis","iNatAg-mini/vanilla_planifolia","iNatAg-mini/vateria_indica","iNatAg-mini/ventilago_viminalis","iNatAg-mini/veratrum_album","iNatAg-mini/veratrum_californicum","iNatAg-mini/verbascum_blattaria","iNatAg-mini/verbascum_lychnitis","iNatAg-mini/verbascum_phlomoides","iNatAg-mini/verbascum_thapsus","iNatAg-mini/verbascum_thaspus","iNatAg-mini/verbena_bonariensis","iNatAg-mini/verbena_brasiliensis","iNatAg-mini/verbena_hastata","iNatAg-mini/verbena_officinalis","iNatAg-mini/verbena_urticifolia","iNatAg-mini/vernonia_altissima","iNatAg-mini/vernonia_amygdalina","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maranthus_dubius","iNatAg/amaranthus_hybridus","iNatAg/amaranthus_hypochondriacus","iNatAg/amaranthus_lividus","iNatAg/amaranthus_retroflexus","iNatAg/amaranthus_speciosus","iNatAg/amaranthus_spinosus","iNatAg/amaranthus_tricolor","iNatAg/amaranthus_viridis","iNatAg/ambelania_acida","iNatAg/ambrosia_acanthicarpa","iNatAg/ambrosia_artemisiifolia","iNatAg/ambrosia_confertiflora","iNatAg/ambrosia_psilostachya","iNatAg/ambrosia_tomentosa","iNatAg/ambrosia_trifida","iNatAg/ammannia_latifolia","iNatAg/ammi_majus","iNatAg/ammophila_arenaria","iNatAg/ammophila_breviligulata","iNatAg/amorpha_fruticosa","iNatAg/amorphophallus_paeoniifolius","iNatAg/amsinckia_douglasiana","iNatAg/amsinckia_lycopsoides","iNatAg/anacardium_occidentale","iNatAg/anagallis_arvensis","iNatAg/ananas_comosus","iNatAg/anchusa_azurea","iNatAg/andrographis_paniculata","iNatAg/andropogon_barbinodis","iNatAg/andropogon_bicornis","iNatAg/andropogon_brachystachyus","iNatAg/andropogon_gayanus","iNatAg/andropogon_gyrans","iNatAg/andropogon_hallii","iNatAg/andropogon_leucostachyus","iNatAg/andropogon_ternarius","iNatAg/androsace_septentrionalis","iNatAg/anemone_hepatica","iNatAg/anemone_nemorosa","iNatAg/anethum_graveolens","iNatAg/angelica_archangelica","iNatAg/angelica_atropurpurea","iNatAg/angelica_sylvestris","iNatAg/angophora_costata","iNatAg/angophora_floribunda","iNatAg/annona_atemoya","iNatAg/annona_cherimola","iNatAg/annona_diversifolia","iNatAg/annona_montana","iNatAg/annona_muricata","iNatAg/annona_purpurea","iNatAg/annona_reticulata","iNatAg/annona_senegalensis","iNatAg/annona_squamosa","iNatAg/anogeissus_acuminata","iNatAg/anogeissus_latifolia","iNatAg/anogeissus_pendula","iNatAg/antennaria_dioica","iNatAg/anthemis_arvensis","iNatAg/anthemis_cotula","iNatAg/anthemis_tinctoria","iNatAg/anthephora_pubescens","iNatAg/anthoxanthum_odoratum","iNatAg/anthriscus_cerefolium","iNatAg/anthyllis_vulneraria","iNatAg/antidesma_bunius","iNatAg/antirrhinum_majus","iNatAg/aphandra_natalia","iNatAg/aphanes_arvensis","iNatAg/apios_americana","iNatAg/apium_graveolens","iNatAg/apocynum_cannabinum","iNatAg/apocynum_sibiricum","iNatAg/aponogeton_distachyos","iNatAg/aquilaria_malaccensis","iNatAg/aquilegia_canadensis","iNatAg/aquilegia_vulgaris","iNatAg/arachis_glabrata","iNatAg/arachis_hypogaea","iNatAg/arachis_pintoi","iNatAg/arachis_villosa","iNatAg/araucaria_angustifolia","iNatAg/araucaria_bidwillii","iNatAg/araucaria_cunninghamii","iNatAg/araucaria_hunsteinii","iNatAg/arbutus_unedo","iNatAg/archidendron_jiringa","iNatAg/arctium_lappa","iNatAg/arctostaphylos_glandulosa","iNatAg/arctostaphylos_manzanita","iNatAg/arctostaphylos_patula","iNatAg/arctostaphylos_uva-ursi","iNatAg/arctostaphylos_viscida","iNatAg/ardisia_crenata","iNatAg/areca_catechu","iNatAg/arenaria_serpyllifolia","iNatAg/arenga_pinnata","iNatAg/argemone_mexicana","iNatAg/argyrodendron_actinophyllum","iNatAg/argyrodendron_peralatum","iNatAg/aria_alnifolia","iNatAg/aristida_adscensionis","iNatAg/aristida_behriana","iNatAg/aristida_congesta","iNatAg/aristida_junciformis","iNatAg/aristida_lanosa","iNatAg/aristida_latifolia","iNatAg/aristida_longispica","iNatAg/aristida_personata","iNatAg/aristida_purpurascens","iNatAg/aristida_schiedeana","iNatAg/aristida_transvaalensis","iNatAg/aristolochia_rotunda","iNatAg/armoracia_rusticana","iNatAg/arnica_montana","iNatAg/arrhenatherum_elatius","iNatAg/artemisia_abrotanum","iNatAg/artemisia_absinthium","iNatAg/artemisia_afra","iNatAg/artemisia_annua","iNatAg/artemisia_campestris","iNatAg/artemisia_dracunculus","iNatAg/artemisia_filifolia","iNatAg/artemisia_glacialis","iNatAg/artemisia_herba-alba","iNatAg/artemisia_ludoviciana","iNatAg/artemisia_stelleriana","iNatAg/artemisia_tridentata","iNatAg/artemisia_vulgaris","iNatAg/artocarpus_altilis","iNatAg/artocarpus_heterophyllus","iNatAg/artocarpus_hirsutus","iNatAg/artocarpus_integer","iNatAg/artocarpus_lakoocha","iNatAg/arundinella_hirta","iNatAg/arundo_donax","iNatAg/asarina_stricta","iNatAg/asarum_europaeum","iNatAg/asclepias_curassavica","iNatAg/asclepias_fascicularis","iNatAg/asclepias_incarnata","iNatAg/asclepias_lanceolata","iNatAg/asclepias_purpurascens","iNatAg/asclepias_speciosa","iNatAg/asclepias_subverticillata","iNatAg/asclepias_tuberosa","iNatAg/asclepias_verticillata","iNatAg/asclepias_viridiflora","iNatAg/asimina_angustifolia","iNatAg/asimina_triloba","iNatAg/asparagus_densiflorus","iNatAg/asparagus_officinalis","iNatAg/asperula_arvensis","iNatAg/asphodelus_albus","iNatAg/asphodelus_tenuifolius","iNatAg/aspilia_angustifolia","iNatAg/asplenium_ruta-muraria","iNatAg/aster_ericoides","iNatAg/astragalus_adsurgens","iNatAg/astragalus_asymmetricus","iNatAg/astragalus_canadensis","iNatAg/astragalus_cicer","iNatAg/astragalus_gummifer","iNatAg/astragalus_mollissimus","iNatAg/astragalus_nuttallianus","iNatAg/astragalus_sinicus","iNatAg/astragalus_tweedyi","iNatAg/astrantia_major","iNatAg/astrebla_lappacea","iNatAg/astrebla_pectinata","iNatAg/astrebla_squarrosa","iNatAg/astrocaryum_jauari","iNatAg/astrocaryum_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ophyllum_apetalum","iNatAg/calophyllum_brasiliense","iNatAg/calophyllum_inophyllum","iNatAg/calopogonium_caeruleum","iNatAg/calopogonium_mucunoides","iNatAg/calotropis_procera","iNatAg/caltha_palustris","iNatAg/calystegia_hederacea","iNatAg/calystegia_occidentalis","iNatAg/calystegia_pubescens","iNatAg/camelina_sativa","iNatAg/camellia_sinensis","iNatAg/campanula_americana","iNatAg/campanula_rapunculus","iNatAg/campanula_rotundifolia","iNatAg/cananga_odorata","iNatAg/canavalia_brasiliensis","iNatAg/canavalia_ensiformis","iNatAg/canavalia_gladiata","iNatAg/canna_indica","iNatAg/canthium_spinosum","iNatAg/capparis_decidua","iNatAg/capparis_spinosa","iNatAg/capparis_tomentosa","iNatAg/capsella_bursa-pastoris","iNatAg/capsicum_annuum","iNatAg/capsicum_chinense","iNatAg/capsicum_frutescens","iNatAg/capsicum_pubescens","iNatAg/caragana_arborescens","iNatAg/caragana_microphylla","iNatAg/carapa_guianensis","iNatAg/cardamine_flexuosa","iNatAg/cardamine_hirsuta","iNatAg/cardamine_impatiens","iNatAg/cardamine_oligosperma","iNatAg/cardamine_parviflora","iNatAg/cardamine_pratensis","iNatAg/cardiospermum_halicacabum","iNatAg/carduus_acanthoides","iNatAg/carduus_crispus","iNatAg/carduus_lanceolatus","iNatAg/carduus_pycnocephalus","iNatAg/carex_nebrascensis","iNatAg/carex_pallescens","iNatAg/carica_cauliflora","iNatAg/carica_papaya","iNatAg/carica_pubescens","iNatAg/cariniana_pyriformis","iNatAg/carissa_carandas","iNatAg/carissa_edulis","iNatAg/carissa_macrocarpa","iNatAg/carlina_acaulis","iNatAg/carludovica_palmata","iNatAg/caroxylon_aphyllum","iNatAg/carpinus_betulus","iNatAg/carthamus_creticus","iNatAg/carthamus_lanatus","iNatAg/carthamus_tinctorius","iNatAg/carum_carvi","iNatAg/carya_illinoensis","iNatAg/caryodendron_orinocense","iNatAg/caryota_urens","iNatAg/casimiroa_edulis","iNatAg/cassia_articulata","iNatAg/cassia_brewsteri","iNatAg/cassia_fistula","iNatAg/cassia_marilandica","iNatAg/cassia_nictitans","iNatAg/cassia_reticulata","iNatAg/cassia_senna","iNatAg/cassia_siamea","iNatAg/cassia_sieberiana","iNatAg/cassia_tomentosa","iNatAg/cassia_tora","iNatAg/castanea_crenata","iNatAg/castanea_dentata","iNatAg/castanea_mollissima","iNatAg/castanea_pumila","iNatAg/castanea_sativa","iNatAg/castanospermum_australe","iNatAg/castilla_elastica","iNatAg/castilleja_angustifolia","iNatAg/castilleja_occidentalis","iNatAg/casuarina_cristata","iNatAg/casuarina_cunninghamiana","iNatAg/casuarina_equisetifolia","iNatAg/casuarina_glauca","iNatAg/casuarina_junghuhniana","iNatAg/casuarina_obesa","iNatAg/catalpa_bignonioides","iNatAg/catha_edulis","iNatAg/catharanthus_roseus","iNatAg/ceanothus_americanus","iNatAg/ceanothus_prostratus","iNatAg/cedrela_odorata","iNatAg/cedrus_deodara","iNatAg/ceiba_pentandra","iNatAg/celastrus_orbiculatus","iNatAg/celastrus_scandens","iNatAg/celosia_argentea","iNatAg/celtis_australis","iNatAg/cenchrus_biflorus","iNatAg/cenchrus_ciliaris","iNatAg/cenchrus_echinatus","iNatAg/cenchrus_setigerus","iNatAg/cenchrus_spinifex","iNatAg/cenchrus_tribuloides","iNatAg/centaurea_biebersteinii","iNatAg/centaurea_calcitrapa","iNatAg/centaurea_cyanus","iNatAg/centaurea_diluta","iNatAg/centaurea_jacea","iNatAg/centaurea_melitensis","iNatAg/centaurea_nigra","iNatAg/centaurea_nigrescens","iNatAg/centaurea_solstitalis","iNatAg/centaurea_solstitialis","iNatAg/centaurea_stoebe","iNatAg/centaurea_virgata","iNatAg/centella_asiatica","iNatAg/centropodia_glauca","iNatAg/centrosema_brasilianum","iNatAg/centrosema_macrocarpum","iNatAg/centrosema_pascuorum","iNatAg/centrosema_plumieri","iNatAg/centrosema_pubescens","iNatAg/centrosema_virginianum","iNatAg/cephalanthus_occidentalis","iNatAg/cerastium_arvense","iNatAg/cerastium_nutans","iNatAg/cerastium_vulgatum","iNatAg/ceratonia_siliqua","iNatAg/ceratopetalum_apetalum","iNatAg/ceratophyllum_demersum","iNatAg/ceratophyllum_echinatum","iNatAg/ceriops_tagal","iNatAg/cestrum_diurnum","iNatAg/ceterach_officinarum","iNatAg/chaerophyllum_tainturieri","iNatAg/chamaebatia_foliolosa","iNatAg/chamaecrista_nictitans","iNatAg/chamaecrista_rotundifolia","iNatAg/chamaedorea_tepejilote","iNatAg/chamaerops_humilis","iNatAg/chara_intermedia","iNatAg/chelidonium_majus","iNatAg/chenopodium_album","iNatAg/chenopodium_ambrosioides","iNatAg/chenopodium_ambrosoides","iNatAg/chenopodium_berlandieri","iNatAg/chenopodium_bonus-henricus","iNatAg/chenopodium_botrys","iNatAg/chenopodium_ficifolium","iNatAg/chenopodium_gigantospermum","iNatAg/chenopodium_glaucum","iNatAg/chenopodium_missouriense","iNatAg/chenopodium_multifidum","iNatAg/chenopodium_murale","iNatAg/chenopodium_polyspermum","iNatAg/chenopodium_quinoa","iNatAg/chenopodium_rubrum","iNatAg/chenopodium_urbicum","iNatAg/chloris_ciliata","iNatAg/chloris_gayana","iNatAg/chloris_roxburghiana","iNatAg/chloris_verticillata","iNatAg/chloris_virgata","iNatAg/chlorogalum_pomeridianum","iNatAg/chlorophora_excelsa","iNatAg/chlorophytum_comosum","iNatAg/chloroxylon_swietenia","iNatAg/chromolaena_odorata","iNatAg/chrysanthemum_coronarium","iNatAg/chrysanthemum_leucanthemu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s_longus","iNatAg/cyperus_odoratus","iNatAg/cyperus_pilosus","iNatAg/cyperus_prolifer","iNatAg/cyperus_pseudovegetus","iNatAg/cyperus_rotundus","iNatAg/cyperus_sanguinolentus","iNatAg/cyperus_squarrosus","iNatAg/cyperus_strigosus","iNatAg/cyperus_subsquarrosus","iNatAg/cyperus_surinamensis","iNatAg/cyphomandra_betacea","iNatAg/cytisus_albus","iNatAg/cytisus_proliferus","iNatAg/cytisus_supinus","iNatAg/dacrydium_franklinii","iNatAg/dactylis_glomerata","iNatAg/dactyloctenium_aegyptium","iNatAg/dactyloctenium_giganteum","iNatAg/dalbergia_latifolia","iNatAg/dalbergia_melanoxylon","iNatAg/dalbergia_sissoo","iNatAg/daphne_laureola","iNatAg/daphne_mezereum","iNatAg/datura_ferox","iNatAg/datura_quercifolia","iNatAg/datura_stramonium","iNatAg/daucus_carota","iNatAg/daucus_carrota","iNatAg/delairea_odorata","iNatAg/delonix_regia","iNatAg/delphinium_bicolor","iNatAg/delphinium_carolinianum","iNatAg/delphinium_menziesii","iNatAg/delphinium_trolliifolium","iNatAg/dendrocalamus_asper","iNatAg/dendrocalamus_giganteus","iNatAg/dendrocalamus_strictus","iNatAg/dendrolobium_umbellatum","iNatAg/derris_elliptica","iNatAg/deschampsia_caespitosa","iNatAg/deschampsia_flexuosa","iNatAg/desmanthus_leptophyllus","iNatAg/desmanthus_virgatus","iNatAg/desmodium_affine","iNatAg/desmodium_barbatum","iNatAg/desmodium_cuneatum","iNatAg/desmodium_cuspidatum","iNatAg/desmodium_distortum","iNatAg/desmodium_gyroides","iNatAg/desmodium_heterophyllum","iNatAg/desmodium_incanum","iNatAg/desmodium_intortum","iNatAg/desmodium_paniculatum","iNatAg/desmodium_psilocarpum","iNatAg/desmodium_reticulatum","iNatAg/desmodium_sandwicense","iNatAg/desmodium_scorpiurus","iNatAg/desmodium_tortuosum","iNatAg/desmodium_triflorum","iNatAg/desmodium_uncinatum","iNatAg/desmodium_velutinum","iNatAg/dialium_guineense","iNatAg/dianthus_armeria","iNatAg/dichanthium_annulatum","iNatAg/dichanthium_aristatum","iNatAg/dichanthium_caricosum","iNatAg/dichanthium_sericeum","iNatAg/dichondra_carolinensis","iNatAg/dichondra_micrantha","iNatAg/dichrostachys_cinerea","iNatAg/dictamnus_albus","iNatAg/didymopanax_morototoni","iNatAg/diervilla_lonicera","iNatAg/digitalis_lanata","iNatAg/digitalis_lutea","iNatAg/digitalis_purpurea","iNatAg/digitaria_argyrograpta","iNatAg/digitaria_ciliaris","iNatAg/digitaria_decumbens","iNatAg/digitaria_didactyla","iNatAg/digitaria_eriantha","iNatAg/digitaria_tricholaenoides","iNatAg/digitaria_violascens","iNatAg/dillenia_indica","iNatAg/dillenia_pentagyna","iNatAg/diodia_virginiana","iNatAg/dioscorea_alata","iNatAg/dioscorea_bulbifera","iNatAg/dioscorea_esculenta","iNatAg/dioscorea_opposita","iNatAg/dioscorea_oppositifolia","iNatAg/dioscorea_trifida","iNatAg/diospyros_digyna","iNatAg/diospyros_kaki","iNatAg/diospyros_malabarica","iNatAg/diospyros_melanoxylon","iNatAg/diospyros_mespiliformis","iNatAg/diospyros_virginiana","iNatAg/diplachne_fusca","iNatAg/diploglottis_cunninghamii","iNatAg/dipsacus_fullonum","iNatAg/dipsacus_laciniatus","iNatAg/dipsacus_sylvestris","iNatAg/dipterocarpus_alatus","iNatAg/dipterocarpus_indicus","iNatAg/dipterocarpus_turbinatus","iNatAg/dodonaea_viscosa","iNatAg/dovyalis_caffra","iNatAg/dovyalis_hebecarpa","iNatAg/draba_nemorosa","iNatAg/draba_verna","iNatAg/dracocephalum_parviflorum","iNatAg/dracocephalum_thymiflorum","iNatAg/drosera_rotundifolia","iNatAg/dryopteris_filix-mas","iNatAg/duboisia_myoporoides","iNatAg/durio_zibethinus","iNatAg/dysoxylum_fraserianum","iNatAg/ecballium_elaterium","iNatAg/echinacea_purpurea","iNatAg/echinochloa_colona","iNatAg/echinochloa_crus-galli","iNatAg/echinochloa_frumentacea","iNatAg/echinochloa_polystachya","iNatAg/echinochloa_pyramidalis","iNatAg/echinops_sphaerocephalus","iNatAg/echium_plantagineum","iNatAg/echium_vulgare","iNatAg/ehrharta_calycina","iNatAg/ehrharta_erecta","iNatAg/ehrharta_longiflora","iNatAg/ehrharta_villosa","iNatAg/eichhornia_crassipes","iNatAg/ekebergia_capensis","iNatAg/elaeagnus_angustifolia","iNatAg/elaeagnus_multiflora","iNatAg/elaeis_guineensis","iNatAg/elaeis_oleifera","iNatAg/elaeocarpus_grandis","iNatAg/eleagnus_angustifolia","iNatAg/elegia_cuspidata","iNatAg/eleocharis_cellulosa","iNatAg/eleocharis_dulcis","iNatAg/eleocharis_macrostachya","iNatAg/eleocharis_montevidensis","iNatAg/eleocharis_vivipara","iNatAg/elephantopus_mollis","iNatAg/elephantorrhiza_elephantina","iNatAg/elettaria_cardamomum","iNatAg/eleusine_indica","iNatAg/ellisia_nyctelea","iNatAg/elsholtzia_ciliata","iNatAg/elymus_canadensis","iNatAg/elymus_caput-medusae","iNatAg/elymus_cinereus","iNatAg/elymus_condensatus","iNatAg/elymus_dahuricus","iNatAg/elymus_glaucus","iNatAg/elymus_viginicus","iNatAg/elymus_virginicus","iNatAg/emilia_sonchifolia","iNatAg/encalypta_intermedia","iNatAg/enneapogon_scoparius","iNatAg/ensete_ventricosum","iNatAg/entada_abyssinica","iNatAg/entada_africana","iNatAg/enterolobium_cyclocarpum","iNatAg/epilobium_angustifolium","iNatAg/epilobium_ciliatum","iNatAg/equisetum_arvense","iNatAg/equisetum_hyemale","iNatAg/equisetum_palustre","iNatAg/equisetum_sylvaticum","iNatA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NatAg/polyscias_fulva","iNatAg/polytrichum_commune","iNatAg/pongamia_pinnata","iNatAg/pontederia_cordata","iNatAg/pontederia_rotundifolia","iNatAg/populus_balsamifera","iNatAg/populus_ciliata","iNatAg/populus_deltoides","iNatAg/populus_euphratica","iNatAg/populus_simonii","iNatAg/portulaca_oleracea","iNatAg/portulaca_pilosa","iNatAg/portulaca_pilosa_pilosa","iNatAg/portulaca_quadrifida","iNatAg/potamogeton_diversifolius","iNatAg/potamogeton_epihydrus","iNatAg/potamogeton_filiformis","iNatAg/potamogeton_foliosus","iNatAg/potamogeton_friesii","iNatAg/potamogeton_gramineus","iNatAg/potamogeton_illinoensis","iNatAg/potamogeton_natans","iNatAg/potamogeton_nodosus","iNatAg/potamogeton_pectinatus","iNatAg/potamogeton_praelongus","iNatAg/potamogeton_pusillus","iNatAg/potamogeton_zosteriformis","iNatAg/potentilla_anserina","iNatAg/potentilla_argentea","iNatAg/potentilla_erecta","iNatAg/potentilla_fruticosa","iNatAg/potentilla_intermedia","iNatAg/potentilla_norvegica","iNatAg/potentilla_norvegicae","iNatAg/potentilla_recta","iNatAg/potentilla_reptans","iNatAg/potentilla_tridentata","iNatAg/poterium_sanguisorba","iNatAg/pouteria_campechiana","iNatAg/pouteria_lucuma","iNatAg/pouteria_sapota","iNatAg/prasophyllum_elatum","iNatAg/primula_veris","iNatAg/proserpinaca_palustris","iNatAg/proserpinaca_pectinata","iNatAg/prosopis_affinis","iNatAg/prosopis_africana","iNatAg/prosopis_alba","iNatAg/prosopis_chilensis","iNatAg/prosopis_cineraria","iNatAg/prosopis_glandulosa","iNatAg/prosopis_juliflora","iNatAg/prosopis_nigra","iNatAg/prosopis_pallida","iNatAg/prosopis_tamarugo","iNatAg/prosopis_velutina","iNatAg/prunella_vulgaris","iNatAg/prunus_africana","iNatAg/prunus_amygdalus","iNatAg/prunus_armeniaca","iNatAg/prunus_avium","iNatAg/prunus_capuli","iNatAg/prunus_cerasus","iNatAg/prunus_domestica","iNatAg/prunus_laurocerasus","iNatAg/prunus_mahaleb","iNatAg/prunus_mume","iNatAg/prunus_padus","iNatAg/prunus_pensylvanica","iNatAg/prunus_persica","iNatAg/prunus_salicina","iNatAg/prunus_spinosa","iNatAg/prunus_virginiana","iNatAg/psathyrostachys_juncea","iNatAg/psidium_cattleianum","iNatAg/psidium_friedrichsthalianum","iNatAg/psidium_guajava","iNatAg/psophocarpus_tetragonolobus","iNatAg/psoralea_repens","iNatAg/ptelea_trifoliata","iNatAg/pterocarpus_angolensis","iNatAg/pterocarpus_dalbergioides","iNatAg/pterocarpus_erinaceus","iNatAg/pterocarpus_indicus","iNatAg/pterocarpus_lucens","iNatAg/pterocarpus_macrocarpus","iNatAg/pterocarpus_marsupium","iNatAg/pterocarpus_santalinoides","iNatAg/pterocarpus_santalinus","iNatAg/pueraria_lobata","iNatAg/pueraria_phaseoloides","iNatAg/pulmonaria_officinalis","iNatAg/punica_granatum","iNatAg/pycnanthus_angolensis","iNatAg/pyrola_rotundifolia","iNatAg/pyrus_communis","iNatAg/pyrus_pyrifolia","iNatAg/quercus_agrifolia","iNatAg/quercus_alba","iNatAg/quercus_bicolor","iNatAg/quercus_chrysolepis","iNatAg/quercus_dumosa","iNatAg/quercus_fusiformis","iNatAg/quercus_ilex","iNatAg/quercus_incana","iNatAg/quercus_lanata","iNatAg/quercus_nigra","iNatAg/quercus_phellos","iNatAg/quercus_robur","iNatAg/quercus_semecarpifolia","iNatAg/quercus_suber","iNatAg/quercus_virginiana","iNatAg/quisqualis_indica","iNatAg/ranunculus_abortivus","iNatAg/ranunculus_acris","iNatAg/ranunculus_arbortivus","iNatAg/ranunculus_arvensis","iNatAg/ranunculus_bulbosus","iNatAg/ranunculus_californicus","iNatAg/ranunculus_cymbalaria","iNatAg/ranunculus_ficaria","iNatAg/ranunculus_flabellaris","iNatAg/ranunculus_muricatulus","iNatAg/ranunculus_muricatus","iNatAg/ranunculus_occidentalis","iNatAg/ranunculus_orthorhynchus","iNatAg/ranunculus_parviflorus","iNatAg/ranunculus_sceleratus","iNatAg/ranunculus_testiculatus","iNatAg/ranunculus_trichophyllus","iNatAg/raphanus_raphanistrum","iNatAg/raphanus_sativus","iNatAg/rauvolfia_caffra","iNatAg/rauvolfia_serpentina","iNatAg/reseda_alba","iNatAg/reseda_lutea","iNatAg/retama_monosperma","iNatAg/rhamnus_cathartica","iNatAg/rhamnus_prinoides","iNatAg/rheum_palmatum","iNatAg/rheum_rhaponticum","iNatAg/rhigozum_trichotomum","iNatAg/rhinanthus_crista-galli","iNatAg/rhinanthus_minor","iNatAg/rhizophora_mangle","iNatAg/rhizophora_mucronata","iNatAg/rhizophora_stylosa","iNatAg/rhodiola_rosea","iNatAg/rhododendron_ferrugineum","iNatAg/rhus_copallinum","iNatAg/rhus_glabra","iNatAg/rhus_typhina","iNatAg/rhynchosia_minima","iNatAg/rhynchosia_senna","iNatAg/rhynchosia_sublobata","iNatAg/ribes_hirtellum","iNatAg/ribes_nigrum","iNatAg/ribes_rubrum","iNatAg/ribes_uva-crispa","iNatAg/ribes_viscosissimum","iNatAg/richardia_brasiliensis","iNatAg/richardia_scabra","iNatAg/ricinus_communis","iNatAg/ricinus_comunis","iNatAg/rivina_humilis","iNatAg/robinia_pseudoacacia","iNatAg/roemeria_refracta","iNatAg/rosa_canina","iNatAg/rosa_cinnamomea","iNatAg/rosa_eglanteria","iNatAg/rosa_pendulina","iNatAg/rosa_pimpinellifolia","iNatAg/rosa_rubiginosa","iNatAg/rosa_spinosissima","iNatAg/roseodendron_donnell-smithii","iNatAg/rosmarinus_officinalis","iNatAg/rubia_tinctorum","iNatAg/rubus_ellipticus","iNatAg/rubus_fructicosus","iNatAg/rubus_fruticosus","iNatAg/rubus_hispidus","iNatAg/rubus_idaeus","iNatAg/rubus_moluccanus","iNatAg/rubus_occidentalis","iNatAg/rubus_pensilvanicus","iNatAg/rudbeckia_amplexicaulis","iNatAg/rudbeckia_hirta","iNatAg/rudbeckia_laciniata","iNatAg/rudbeckia_triloba","iNatAg/rumex_acetosa","iNatAg/rumex_acetosella","iNatAg/rumex_aquaticus","iNatAg/rumex_crispus","iNatAg/rumex_dentatus","iNatAg/rumex_hymenosepalus","iNatAg/rumex_longifolius","iNatAg/rumex_maritimus","iNatAg/rumex_obtusifolius","iNatAg/rumex_patienta","iNatAg/rumex_patientia","iNatAg/rumex_pseudonatronatus","iNatAg/rumex_pulcher","iNatAg/rumex_verticillatus","iNatAg/ruppia_maritima","iNatAg/ruscus_aculeatus","iNatAg/ruta_graveolens","iNatAg/saccharum_officinarum","iNatAg/saccharum_sinense","iNatAg/saccharum_spontaneum","iNatAg/sacorstemma_cynanchoides","iNatAg/sagina_procumbens","iNatAg/sagittaria_kurziana","iNatAg/sagittaria_lancifolia","iNatAg/sagittaria_latifolia","iNatAg/sagittaria_sagittifolia","iNatAg/salacca_wallichiana","iNatAg/salacca_zalacca","iNatAg/salicornia_bigelovii","iNatAg/salix_alba","iNatAg/salix_caprea","iNatAg/salix_laevigata","iNatAg/salix_pentandra","iNatAg/salix_viminalis","iNatAg/salsola_kali","iNatAg/salsola_tragus","iNatAg/salsola_vermiculata","iNatAg/salvadora_persica","iNatAg/salvia_lyrata","iNatAg/salvia_officinalis","iNatAg/salvia_sclarea","iNatAg/salvia_verticillata","iNatAg/salvinia_auriculata","iNatAg/samanea_saman","iNatAg/sambucus_canadensis","iNatAg/sambucus_canadiensis","iNatAg/sambucus_cerulea","iNatAg/sambucus_ebulus","iNatAg/sambucus_nigra","iNatAg/sambucus_racemosa","iNatAg/samolus_parviflorus","iNatAg/samolus_valerandi","iNatAg/sanguisorba_minor","iNatAg/sanguisorba_officinalis","iNatAg/sanicula_europaea","iNatAg/santalum_acuminatum","iNatAg/santalum_album","iNatAg/santolina_chamaecyparissus","iNatAg/sapindus_emarginatus","iNatAg/sapindus_saponaria","iNatAg/sapium_sebiferum","iNatAg/saponaria_officinalis","iNatAg/sarcostemma_cynanchoides","iNatAg/satureja_hortensis","iNatAg/satureja_montana","iNatAg/sauropus_androgynus","iNatAg/saururus_cernuus","iNatAg/scandix_pecten-veneris","iNatAg/schima_wallichii","iNatAg/schinus_molle","iNatAg/schinus_terebinthifolia","iNatAg/schinus_terebinthifolius","iNatAg/schismus_arabicus","iNatAg/schizolobium_parahyba","iNatAg/schizomeria_ovata","iNatAg/schleichera_oleosa","iNatAg/scirpus_lacustris","iNatAg/scleranthus_annuus","iNatAg/sclerocarya_caffra","iNatAg/scoparia_dulcis","iNatAg/scorzonera_laciniata","iNatAg/scrophularia_lanceolata","iNatAg/searsia_angustifolia","iNatAg/secale_cereale","iNatAg/secale_montanum","iNatAg/sechium_edule","iNatAg/securidaca_longepedunculata","iNatAg/securidaca_longipedunculata","iNatAg/sedum_acre","iNatAg/sedum_telephium","iNatAg/sehima_nervosum","iNatAg/sempervivum_arachnoideum","iNatAg/sempervivum_tectorum","iNatAg/senecio_elegans","iNatAg/senecio_jacobaea","iNatAg/senecio_madagascariensis","iNatAg/senecio_plattensis","iNatAg/senecio_squalidus","iNatAg/senecio_sylvaticus","iNatAg/senecio_viscosus","iNatAg/senecio_vulgaris","iNatAg/senna_spectabilis","iNatAg/serenoa_repens","iNatAg/sesamum_indicum","iNatAg/sesbania_bispinosa","iNatAg/sesbania_cannabina","iNatAg/sesbania_exaltata","iNatAg/sesbania_formosa","iNatAg/sesbania_grandiflora","iNatAg/sesbania_pachycarpa","iNatAg/sesbania_sesban","iNatAg/setaria_incrassata","iNatAg/setaria_italica","iNatAg/setaria_lindenbergiana","iNatAg/setaria_pumila","iNatAg/seymeria_pectinata","iNatAg/shorea_robusta","iNatAg/shorea_talura","iNatAg/sicyos_angulatus","iNatAg/sida_angustifolia","iNatAg/sida_cordifolia","iNatAg/sida_spinosa","iNatAg/silene_antirrhina","iNatAg/silene_armeria","iNatAg/silene_conica","iNatAg/silene_conoidea","iNatAg/silene_gallica","iNatAg/silene_noctiflora","iNatAg/silene_pendula","iNatAg/silphium_asperrimum","iNatAg/silphium_integrifolium","iNatAg/silphium_laciniatum","iNatAg/silybum_marianum","iNatAg/simarouba_glauca","iNatAg/simmondsia_chinensis","iNatAg/simsia_auriculata","iNatAg/sinapis_alba","iNatAg/sinapis_arvensis","iNatAg/sinapis_incana","iNatAg/siphonochilus_aethiopicus","iNatAg/sisymbrium_altissimum","iNatAg/sisymbrium_erysimoides","iNatAg/sisymbrium_irio","iNatAg/sisymbrium_officinale","iNatAg/sisymbrium_orientale","iNatAg/sisymbrium_sophia","iNatAg/sisyrinchium_montanum","iNatAg/sloanea_woollsii","iNatAg/smilax_aspera","iNatAg/smilax_bona-nox","iNatAg/smilax_laurifolia","iNatAg/smilax_rotundifolia","iNatAg/solanum_aethiopicum","iNatAg/solanum_americanum","iNatAg/solanum_capsicoides","iNatAg/solanum_carolinense","iNatAg/solanum_coriaceum","iNatAg/solanum_dimidiatum","iNatAg/solanum_diphyllum","iNatAg/solanum_dulcamara","iNatAg/solanum_elaeagnifolium","iNatAg/solanum_eleagnifolium","iNatAg/solanum_ellipticum","iNatAg/solanum_ferox","iNatAg/solanum_heterodoxum","iNatAg/solanum_incanum","iNatAg/solanum_jamaicense","iNatAg/solanum_lanceolatum","iNatAg/solanum_macrocarpon","iNatAg/solanum_mammosum","iNatAg/solanum_marginatum","iNatAg/solanum_mauritianum","iNatAg/solanum_melo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tAg/tradescantia_bracteata","iNatAg/tradescantia_fluminensis","iNatAg/tradescantia_ohiensis","iNatAg/tradescantia_virginiana","iNatAg/tragia_betonicifolia","iNatAg/tragopogon_lamottei","iNatAg/tragopogon_porrifolius","iNatAg/tragopogon_pratensis","iNatAg/tragus_koelerioides","iNatAg/trapa_natans","iNatAg/trema_orientale","iNatAg/trianthema_portulacastrum","iNatAg/tribulus_cistoides","iNatAg/tribulus_terrestris","iNatAg/trichanthera_gigantea","iNatAg/trichoneura_grandiglumis","iNatAg/trichosanthes_cucumerina","iNatAg/trichostema_lanceolatum","iNatAg/tridax_procumbens","iNatAg/trifolium_africanum","iNatAg/trifolium_alexandrinum","iNatAg/trifolium_ambiguum","iNatAg/trifolium_angustifolium","iNatAg/trifolium_arvense","iNatAg/trifolium_burchellianum","iNatAg/trifolium_campestre","iNatAg/trifolium_carolinianum","iNatAg/trifolium_cherleri","iNatAg/trifolium_dubium","iNatAg/trifolium_fragiferum","iNatAg/trifolium_glanduliferum","iNatAg/trifolium_glomeratum","iNatAg/trifolium_hirtum","iNatAg/trifolium_hybridum","iNatAg/trifolium_incarnatum","iNatAg/trifolium_medium","iNatAg/trifolium_michelianum","iNatAg/trifolium_nigrescens","iNatAg/trifolium_patens","iNatAg/trifolium_pilulare","iNatAg/trifolium_polymorphum","iNatAg/trifolium_pratense","iNatAg/trifolium_reflexum","iNatAg/trifolium_repens","iNatAg/trifolium_resupinatum","iNatAg/trifolium_subterraneum","iNatAg/trifolium_tomentosum","iNatAg/trifolium_variegatum","iNatAg/trifolium_vesiculosum","iNatAg/trifolium_wormskioldii","iNatAg/triglochin_maritima","iNatAg/triglochin_maritimum","iNatAg/triglochin_palustre","iNatAg/trigonella_foenum-graecum","iNatAg/tripsacum_dactyloides","iNatAg/trisetum_flavescens","iNatAg/tristachya_leucothrix","iNatAg/triticum_aestivum","iNatAg/triticum_dicoccoides","iNatAg/triticum_durum","iNatAg/triticum_spelta","iNatAg/triumfetta_rhomboidea","iNatAg/triumfetta_semitriloba","iNatAg/trollius_europaeus","iNatAg/tropaeolum_majus","iNatAg/tropaeolum_tuberosum","iNatAg/tropidocarpum_gracile","iNatAg/turritis_glabra","iNatAg/tussilago_farfara","iNatAg/tylosema_esculentum","iNatAg/typha_angustifolia","iNatAg/typha_domingensis","iNatAg/typha_latifolia","iNatAg/uapaca_kirkiana","iNatAg/ulex_europaeus","iNatAg/ullucus_tuberosus","iNatAg/ulmus_procera","iNatAg/umbilicus_rupestris","iNatAg/uncaria_gambir","iNatAg/urelytrum_agropyroides","iNatAg/urena_lobata","iNatAg/urochloa_mosambicensis","iNatAg/urochloa_panicoides","iNatAg/urtica_chamaedryoides","iNatAg/urtica_dioica","iNatAg/urtica_urens","iNatAg/utricularia_floridana","iNatAg/utricularia_foliosa","iNatAg/utricularia_gibba","iNatAg/utricularia_purpurea","iNatAg/utricularia_radiata","iNatAg/utricularia_vulgaris","iNatAg/uvaria_littoralis","iNatAg/uvularia_sessilifolia","iNatAg/vaccinium_angustifolium","iNatAg/vaccinium_corymbosum","iNatAg/vaccinium_macrocarpon","iNatAg/vaccinium_myrtillus","iNatAg/vaccinium_uliginosum","iNatAg/vaccinium_vitis-idaea","iNatAg/valeriana_officinalis","iNatAg/valerianella_eriocarpa","iNatAg/vallisneria_americana","iNatAg/vangueria_infausta","iNatAg/vangueria_madagascariensis","iNatAg/vanilla_planifolia","iNatAg/vateria_indica","iNatAg/ventilago_viminalis","iNatAg/veratrum_album","iNatAg/veratrum_californicum","iNatAg/verbascum_blattaria","iNatAg/verbascum_lychnitis","iNatAg/verbascum_phlomoides","iNatAg/verbascum_thapsus","iNatAg/verbascum_thaspus","iNatAg/verbena_bonariensis","iNatAg/verbena_brasiliensis","iNatAg/verbena_hastata","iNatAg/verbena_officinalis","iNatAg/verbena_urticifolia","iNatAg/vernonia_altissima","iNatAg/vernonia_amygdalina","iNatAg/vernonia_baldwinii","iNatAg/vernonia_chamaedrys","iNatAg/vernonia_fasciculata","iNatAg/veronica_agrestis","iNatAg/veronica_anagallis-aquatica","iNatAg/veronica_arvensis","iNatAg/veronica_biloba","iNatAg/veronica_chamaedrys","iNatAg/veronica_filiformis","iNatAg/veronica_hederaefolia","iNatAg/veronica_hederifolia","iNatAg/veronica_longifolia","iNatAg/veronica_officinalis","iNatAg/veronica_peregrina","iNatAg/veronica_polita","iNatAg/veronica_serpyllifolia","iNatAg/vetiveria_zizanioides","iNatAg/viburnum_cassinoides","iNatAg/viburnum_lentago","iNatAg/viburnum_prunifolium","iNatAg/viccia_cracca","iNatAg/vicia_augustifolia","iNatAg/vicia_benghalensis","iNatAg/vicia_cracca","iNatAg/vicia_ervilia","iNatAg/vicia_faba","iNatAg/vicia_monantha","iNatAg/vicia_narbonensis","iNatAg/vicia_pannonica","iNatAg/vicia_sativa","iNatAg/vicia_sepium","iNatAg/vigna_adenantha","iNatAg/vigna_angularis","iNatAg/vigna_hosei","iNatAg/vigna_lanceolata","iNatAg/vigna_longifolia","iNatAg/vigna_luteola","iNatAg/vigna_parkeri","iNatAg/vigna_radiata","iNatAg/vigna_trilobata","iNatAg/vigna_umbellata","iNatAg/vigna_unguiculata","iNatAg/vigna_vexillata","iNatAg/vinca_major","iNatAg/vinca_minor","iNatAg/viola_lanceolata","iNatAg/viola_odorata","iNatAg/viola_tricolor","iNatAg/viscum_album","iNatAg/vitellaria_paradoxa","iNatAg/vitex_agnus-castus","iNatAg/vitex_doniana","iNatAg/vitex_negundo","iNatAg/vitis_labrusca","iNatAg/vitis_rotundifolia","iNatAg/vitis_vinifera",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\ No newline at end of file
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bescens","iNatAg-mini/averrhoa_bilimbi","iNatAg-mini/averrhoa_carambola","iNatAg-mini/avicennia_germinans","iNatAg-mini/avicennia_marina","iNatAg-mini/avicennia_officinalis","iNatAg-mini/axonopus_affinis","iNatAg-mini/axonopus_compressus","iNatAg-mini/axonopus_fissifolius","iNatAg-mini/axyris_amaranthoides","iNatAg-mini/azadirachta_indica","iNatAg-mini/azanza_garckeana","iNatAg-mini/azolla_filiculoides","iNatAg-mini/azolla_pinnata","iNatAg-mini/baccaurea_motleyana","iNatAg-mini/baccaurea_ramiflora","iNatAg-mini/baccharis_glutinosa","iNatAg-mini/baccharis_pilularis","iNatAg-mini/bactris_gasipaes","iNatAg-mini/baikiaea_plurijuga","iNatAg-mini/balanites_aegyptiaca","iNatAg-mini/bambusa_arundinacea","iNatAg-mini/bambusa_balcooa","iNatAg-mini/bambusa_blumeana","iNatAg-mini/bambusa_tulda","iNatAg-mini/bambusa_vulgaris","iNatAg-mini/banksia_integrifolia","iNatAg-mini/banksia_occidentalis","iNatAg-mini/baphia_nitida","iNatAg-mini/barringtonia_racemosa","iNatAg-mini/basella_alba","iNatAg-mini/bauhinia_aculeata","iNatAg-mini/bauhinia_petersiana","iNatAg-mini/bauhinia_racemosa","iNatAg-mini/bauhinia_rufescens","iNatAg-mini/bauhinia_thonningii","iNatAg-mini/bauhinia_tomentosa","iNatAg-mini/bauhinia_variegata","iNatAg-mini/beckmannia_eruciformis","iNatAg-mini/beckmannia_syzigachne","iNatAg-mini/bellis_perennis","iNatAg-mini/benincasa_hispida","iNatAg-mini/berberis_aquifolium","iNatAg-mini/berberis_thunbergii","iNatAg-mini/berberis_vulgaris","iNatAg-mini/berchemia_discolor","iNatAg-mini/berrya_cordifolia","iNatAg-mini/bersama_lucens","iNatAg-mini/bertholletia_excelsa","iNatAg-mini/beta_vulgaris","iNatAg-mini/betula_nigra","iNatAg-mini/betula_pendula","iNatAg-mini/betula_pubescens","iNatAg-mini/bidens_bipinnata","iNatAg-mini/bidens_cernua","iNatAg-mini/bidens_frondosa","iNatAg-mini/bidens_pilosa","iNatAg-mini/bidens_tripartita","iNatAg-mini/bignonia_capreolata","iNatAg-mini/biserrula_pelecinus","iNatAg-mini/bixa_orellana","iNatAg-mini/blighia_sapida","iNatAg-mini/blumea_balsamifera","iNatAg-mini/bocconia_frutescens","iNatAg-mini/boehmeria_nivea","iNatAg-mini/boerhavia_coccinea","iNatAg-mini/boerhavia_diffusa","iNatAg-mini/boerhavia_erecta","iNatAg-mini/boesenbergia_rotunda","iNatAg-mini/bolusanthus_speciosus","iNatAg-mini/bombacopsis_quinata","iNatAg-mini/bombax_ceiba","iNatAg-mini/bombax_insigne","iNatAg-mini/borago_officinalis","iNatAg-mini/borassus_aethiopum","iNatAg-mini/borassus_flabellifer","iNatAg-mini/borojoa_patinoi","iNatAg-mini/boronia_glabra","iNatAg-mini/boscia_angustifolia","iNatAg-mini/boswellia_serrata","iNatAg-mini/bothriochloa_bladhii","iNatAg-mini/bothriochloa_insculpta","iNatAg-mini/bothriochloa_ischaemum","iNatAg-mini/bothriochloa_pertusa","iNatAg-mini/bougainvillea_glabra","iNatAg-mini/bouteloua_curtipendula","iNatAg-mini/bouteloua_gracilis","iNatAg-mini/brachiaria_brizantha","iNatAg-mini/brachiaria_decumbens","iNatAg-mini/brachiaria_deflexa","iNatAg-mini/brachiaria_distachya","iNatAg-mini/brachiaria_humidicola","iNatAg-mini/brachiaria_mutica","iNatAg-mini/brachiaria_ramosa","iNatAg-mini/brachiaria_serrata","iNatAg-mini/brachychiton_acerifolius","iNatAg-mini/brachychiton_populneus","iNatAg-mini/brachylaena_huillensis","iNatAg-mini/brachystegia_spiciformis","iNatAg-mini/brassica_campestris","iNatAg-mini/brassica_chinensis","iNatAg-mini/brassica_incana","iNatAg-mini/brassica_juncea","iNatAg-mini/brassica_napus","iNatAg-mini/brassica_nigra","iNatAg-mini/brassica_rapa","iNatAg-mini/brassica_tournefortii","iNatAg-mini/bridelia_micrantha","iNatAg-mini/briza_maxima","iNatAg-mini/briza_media","iNatAg-mini/briza_minor","iNatAg-mini/bromus_arvensis","iNatAg-mini/bromus_carinatus","iNatAg-mini/bromus_catharticus","iNatAg-mini/bromus_diandrus","iNatAg-mini/bromus_erectus","iNatAg-mini/bromus_hordeaceus","iNatAg-mini/bromus_inermis","iNatAg-mini/bromus_madritensis","iNatAg-mini/bromus_marginatus","iNatAg-mini/bromus_racemosus","iNatAg-mini/bromus_rubens","iNatAg-mini/bromus_secalinus","iNatAg-mini/bromus_sterilis","iNatAg-mini/bromus_tectorum","iNatAg-mini/bromus_unioloides","iNatAg-mini/bromus_willdenowii","iNatAg-mini/brosimum_alicastrum","iNatAg-mini/broussonetia_papyrifera","iNatAg-mini/bruguiera_gymnorrhiza","iNatAg-mini/bryonia_alba","iNatAg-mini/bryonia_cretica","iNatAg-mini/buchloe_dactyloides","iNatAg-mini/buckinghamia_celsissima","iNatAg-mini/bunias_erucago","iNatAg-mini/bunias_orientalis","iNatAg-mini/burkea_africana","iNatAg-mini/bursera_simaruba","iNatAg-mini/butea_monosperma","iNatAg-mini/butomus_umbellatus","iNatAg-mini/buxus_sempervirens","iNatAg-mini/cacalia_atriplicifolia","iNatAg-mini/caesalpinia_coriaria","iNatAg-mini/caesalpinia_sappan","iNatAg-mini/cajanus_cajan","iNatAg-mini/calamagrostis_epigeios","iNatAg-mini/calathea_allouia","iNatAg-mini/calendula_arvensis","iNatAg-mini/calendula_officinalis","iNatAg-mini/calliandra_calothyrsus","iNatAg-mini/calliandra_tweedii","iNatAg-mini/callisia_angustifolia","iNatAg-mini/callitriche_palustris","iNatAg-mini/callitriche_stagnalis","iNatAg-mini/callitriche_verna","iNatAg-mini/callitris_columellaris","iNatAg-mini/callitris_endlicheri","iNatAg-mini/callitris_macleayana","iNatAg-mini/calluna_vulgaris","iNatAg-mini/calodendrum_capense","iNatAg-mini/calophyllum_apetalum","iNatAg-mini/calophyllum_brasiliense","iNatAg-mini/calophyllum_inophyllum","iNatAg-mini/calopogonium_caeruleum","iNatAg-mini/calopogonium_mucunoides","iNatAg-mini/calotropis_procera","iNatAg-mini/caltha_palustris","iNatAg-mini/calystegia_hederacea","iNatAg-mini/calystegia_occidentalis","iNatAg-mini/calystegia_pubescens","iNatAg-mini/camelina_sativa","iNatAg-mini/camellia_sinensis","iNatAg-mini/campanula_americana","iNatAg-mini/campanula_rapunculus","iNatAg-mini/campanula_rotundifolia","iNatAg-mini/cananga_odorata","iNatAg-mini/canavalia_brasiliensis","iNatAg-mini/canavalia_ensiformis","iNatAg-mini/canavalia_gladiata","iNatAg-mini/canna_indica","iNatAg-mini/canthium_spinosum","iNatAg-mini/capparis_decidua","iNatAg-mini/capparis_spinosa","iNatAg-mini/capparis_tomentosa","iNatAg-mini/capsella_bursa-pastoris","iNatAg-mini/capsicum_annuum","iNatAg-mini/capsicum_chinense","iNatAg-mini/capsicum_frutescens","iNatAg-mini/capsicum_pubescens","iNatAg-mini/caragana_arborescens","iNatAg-mini/caragana_microphylla","iNatAg-mini/carapa_guianensis","iNatAg-mini/cardamine_flexuosa","iNatAg-mini/cardamine_hirsuta","iNatAg-mini/cardamine_impatiens","iNatAg-mini/cardamine_oligosperma","iNatAg-mini/cardamine_parviflora","iNatAg-mini/cardamine_pratensis","iNatAg-mini/cardiospermum_halicacabum","iNatAg-mini/carduus_acanthoides","iNatAg-mini/carduus_crispus","iNatAg-mini/carduus_lanceolatus","iNatAg-mini/carduus_pycnocephalus","iNatAg-mini/carex_nebrascensis","iNatAg-mini/carex_pallescens","iNatAg-mini/carica_cauliflora","iNatAg-mini/carica_papaya","iNatAg-mini/carica_pubescens","iNatAg-mini/cariniana_pyriformis","iNatAg-mini/carissa_carandas","iNatAg-mini/carissa_edulis","iNatAg-mini/carissa_macrocarpa","iNatAg-mini/carlina_acaulis","iNatAg-mini/carludovica_palmata","iNatAg-mini/caroxylon_aphyllum","iNatAg-mini/carpinus_betulus","iNatAg-mini/carthamus_creticus","iNatAg-mini/carthamus_lanatus","iNatAg-mini/carthamus_tinctorius","iNatAg-mini/carum_carvi","iNatAg-mini/carya_illinoensis","iNatAg-mini/caryodendron_orinocense","iNatAg-mini/caryota_urens","iNatAg-mini/casimiroa_edulis","iNatAg-mini/cassia_articulata","iNatAg-mini/cassia_brewsteri","iNatAg-mini/cassia_fistula","iNatAg-mini/cassia_marilandica","iNatAg-mini/cassia_nictitans","iNatAg-mini/cassia_reticulata","iNatAg-mini/cassia_senna","iNatAg-mini/cassia_siamea","iNatAg-mini/cassia_sieberiana","iNatAg-mini/cassia_tomentosa","iNatAg-mini/cassia_tora","iNatAg-mini/castanea_crenata","iNatAg-mini/castanea_dentata","iNatAg-mini/castanea_mollissima","iNatAg-mini/castanea_pumila","iNatAg-mini/castanea_sativa","iNatAg-mini/castanospermum_australe","iNatAg-mini/castilla_elastica","iNatAg-mini/castilleja_angustifolia","iNatAg-mini/castilleja_occidentalis","iNatAg-mini/casuarina_cristata","iNatAg-mini/casuarina_cunninghamiana","iNatAg-mini/casuarina_equisetifolia","iNatAg-mini/casuarina_glauca","iNatAg-mini/casuarina_junghuhniana","iNatAg-mini/casuarina_obesa","iNatAg-mini/catalpa_bignonioides","iNatAg-mini/catha_edulis","iNatAg-mini/catharanthus_roseus","iNatAg-mini/ceanothus_americanus","iNatAg-mini/ceanothus_prostratus","iNatAg-mini/cedrela_odorata","iNatAg-mini/cedrus_deodara","iNatAg-mini/ceiba_pentandra","iNatAg-mini/celastrus_orbiculatus","iNatAg-mini/celastrus_scandens","iNatAg-mini/celosia_argentea","iNatAg-mini/celtis_australis","iNatAg-mini/cenchrus_biflorus","iNatAg-mini/cenchrus_ciliaris","iNatAg-mini/cenchrus_echinatus","iNatAg-mini/cenchrus_setigerus","iNatAg-mini/cenchrus_spinifex","iNatAg-mini/cenchrus_tribuloides","iNatAg-mini/centaurea_biebersteinii","iNatAg-mini/centaurea_calcitrapa","iNatAg-mini/centaurea_cyanus","iNatAg-mini/centaurea_diluta","iNatAg-mini/centaurea_jacea","iNatAg-mini/centaurea_melitensis","iNatAg-mini/centaurea_nigra","iNatAg-mini/centaurea_nigrescens","iNatAg-mini/centaurea_solstitalis","iNatAg-mini/centaurea_solstitialis","iNatAg-mini/centaurea_stoebe","iNatAg-mini/centaurea_virgata","iNatAg-mini/centella_asiatica","iNatAg-mini/centropodia_glauca","iNatAg-mini/centrosema_brasilianum","iNatAg-mini/centrosema_macrocarpum","iNatAg-mini/centrosema_pascuorum","iNatAg-mini/centrosema_plumieri","iNatAg-mini/centrosema_pubescens","iNatAg-mini/centrosema_virginianum","iNatAg-mini/cephalanthus_occidentalis","iNatAg-mini/cerastium_arvense","iNatAg-mini/cerastium_nutans","iNatAg-mini/cerastium_vulgatum","iNatAg-mini/ceratonia_siliqua","iNatAg-mini/ceratopetalum_apetalum","iNatAg-mini/ceratophyllum_demersum","iNatAg-mini/ceratophyllum_echinatum","iNatAg-mini/ceriops_tagal","iNatAg-mini/cestrum_diurnum","iNatAg-mini/ceterach_officinarum","iNatAg-mini/chaerophyllum_tainturieri","iNatAg-mini/chamaebatia_foliolosa","iNatAg-mini/chamaecrista_nictitans","iNatAg-mini/chamaecrista_rotundifolia","iNatAg-mini/chamaedorea_tepejilote","iNatAg-mini/chamae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riganum_onites","iNatAg-mini/origanum_vulgare","iNatAg-mini/ornithogalum_nutans","iNatAg-mini/ornithogalum_umbellatum","iNatAg-mini/ornithopus_compressus","iNatAg-mini/ornithopus_sativus","iNatAg-mini/orobanche_flava","iNatAg-mini/orobanche_ludoviciana","iNatAg-mini/orobanche_minor","iNatAg-mini/orobanche_ramosa","iNatAg-mini/orontium_aquaticum","iNatAg-mini/orthosiphon_aristatus","iNatAg-mini/oryza_sativa","iNatAg-mini/oryzopsis_holciformis","iNatAg-mini/oryzopsis_miliacea","iNatAg-mini/osmorhiza_berteroi","iNatAg-mini/osmunda_regalis","iNatAg-mini/ottochloa_nodosa","iNatAg-mini/oxalis_acetosella","iNatAg-mini/oxalis_corniculata","iNatAg-mini/oxalis_pes-caprae","iNatAg-mini/oxalis_pescaprae","iNatAg-mini/oxalis_stricta","iNatAg-mini/oxalis_tuberosa","iNatAg-mini/oxytropis_lambertii","iNatAg-mini/pachyrhizus_erosus","iNatAg-mini/paederia_cruddasiana","iNatAg-mini/paederia_foetida","iNatAg-mini/paeonia_officinalis","iNatAg-mini/panax_ginseng","iNatAg-mini/panax_quinquefolius","iNatAg-mini/pangium_edule","iNatAg-mini/panicum_antidotale","iNatAg-mini/panicum_capillare","iNatAg-mini/panicum_coloratum","iNatAg-mini/panicum_ecklonii","iNatAg-mini/panicum_gattingeri","iNatAg-mini/panicum_maximum","iNatAg-mini/panicum_miliaceum","iNatAg-mini/panicum_natalense","iNatAg-mini/panicum_obtusum","iNatAg-mini/panicum_pilosum","iNatAg-mini/panicum_racemosum","iNatAg-mini/panicum_repens","iNatAg-mini/panicum_sphaerocarpon","iNatAg-mini/panicum_trichocladum","iNatAg-mini/panicum_turgidum","iNatAg-mini/panicum_virgatum","iNatAg-mini/papaver_argemone","iNatAg-mini/papaver_bracteatum","iNatAg-mini/papaver_dubium","iNatAg-mini/papaver_rhoeas","iNatAg-mini/papaver_somniferum","iNatAg-mini/parietaria_floridana","iNatAg-mini/parietaria_officinalis","iNatAg-mini/parinari_curatellifolia","iNatAg-mini/parkia_biglobosa","iNatAg-mini/parkia_speciosa","iNatAg-mini/parkinsonia_aculeata","iNatAg-mini/parnassia_palustris","iNatAg-mini/parsonsia_latifolia","iNatAg-mini/parthenium_argentatum","iNatAg-mini/parthenium_hysterophorus","iNatAg-mini/paspalum_conjugatum","iNatAg-mini/paspalum_dilatatum","iNatAg-mini/paspalum_distichum","iNatAg-mini/paspalum_nicorae","iNatAg-mini/paspalum_notatum","iNatAg-mini/paspalum_plicatulum","iNatAg-mini/paspalum_scrobiculatum","iNatAg-mini/paspalum_separatum","iNatAg-mini/paspalum_urvillei","iNatAg-mini/paspalum_vaginatum","iNatAg-mini/passiflora_bicornis","iNatAg-mini/passiflora_edulis","iNatAg-mini/passiflora_foetida","iNatAg-mini/passiflora_incarnata","iNatAg-mini/passiflora_laurifolia","iNatAg-mini/passiflora_ligularis","iNatAg-mini/passiflora_lutea","iNatAg-mini/passiflora_mollissima","iNatAg-mini/passiflora_quadrangularis","iNatAg-mini/passiflora_suberosa","iNatAg-mini/pastinaca_sativa","iNatAg-mini/paullinia_cupana","iNatAg-mini/paulownia_tomentosa","iNatAg-mini/peganum_harmala","iNatAg-mini/pelargonium_graveolens","iNatAg-mini/peltandra_sagittifolia","iNatAg-mini/peltandra_virginica","iNatAg-mini/peltophorum_africanum","iNatAg-mini/peltophorum_pterocarpum","iNatAg-mini/pennisetum_clandestinum","iNatAg-mini/pennisetum_glaucum","iNatAg-mini/pennisetum_macrourum","iNatAg-mini/pennisetum_pedicellatum","iNatAg-mini/pennisetum_polystachyon","iNatAg-mini/pennisetum_purpureum","iNatAg-mini/pennisetum_setaceum","iNatAg-mini/pennisetum_villosum","iNatAg-mini/perilla_frutescens","iNatAg-mini/persea_americana","iNatAg-mini/persicaria_maculosa","iNatAg-mini/persoonia_falcata","iNatAg-mini/petalostigma_pubescens","iNatAg-mini/petasites_albus","iNatAg-mini/petasites_hybridus","iNatAg-mini/petroselinum_crispum","iNatAg-mini/petunia_parviflora","iNatAg-mini/peucedanum_ostruthium","iNatAg-mini/phalaris_aquatica","iNatAg-mini/phalaris_arundinacea","iNatAg-mini/phalaris_arundinaceae","iNatAg-mini/phalaris_brachystachys","iNatAg-mini/phalaris_canariensis","iNatAg-mini/phalaris_caroliniana","iNatAg-mini/phalaris_coerulescens","iNatAg-mini/phalaris_paradoxa","iNatAg-mini/phaseolus_acutifolius","iNatAg-mini/phaseolus_coccineus","iNatAg-mini/phaseolus_lunatus","iNatAg-mini/phaseolus_vulgaris","iNatAg-mini/phleum_alpinum","iNatAg-mini/phleum_pratense","iNatAg-mini/phoenix_dactylifera","iNatAg-mini/phoenix_reclinata","iNatAg-mini/phoenix_sylvestris","iNatAg-mini/phormium_tenax","iNatAg-mini/phragmites_australis","iNatAg-mini/phragmites_communis","iNatAg-mini/phragmites_karka","iNatAg-mini/phyllanthus_niruri","iNatAg-mini/phyllanthus_tenellus","iNatAg-mini/phyllanthus_urinaria","iNatAg-mini/phyllocladus_aspleniifolius","iNatAg-mini/physalis_alkekengi","iNatAg-mini/physalis_angulata","iNatAg-mini/physalis_heterophylla","iNatAg-mini/physalis_lancifolia","iNatAg-mini/physalis_peruviana","iNatAg-mini/physalis_philadelphica","iNatAg-mini/physalis_pubescens","iNatAg-mini/physalis_virginiana","iNatAg-mini/physalis_viscosa","iNatAg-mini/phytolacca_acinosa","iNatAg-mini/phytolacca_americana","iNatAg-mini/phytolacca_dioica","iNatAg-mini/picea_abies","iNatAg-mini/picea_omorica","iNatAg-mini/picea_omorika","iNatAg-mini/picris_echioides","iNa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umatococcus_daniellii","iNatAg-mini/themeda_australis","iNatAg-mini/themeda_quadrivalvis","iNatAg-mini/themeda_triandra","iNatAg-mini/theobroma_bicolor","iNatAg-mini/theobroma_cacao","iNatAg-mini/theobroma_grandiflorum","iNatAg-mini/thermopsis_montana","iNatAg-mini/thermopsis_rhombifolia","iNatAg-mini/thespesia_populnea","iNatAg-mini/thlaspi_arvense","iNatAg-mini/thlaspi_perfoliatum","iNatAg-mini/thuja_occidentalis","iNatAg-mini/thymus_serphyllum","iNatAg-mini/thymus_serpyllum","iNatAg-mini/thymus_vulgaris","iNatAg-mini/thyrsostachys_siamensis","iNatAg-mini/thysanolaena_latifolia","iNatAg-mini/tilia_cordata","iNatAg-mini/tilia_platyphyllos","iNatAg-mini/tipuana_tipu","iNatAg-mini/tithonia_diversifolia","iNatAg-mini/toona_ciliata","iNatAg-mini/torenia_glabra","iNatAg-mini/toxicodendron_pubescens","iNatAg-mini/trachypogon_spicatus","iNatAg-mini/tradescantia_bracteata","iNatAg-mini/tradescantia_fluminensis","iNatAg-mini/tradescantia_ohiensis","iNatAg-mini/tradescantia_virginiana","iNatAg-mini/tragia_betonicifolia","iNatAg-mini/tragopogon_lamottei","iNatAg-mini/tragopogon_porrifolius","iNatAg-mini/tragopogon_pratensis","iNatAg-mini/tragus_koelerioides","iNatAg-mini/trapa_natans","iNatAg-mini/trema_orientale","iNatAg-mini/trianthema_portulacastrum","iNatAg-mini/tribulus_cistoides","iNatAg-mini/tribulus_terrestris","iNatAg-mini/trichanthera_gigantea","iNatAg-mini/trichoneura_grandiglumis","iNatAg-mini/trichosanthes_cucumerina","iNatAg-mini/trichostema_lanceolatum","iNatAg-mini/tridax_procumbens","iNatAg-mini/trifolium_africanum","iNatAg-mini/trifolium_alexandrinum","iNatAg-mini/trifolium_ambiguum","iNatAg-mini/trifolium_angustifolium","iNatAg-mini/trifolium_arvense","iNatAg-mini/trifolium_burchellianum","iNatAg-mini/trifolium_campestre","iNatAg-mini/trifolium_carolinianum","iNatAg-mini/trifolium_cherleri","iNatAg-mini/trifolium_dubium","iNatAg-mini/trifolium_fragiferum","iNatAg-mini/trifolium_glanduliferum","iNatAg-mini/trifolium_glomeratum","iNatAg-mini/trifolium_hirtum","iNatAg-mini/trifolium_hybridum","iNatAg-mini/trifolium_incarnatum","iNatAg-mini/trifolium_medium","iNatAg-mini/trifolium_michelianum","iNatAg-mini/trifolium_nigrescens","iNatAg-mini/trifolium_patens","iNatAg-mini/trifolium_pilulare","iNatAg-mini/trifolium_polymorphum","iNatAg-mini/trifolium_pratense","iNatAg-mini/trifolium_reflexum","iNatAg-mini/trifolium_repens","iNatAg-mini/trifolium_resupinatum","iNatAg-mini/trifolium_subterraneum","iNatAg-mini/trifolium_tomentosum","iNatAg-mini/trifolium_variegatum","iNatAg-mini/trifolium_vesiculosum","iNatAg-mini/trifolium_wormskioldii","iNatAg-mini/triglochin_maritima","iNatAg-mini/triglochin_maritimum","iNatAg-mini/triglochin_palustre","iNatAg-mini/trigonella_foenum-graecum","iNatAg-mini/tripsacum_dactyloides","iNatAg-mini/trisetum_flavescens","iNatAg-mini/tristachya_leucothrix","iNatAg-mini/triticum_aestivum","iNatAg-mini/triticum_dicoccoides","iNatAg-mini/triticum_durum","iNatAg-mini/triticum_spelta","iNatAg-mini/triumfetta_rhomboidea","iNatAg-mini/triumfetta_semitriloba","iNatAg-mini/trollius_europaeus","iNatAg-mini/tropaeolum_majus","iNatAg-mini/tropaeolum_tuberosum","iNatAg-mini/tropidocarpum_gracile","iNatAg-mini/turritis_glabra","iNatAg-mini/tussilago_farfara","iNatAg-mini/tylosema_esculentum","iNatAg-mini/typha_angustifolia","iNatAg-mini/typha_domingensis","iNatAg-mini/typha_latifolia","iNatAg-mini/uapaca_kirkiana","iNatAg-mini/ulex_europaeus","iNatAg-mini/ullucus_tuberosus","iNatAg-mini/ulmus_procera","iNatAg-mini/umbilicus_rupestris","iNatAg-mini/uncaria_gambir","iNatAg-mini/urelytrum_agropyroides","iNatAg-mini/urena_lobata","iNatAg-mini/urochloa_mosambicensis","iNatAg-mini/urochloa_panicoides","iNatAg-mini/urtica_chamaedryoides","iNatAg-mini/urtica_dioica","iNatAg-mini/urtica_urens","iNatAg-mini/utricularia_floridana","iNatAg-mini/utricularia_foliosa","iNatAg-mini/utricularia_gibba","iNatAg-mini/utricularia_purpurea","iNatAg-mini/utricularia_radiata","iNatAg-mini/utricularia_vulgaris","iNatAg-mini/uvaria_littoralis","iNatAg-mini/uvularia_sessilifolia","iNatAg-mini/vaccinium_angustifolium","iNatAg-mini/vaccinium_corymbosum","iNatAg-mini/vaccinium_macrocarpon","iNatAg-mini/vaccinium_myrtillus","iNatAg-mini/vaccinium_uliginosum","iNatAg-mini/vaccinium_vitis-idaea","iNatAg-mini/valeriana_officinalis","iNatAg-mini/valerianella_eriocarpa","iNatAg-mini/vallisneria_americana","iNatAg-mini/vangueria_infausta","iNatAg-mini/vangueria_madagascariensis","iNatAg-mini/vanilla_planifolia","iNatAg-mini/vateria_indica","iNatAg-mini/ventilago_viminalis","iNatAg-mini/veratrum_album","iNatAg-mini/veratrum_californicum","iNatAg-mini/verbascum_blattaria","iNatAg-mini/verbascum_lychnitis","iNatAg-mini/verbascum_phlomoides","iNatAg-mini/verbascum_thapsus","iNatAg-mini/verbascum_thaspus","iNatAg-mini/verbena_bonariensis","iNatAg-mini/verbena_brasiliensis","iNatAg-mini/verbena_hastata","iNatAg-mini/verbena_officinalis","iNatAg-mini/verbena_urticifolia","iNatAg-mini/vernonia_altissima","iNatAg-mini/vernonia_amygdalina","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maranthus_dubius","iNatAg/amaranthus_hybridus","iNatAg/amaranthus_hypochondriacus","iNatAg/amaranthus_lividus","iNatAg/amaranthus_retroflexus","iNatAg/amaranthus_speciosus","iNatAg/amaranthus_spinosus","iNatAg/amaranthus_tricolor","iNatAg/amaranthus_viridis","iNatAg/ambelania_acida","iNatAg/ambrosia_acanthicarpa","iNatAg/ambrosia_artemisiifolia","iNatAg/ambrosia_confertiflora","iNatAg/ambrosia_psilostachya","iNatAg/ambrosia_tomentosa","iNatAg/ambrosia_trifida","iNatAg/ammannia_latifolia","iNatAg/ammi_majus","iNatAg/ammophila_arenaria","iNatAg/ammophila_breviligulata","iNatAg/amorpha_fruticosa","iNatAg/amorphophallus_paeoniifolius","iNatAg/amsinckia_douglasiana","iNatAg/amsinckia_lycopsoides","iNatAg/anacardium_occidentale","iNatAg/anagallis_arvensis","iNatAg/ananas_comosus","iNatAg/anchusa_azurea","iNatAg/andrographis_paniculata","iNatAg/andropogon_barbinodis","iNatAg/andropogon_bicornis","iNatAg/andropogon_brachystachyus","iNatAg/andropogon_gayanus","iNatAg/andropogon_gyrans","iNatAg/andropogon_hallii","iNatAg/andropogon_leucostachyus","iNatAg/andropogon_ternarius","iNatAg/androsace_septentrionalis","iNatAg/anemone_hepatica","iNatAg/anemone_nemorosa","iNatAg/anethum_graveolens","iNatAg/angelica_archangelica","iNatAg/angelica_atropurpurea","iNatAg/angelica_sylvestris","iNatAg/angophora_costata","iNatAg/angophora_floribunda","iNatAg/annona_atemoya","iNatAg/annona_cherimola","iNatAg/annona_diversifolia","iNatAg/annona_montana","iNatAg/annona_muricata","iNatAg/annona_purpurea","iNatAg/annona_reticulata","iNatAg/annona_senegalensis","iNatAg/annona_squamosa","iNatAg/anogeissus_acuminata","iNatAg/anogeissus_latifolia","iNatAg/anogeissus_pendula","iNatAg/antennaria_dioica","iNatAg/anthemis_arvensis","iNatAg/anthemis_cotula","iNatAg/anthemis_tinctoria","iNatAg/anthephora_pubescens","iNatAg/anthoxanthum_odoratum","iNatAg/anthriscus_cerefolium","iNatAg/anthyllis_vulneraria","iNatAg/antidesma_bunius","iNatAg/antirrhinum_majus","iNatAg/aphandra_natalia","iNatAg/aphanes_arvensis","iNatAg/apios_americana","iNatAg/apium_graveolens","iNatAg/apocynum_cannabinum","iNatAg/apocynum_sibiricum","iNatAg/aponogeton_distachyos","iNatAg/aquilaria_malaccensis","iNatAg/aquilegia_canadensis","iNatAg/aquilegia_vulgaris","iNatAg/arachis_glabrata","iNatAg/arachis_hypogaea","iNatAg/arachis_pintoi","iNatAg/arachis_villosa","iNatAg/araucaria_angustifolia","iNatAg/araucaria_bidwillii","iNatAg/araucaria_cunninghamii","iNatAg/araucaria_hunsteinii","iNatAg/arbutus_unedo","iNatAg/archidendron_jiringa","iNatAg/arctium_lappa","iNatAg/arctostaphylos_glandulosa","iNatAg/arctostaphylos_manzanita","iNatAg/arctostaphylos_patula","iNatAg/arctostaphylos_uva-ursi","iNatAg/arctostaphylos_viscida","iNatAg/ardisia_crenata","iNatAg/areca_catechu","iNatAg/arenaria_serpyllifolia","iNatAg/arenga_pinnata","iNatAg/argemone_mexicana","iNatAg/argyrodendron_actinophyllum","iNatAg/argyrodendron_peralatum","iNatAg/aria_alnifolia","iNatAg/aristida_adscensionis","iNatAg/aristida_behriana","iNatAg/aristida_congesta","iNatAg/aristida_junciformis","iNatAg/aristida_lanosa","iNatAg/aristida_latifolia","iNatAg/aristida_longispica","iNatAg/aristida_personata","iNatAg/aristida_purpurascens","iNatAg/aristida_schiedeana","iNatAg/aristida_transvaalensis","iNatAg/aristolochia_rotunda","iNatAg/armoracia_rusticana","iNatAg/arnica_montana","iNatAg/arrhenatherum_elatius","iNatAg/artemisia_abrotanum","iNatAg/artemisia_absinthium","iNatAg/artemisia_afra","iNatAg/artemisia_annua","iNatAg/artemisia_campestris","iNatAg/artemisia_dracunculus","iNatAg/artemisia_filifolia","iNatAg/artemisia_glacialis","iNatAg/artemisia_herba-alba","iNatAg/artemisia_ludoviciana","iNatAg/artemisia_stelleriana","iNatAg/artemisia_tridentata","iNatAg/artemisia_vulgaris","iNatAg/artocarpus_altilis","iNatAg/artocarpus_heterophyllus","iNatAg/artocarpus_hirsutus","iNatAg/artocarpus_integer","iNatAg/artocarpus_lakoocha","iNatAg/arundinella_hirta","iNatAg/arundo_donax","iNatAg/asarina_stricta","iNatAg/asarum_europaeum","iNatAg/asclepias_curassavica","iNatAg/asclepias_fascicularis","iNatAg/asclepias_incarnata","iNatAg/asclepias_lanceolata","iNatAg/asclepias_purpurascens","iNatAg/asclepias_speciosa","iNatAg/asclepias_subverticillata","iNatAg/asclepias_tuberosa","iNatAg/asclepias_verticillata","iNatAg/asclepias_viridiflora","iNatAg/asimina_angustifolia","iNatAg/asimina_triloba","iNatAg/asparagus_densiflorus","iNatAg/asparagus_officinalis","iNatAg/asperula_arvensis","iNatAg/asphodelus_albus","iNatAg/asphodelus_tenuifolius","iNatAg/aspilia_angustifolia","iNatAg/asplenium_ruta-muraria","iNatAg/aster_ericoides","iNatAg/astragalus_adsurgens","iNatAg/astragalus_asymmetricus","iNatAg/astragalus_canadensis","iNatAg/astragalus_cicer","iNatAg/astragalus_gummifer","iNatAg/astragalus_mollissimus","iNatAg/astragalus_nuttallianus","iNatAg/astragalus_sinicus","iNatAg/astragalus_tweedyi","iNatAg/astrantia_major","iNatAg/astrebla_lappacea","iNatAg/astrebla_pectinata","iNatAg/astrebla_squarrosa","iNatAg/astrocaryum_jauari","iNatAg/astrocaryum_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ophyllum_apetalum","iNatAg/calophyllum_brasiliense","iNatAg/calophyllum_inophyllum","iNatAg/calopogonium_caeruleum","iNatAg/calopogonium_mucunoides","iNatAg/calotropis_procera","iNatAg/caltha_palustris","iNatAg/calystegia_hederacea","iNatAg/calystegia_occidentalis","iNatAg/calystegia_pubescens","iNatAg/camelina_sativa","iNatAg/camellia_sinensis","iNatAg/campanula_americana","iNatAg/campanula_rapunculus","iNatAg/campanula_rotundifolia","iNatAg/cananga_odorata","iNatAg/canavalia_brasiliensis","iNatAg/canavalia_ensiformis","iNatAg/canavalia_gladiata","iNatAg/canna_indica","iNatAg/canthium_spinosum","iNatAg/capparis_decidua","iNatAg/capparis_spinosa","iNatAg/capparis_tomentosa","iNatAg/capsella_bursa-pastoris","iNatAg/capsicum_annuum","iNatAg/capsicum_chinense","iNatAg/capsicum_frutescens","iNatAg/capsicum_pubescens","iNatAg/caragana_arborescens","iNatAg/caragana_microphylla","iNatAg/carapa_guianensis","iNatAg/cardamine_flexuosa","iNatAg/cardamine_hirsuta","iNatAg/cardamine_impatiens","iNatAg/cardamine_oligosperma","iNatAg/cardamine_parviflora","iNatAg/cardamine_pratensis","iNatAg/cardiospermum_halicacabum","iNatAg/carduus_acanthoides","iNatAg/carduus_crispus","iNatAg/carduus_lanceolatus","iNatAg/carduus_pycnocephalus","iNatAg/carex_nebrascensis","iNatAg/carex_pallescens","iNatAg/carica_cauliflora","iNatAg/carica_papaya","iNatAg/carica_pubescens","iNatAg/cariniana_pyriformis","iNatAg/carissa_carandas","iNatAg/carissa_edulis","iNatAg/carissa_macrocarpa","iNatAg/carlina_acaulis","iNatAg/carludovica_palmata","iNatAg/caroxylon_aphyllum","iNatAg/carpinus_betulus","iNatAg/carthamus_creticus","iNatAg/carthamus_lanatus","iNatAg/carthamus_tinctorius","iNatAg/carum_carvi","iNatAg/carya_illinoensis","iNatAg/caryodendron_orinocense","iNatAg/caryota_urens","iNatAg/casimiroa_edulis","iNatAg/cassia_articulata","iNatAg/cassia_brewsteri","iNatAg/cassia_fistula","iNatAg/cassia_marilandica","iNatAg/cassia_nictitans","iNatAg/cassia_reticulata","iNatAg/cassia_senna","iNatAg/cassia_siamea","iNatAg/cassia_sieberiana","iNatAg/cassia_tomentosa","iNatAg/cassia_tora","iNatAg/castanea_crenata","iNatAg/castanea_dentata","iNatAg/castanea_mollissima","iNatAg/castanea_pumila","iNatAg/castanea_sativa","iNatAg/castanospermum_australe","iNatAg/castilla_elastica","iNatAg/castilleja_angustifolia","iNatAg/castilleja_occidentalis","iNatAg/casuarina_cristata","iNatAg/casuarina_cunninghamiana","iNatAg/casuarina_equisetifolia","iNatAg/casuarina_glauca","iNatAg/casuarina_junghuhniana","iNatAg/casuarina_obesa","iNatAg/catalpa_bignonioides","iNatAg/catha_edulis","iNatAg/catharanthus_roseus","iNatAg/ceanothus_americanus","iNatAg/ceanothus_prostratus","iNatAg/cedrela_odorata","iNatAg/cedrus_deodara","iNatAg/ceiba_pentandra","iNatAg/celastrus_orbiculatus","iNatAg/celastrus_scandens","iNatAg/celosia_argentea","iNatAg/celtis_australis","iNatAg/cenchrus_biflorus","iNatAg/cenchrus_ciliaris","iNatAg/cenchrus_echinatus","iNatAg/cenchrus_setigerus","iNatAg/cenchrus_spinifex","iNatAg/cenchrus_tribuloides","iNatAg/centaurea_biebersteinii","iNatAg/centaurea_calcitrapa","iNatAg/centaurea_cyanus","iNatAg/centaurea_diluta","iNatAg/centaurea_jacea","iNatAg/centaurea_melitensis","iNatAg/centaurea_nigra","iNatAg/centaurea_nigrescens","iNatAg/centaurea_solstitalis","iNatAg/centaurea_solstitialis","iNatAg/centaurea_stoebe","iNatAg/centaurea_virgata","iNatAg/centella_asiatica","iNatAg/centropodia_glauca","iNatAg/centrosema_brasilianum","iNatAg/centrosema_macrocarpum","iNatAg/centrosema_pascuorum","iNatAg/centrosema_plumieri","iNatAg/centrosema_pubescens","iNatAg/centrosema_virginianum","iNatAg/cephalanthus_occidentalis","iNatAg/cerastium_arvense","iNatAg/cerastium_nutans","iNatAg/cerastium_vulgatum","iNatAg/ceratonia_siliqua","iNatAg/ceratopetalum_apetalum","iNatAg/ceratophyllum_demersum","iNatAg/ceratophyllum_echinatum","iNatAg/ceriops_tagal","iNatAg/cestrum_diurnum","iNatAg/ceterach_officinarum","iNatAg/chaerophyllum_tainturieri","iNatAg/chamaebatia_foliolosa","iNatAg/chamaecrista_nictitans","iNatAg/chamaecrista_rotundifolia","iNatAg/chamaedorea_tepejilote","iNatAg/chamaerops_humilis","iNatAg/chara_intermedia","iNatAg/chelidonium_majus","iNatAg/chenopodium_album","iNatAg/chenopodium_ambrosioides","iNatAg/chenopodium_ambrosoides","iNatAg/chenopodium_berlandieri","iNatAg/chenopodium_bonus-henricus","iNatAg/chenopodium_botrys","iNatAg/chenopodium_ficifolium","iNatAg/chenopodium_gigantospermum","iNatAg/chenopodium_glaucum","iNatAg/chenopodium_missouriense","iNatAg/chenopodium_multifidum","iNatAg/chenopodium_murale","iNatAg/chenopodium_polyspermum","iNatAg/chenopodium_quinoa","iNatAg/chenopodium_rubrum","iNatAg/chenopodium_urbicum","iNatAg/chloris_ciliata","iNatAg/chloris_gayana","iNatAg/chloris_roxburghiana","iNatAg/chloris_verticillata","iNatAg/chloris_virgata","iNatAg/chlorogalum_pomeridianum","iNatAg/chlorophora_excelsa","iNatAg/chlorophytum_comosum","iNatAg/chloroxylon_swietenia","iNatAg/chromolaena_odorata","iNatAg/chrysanthemum_coronarium","iNatAg/chrysanthemum_leucanthemu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s_longus","iNatAg/cyperus_odoratus","iNatAg/cyperus_pilosus","iNatAg/cyperus_prolifer","iNatAg/cyperus_pseudovegetus","iNatAg/cyperus_rotundus","iNatAg/cyperus_sanguinolentus","iNatAg/cyperus_squarrosus","iNatAg/cyperus_strigosus","iNatAg/cyperus_subsquarrosus","iNatAg/cyperus_surinamensis","iNatAg/cyphomandra_betacea","iNatAg/cytisus_albus","iNatAg/cytisus_proliferus","iNatAg/cytisus_supinus","iNatAg/dacrydium_franklinii","iNatAg/dactylis_glomerata","iNatAg/dactyloctenium_aegyptium","iNatAg/dactyloctenium_giganteum","iNatAg/dalbergia_latifolia","iNatAg/dalbergia_melanoxylon","iNatAg/dalbergia_sissoo","iNatAg/daphne_laureola","iNatAg/daphne_mezereum","iNatAg/datura_ferox","iNatAg/datura_quercifolia","iNatAg/datura_stramonium","iNatAg/daucus_carota","iNatAg/daucus_carrota","iNatAg/delairea_odorata","iNatAg/delonix_regia","iNatAg/delphinium_bicolor","iNatAg/delphinium_carolinianum","iNatAg/delphinium_menziesii","iNatAg/delphinium_trolliifolium","iNatAg/dendrocalamus_asper","iNatAg/dendrocalamus_giganteus","iNatAg/dendrocalamus_strictus","iNatAg/dendrolobium_umbellatum","iNatAg/derris_elliptica","iNatAg/deschampsia_caespitosa","iNatAg/deschampsia_flexuosa","iNatAg/desmanthus_leptophyllus","iNatAg/desmanthus_virgatus","iNatAg/desmodium_affine","iNatAg/desmodium_barbatum","iNatAg/desmodium_cuneatum","iNatAg/desmodium_cuspidatum","iNatAg/desmodium_distortum","iNatAg/desmodium_gyroides","iNatAg/desmodium_heterophyllum","iNatAg/desmodium_incanum","iNatAg/desmodium_intortum","iNatAg/desmodium_paniculatum","iNatAg/desmodium_psilocarpum","iNatAg/desmodium_reticulatum","iNatAg/desmodium_sandwicense","iNatAg/desmodium_scorpiurus","iNatAg/desmodium_tortuosum","iNatAg/desmodium_triflorum","iNatAg/desmodium_uncinatum","iNatAg/desmodium_velutinum","iNatAg/dialium_guineense","iNatAg/dianthus_armeria","iNatAg/dichanthium_annulatum","iNatAg/dichanthium_aristatum","iNatAg/dichanthium_caricosum","iNatAg/dichanthium_sericeum","iNatAg/dichondra_carolinensis","iNatAg/dichondra_micrantha","iNatAg/dichrostachys_cinerea","iNatAg/dictamnus_albus","iNatAg/didymopanax_morototoni","iNatAg/diervilla_lonicera","iNatAg/digitalis_lanata","iNatAg/digitalis_lutea","iNatAg/digitalis_purpurea","iNatAg/digitaria_argyrograpta","iNatAg/digitaria_ciliaris","iNatAg/digitaria_decumbens","iNatAg/digitaria_didactyla","iNatAg/digitaria_eriantha","iNatAg/digitaria_tricholaenoides","iNatAg/digitaria_violascens","iNatAg/dillenia_indica","iNatAg/dillenia_pentagyna","iNatAg/diodia_virginiana","iNatAg/dioscorea_alata","iNatAg/dioscorea_bulbifera","iNatAg/dioscorea_esculenta","iNatAg/dioscorea_opposita","iNatAg/dioscorea_oppositifolia","iNatAg/dioscorea_trifida","iNatAg/diospyros_digyna","iNatAg/diospyros_kaki","iNatAg/diospyros_malabarica","iNatAg/diospyros_melanoxylon","iNatAg/diospyros_mespiliformis","iNatAg/diospyros_virginiana","iNatAg/diplachne_fusca","iNatAg/diploglottis_cunninghamii","iNatAg/dipsacus_fullonum","iNatAg/dipsacus_laciniatus","iNatAg/dipsacus_sylvestris","iNatAg/dipterocarpus_alatus","iNatAg/dipterocarpus_indicus","iNatAg/dipterocarpus_turbinatus","iNatAg/dodonaea_viscosa","iNatAg/dovyalis_caffra","iNatAg/dovyalis_hebecarpa","iNatAg/draba_nemorosa","iNatAg/draba_verna","iNatAg/dracocephalum_parviflorum","iNatAg/dracocephalum_thymiflorum","iNatAg/drosera_rotundifolia","iNatAg/dryopteris_filix-mas","iNatAg/duboisia_myoporoides","iNatAg/durio_zibethinus","iNatAg/dysoxylum_fraserianum","iNatAg/ecballium_elaterium","iNatAg/echinacea_purpurea","iNatAg/echinochloa_colona","iNatAg/echinochloa_crus-galli","iNatAg/echinochloa_frumentacea","iNatAg/echinochloa_polystachya","iNatAg/echinochloa_pyramidalis","iNatAg/echinops_sphaerocephalus","iNatAg/echium_plantagineum","iNatAg/echium_vulgare","iNatAg/ehrharta_calycina","iNatAg/ehrharta_erecta","iNatAg/ehrharta_longiflora","iNatAg/ehrharta_villosa","iNatAg/eichhornia_crassipes","iNatAg/ekebergia_capensis","iNatAg/elaeagnus_angustifolia","iNatAg/elaeagnus_multiflora","iNatAg/elaeis_guineensis","iNatAg/elaeis_oleifera","iNatAg/elaeocarpus_grandis","iNatAg/eleagnus_angustifolia","iNatAg/elegia_cuspidata","iNatAg/eleocharis_cellulosa","iNatAg/eleocharis_dulcis","iNatAg/eleocharis_macrostachya","iNatAg/eleocharis_montevidensis","iNatAg/eleocharis_vivipara","iNatAg/elephantopus_mollis","iNatAg/elephantorrhiza_elephantina","iNatAg/elettaria_cardamomum","iNatAg/eleusine_indica","iNatAg/ellisia_nyctelea","iNatAg/elsholtzia_ciliata","iNatAg/elymus_canadensis","iNatAg/elymus_caput-medusae","iNatAg/elymus_cinereus","iNatAg/elymus_condensatus","iNatAg/elymus_dahuricus","iNatAg/elymus_glaucus","iNatAg/elymus_viginicus","iNatAg/elymus_virginicus","iNatAg/emilia_sonchifolia","iNatAg/encalypta_intermedia","iNatAg/enneapogon_scoparius","iNatAg/ensete_ventricosum","iNatAg/entada_abyssinica","iNatAg/entada_africana","iNatAg/enterolobium_cyclocarpum","iNatAg/epilobium_angustifolium","iNatAg/epilobium_ciliatum","iNatAg/equisetum_arvense","iNatAg/equisetum_hyemale","iNatAg/equisetum_palustre","iNatAg/equisetum_sylvaticum","iNatA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NatAg/polyscias_fulva","iNatAg/polytrichum_commune","iNatAg/pongamia_pinnata","iNatAg/pontederia_cordata","iNatAg/pontederia_rotundifolia","iNatAg/populus_balsamifera","iNatAg/populus_ciliata","iNatAg/populus_deltoides","iNatAg/populus_euphratica","iNatAg/populus_simonii","iNatAg/portulaca_oleracea","iNatAg/portulaca_pilosa","iNatAg/portulaca_pilosa_pilosa","iNatAg/portulaca_quadrifida","iNatAg/potamogeton_diversifolius","iNatAg/potamogeton_epihydrus","iNatAg/potamogeton_filiformis","iNatAg/potamogeton_foliosus","iNatAg/potamogeton_friesii","iNatAg/potamogeton_gramineus","iNatAg/potamogeton_illinoensis","iNatAg/potamogeton_natans","iNatAg/potamogeton_nodosus","iNatAg/potamogeton_pectinatus","iNatAg/potamogeton_praelongus","iNatAg/potamogeton_pusillus","iNatAg/potamogeton_zosteriformis","iNatAg/potentilla_anserina","iNatAg/potentilla_argentea","iNatAg/potentilla_erecta","iNatAg/potentilla_fruticosa","iNatAg/potentilla_intermedia","iNatAg/potentilla_norvegica","iNatAg/potentilla_norvegicae","iNatAg/potentilla_recta","iNatAg/potentilla_reptans","iNatAg/potentilla_tridentata","iNatAg/poterium_sanguisorba","iNatAg/pouteria_campechiana","iNatAg/pouteria_lucuma","iNatAg/pouteria_sapota","iNatAg/prasophyllum_elatum","iNatAg/primula_veris","iNatAg/proserpinaca_palustris","iNatAg/proserpinaca_pectinata","iNatAg/prosopis_affinis","iNatAg/prosopis_africana","iNatAg/prosopis_alba","iNatAg/prosopis_chilensis","iNatAg/prosopis_cineraria","iNatAg/prosopis_glandulosa","iNatAg/prosopis_juliflora","iNatAg/prosopis_nigra","iNatAg/prosopis_pallida","iNatAg/prosopis_tamarugo","iNatAg/prosopis_velutina","iNatAg/prunella_vulgaris","iNatAg/prunus_africana","iNatAg/prunus_amygdalus","iNatAg/prunus_armeniaca","iNatAg/prunus_avium","iNatAg/prunus_capuli","iNatAg/prunus_cerasus","iNatAg/prunus_domestica","iNatAg/prunus_laurocerasus","iNatAg/prunus_mahaleb","iNatAg/prunus_mume","iNatAg/prunus_padus","iNatAg/prunus_pensylvanica","iNatAg/prunus_persica","iNatAg/prunus_salicina","iNatAg/prunus_spinosa","iNatAg/prunus_virginiana","iNatAg/psathyrostachys_juncea","iNatAg/psidium_cattleianum","iNatAg/psidium_friedrichsthalianum","iNatAg/psidium_guajava","iNatAg/psophocarpus_tetragonolobus","iNatAg/psoralea_repens","iNatAg/ptelea_trifoliata","iNatAg/pterocarpus_angolensis","iNatAg/pterocarpus_dalbergioides","iNatAg/pterocarpus_erinaceus","iNatAg/pterocarpus_indicus","iNatAg/pterocarpus_lucens","iNatAg/pterocarpus_macrocarpus","iNatAg/pterocarpus_marsupium","iNatAg/pterocarpus_santalinoides","iNatAg/pterocarpus_santalinus","iNatAg/pueraria_lobata","iNatAg/pueraria_phaseoloides","iNatAg/pulmonaria_officinalis","iNatAg/punica_granatum","iNatAg/pycnanthus_angolensis","iNatAg/pyrola_rotundifolia","iNatAg/pyrus_communis","iNatAg/pyrus_pyrifolia","iNatAg/quercus_agrifolia","iNatAg/quercus_alba","iNatAg/quercus_bicolor","iNatAg/quercus_chrysolepis","iNatAg/quercus_dumosa","iNatAg/quercus_fusiformis","iNatAg/quercus_ilex","iNatAg/quercus_incana","iNatAg/quercus_lanata","iNatAg/quercus_nigra","iNatAg/quercus_phellos","iNatAg/quercus_robur","iNatAg/quercus_semecarpifolia","iNatAg/quercus_suber","iNatAg/quercus_virginiana","iNatAg/quisqualis_indica","iNatAg/ranunculus_abortivus","iNatAg/ranunculus_acris","iNatAg/ranunculus_arbortivus","iNatAg/ranunculus_arvensis","iNatAg/ranunculus_bulbosus","iNatAg/ranunculus_californicus","iNatAg/ranunculus_cymbalaria","iNatAg/ranunculus_ficaria","iNatAg/ranunculus_flabellaris","iNatAg/ranunculus_muricatulus","iNatAg/ranunculus_muricatus","iNatAg/ranunculus_occidentalis","iNatAg/ranunculus_orthorhynchus","iNatAg/ranunculus_parviflorus","iNatAg/ranunculus_sceleratus","iNatAg/ranunculus_testiculatus","iNatAg/ranunculus_trichophyllus","iNatAg/raphanus_raphanistrum","iNatAg/raphanus_sativus","iNatAg/rauvolfia_caffra","iNatAg/rauvolfia_serpentina","iNatAg/reseda_alba","iNatAg/reseda_lutea","iNatAg/retama_monosperma","iNatAg/rhamnus_cathartica","iNatAg/rhamnus_prinoides","iNatAg/rheum_palmatum","iNatAg/rheum_rhaponticum","iNatAg/rhigozum_trichotomum","iNatAg/rhinanthus_crista-galli","iNatAg/rhinanthus_minor","iNatAg/rhizophora_mangle","iNatAg/rhizophora_mucronata","iNatAg/rhizophora_stylosa","iNatAg/rhodiola_rosea","iNatAg/rhododendron_ferrugineum","iNatAg/rhus_copallinum","iNatAg/rhus_glabra","iNatAg/rhus_typhina","iNatAg/rhynchosia_minima","iNatAg/rhynchosia_senna","iNatAg/rhynchosia_sublobata","iNatAg/ribes_hirtellum","iNatAg/ribes_nigrum","iNatAg/ribes_rubrum","iNatAg/ribes_uva-crispa","iNatAg/ribes_viscosissimum","iNatAg/richardia_brasiliensis","iNatAg/richardia_scabra","iNatAg/ricinus_communis","iNatAg/ricinus_comunis","iNatAg/rivina_humilis","iNatAg/robinia_pseudoacacia","iNatAg/roemeria_refracta","iNatAg/rosa_canina","iNatAg/rosa_cinnamomea","iNatAg/rosa_eglanteria","iNatAg/rosa_pendulina","iNatAg/rosa_pimpinellifolia","iNatAg/rosa_rubiginosa","iNatAg/rosa_spinosissima","iNatAg/roseodendron_donnell-smithii","iNatAg/rosmarinus_officinalis","iNatAg/rubia_tinctorum","iNatAg/rubus_ellipticus","iNatAg/rubus_fructicosus","iNatAg/rubus_fruticosus","iNatAg/rubus_hispidus","iNatAg/rubus_idaeus","iNatAg/rubus_moluccanus","iNatAg/rubus_occidentalis","iNatAg/rubus_pensilvanicus","iNatAg/rudbeckia_amplexicaulis","iNatAg/rudbeckia_hirta","iNatAg/rudbeckia_laciniata","iNatAg/rudbeckia_triloba","iNatAg/rumex_acetosa","iNatAg/rumex_acetosella","iNatAg/rumex_aquaticus","iNatAg/rumex_crispus","iNatAg/rumex_dentatus","iNatAg/rumex_hymenosepalus","iNatAg/rumex_longifolius","iNatAg/rumex_maritimus","iNatAg/rumex_obtusifolius","iNatAg/rumex_patienta","iNatAg/rumex_patientia","iNatAg/rumex_pseudonatronatus","iNatAg/rumex_pulcher","iNatAg/rumex_verticillatus","iNatAg/ruppia_maritima","iNatAg/ruscus_aculeatus","iNatAg/ruta_graveolens","iNatAg/saccharum_officinarum","iNatAg/saccharum_sinense","iNatAg/saccharum_spontaneum","iNatAg/sacorstemma_cynanchoides","iNatAg/sagina_procumbens","iNatAg/sagittaria_kurziana","iNatAg/sagittaria_lancifolia","iNatAg/sagittaria_latifolia","iNatAg/sagittaria_sagittifolia","iNatAg/salacca_wallichiana","iNatAg/salacca_zalacca","iNatAg/salicornia_bigelovii","iNatAg/salix_alba","iNatAg/salix_caprea","iNatAg/salix_laevigata","iNatAg/salix_pentandra","iNatAg/salix_viminalis","iNatAg/salsola_kali","iNatAg/salsola_tragus","iNatAg/salsola_vermiculata","iNatAg/salvadora_persica","iNatAg/salvia_lyrata","iNatAg/salvia_officinalis","iNatAg/salvia_sclarea","iNatAg/salvia_verticillata","iNatAg/salvinia_auriculata","iNatAg/samanea_saman","iNatAg/sambucus_canadensis","iNatAg/sambucus_canadiensis","iNatAg/sambucus_cerulea","iNatAg/sambucus_ebulus","iNatAg/sambucus_nigra","iNatAg/sambucus_racemosa","iNatAg/samolus_parviflorus","iNatAg/samolus_valerandi","iNatAg/sanguisorba_minor","iNatAg/sanguisorba_officinalis","iNatAg/sanicula_europaea","iNatAg/santalum_acuminatum","iNatAg/santalum_album","iNatAg/santolina_chamaecyparissus","iNatAg/sapindus_emarginatus","iNatAg/sapindus_saponaria","iNatAg/sapium_sebiferum","iNatAg/saponaria_officinalis","iNatAg/sarcostemma_cynanchoides","iNatAg/satureja_hortensis","iNatAg/satureja_montana","iNatAg/sauropus_androgynus","iNatAg/saururus_cernuus","iNatAg/scandix_pecten-veneris","iNatAg/schima_wallichii","iNatAg/schinus_molle","iNatAg/schinus_terebinthifolia","iNatAg/schinus_terebinthifolius","iNatAg/schismus_arabicus","iNatAg/schizolobium_parahyba","iNatAg/schizomeria_ovata","iNatAg/schleichera_oleosa","iNatAg/scirpus_lacustris","iNatAg/scleranthus_annuus","iNatAg/sclerocarya_caffra","iNatAg/scoparia_dulcis","iNatAg/scorzonera_laciniata","iNatAg/scrophularia_lanceolata","iNatAg/searsia_angustifolia","iNatAg/secale_cereale","iNatAg/secale_montanum","iNatAg/sechium_edule","iNatAg/securidaca_longepedunculata","iNatAg/securidaca_longipedunculata","iNatAg/sedum_acre","iNatAg/sedum_telephium","iNatAg/sehima_nervosum","iNatAg/sempervivum_arachnoideum","iNatAg/sempervivum_tectorum","iNatAg/senecio_elegans","iNatAg/senecio_jacobaea","iNatAg/senecio_madagascariensis","iNatAg/senecio_plattensis","iNatAg/senecio_squalidus","iNatAg/senecio_sylvaticus","iNatAg/senecio_viscosus","iNatAg/senecio_vulgaris","iNatAg/senna_spectabilis","iNatAg/serenoa_repens","iNatAg/sesamum_indicum","iNatAg/sesbania_bispinosa","iNatAg/sesbania_cannabina","iNatAg/sesbania_exaltata","iNatAg/sesbania_formosa","iNatAg/sesbania_grandiflora","iNatAg/sesbania_pachycarpa","iNatAg/sesbania_sesban","iNatAg/setaria_incrassata","iNatAg/setaria_italica","iNatAg/setaria_lindenbergiana","iNatAg/setaria_pumila","iNatAg/seymeria_pectinata","iNatAg/shorea_robusta","iNatAg/shorea_talura","iNatAg/sicyos_angulatus","iNatAg/sida_angustifolia","iNatAg/sida_cordifolia","iNatAg/sida_spinosa","iNatAg/silene_antirrhina","iNatAg/silene_armeria","iNatAg/silene_conica","iNatAg/silene_conoidea","iNatAg/silene_gallica","iNatAg/silene_noctiflora","iNatAg/silene_pendula","iNatAg/silphium_asperrimum","iNatAg/silphium_integrifolium","iNatAg/silphium_laciniatum","iNatAg/silybum_marianum","iNatAg/simarouba_glauca","iNatAg/simmondsia_chinensis","iNatAg/simsia_auriculata","iNatAg/sinapis_alba","iNatAg/sinapis_arvensis","iNatAg/sinapis_incana","iNatAg/siphonochilus_aethiopicus","iNatAg/sisymbrium_altissimum","iNatAg/sisymbrium_erysimoides","iNatAg/sisymbrium_irio","iNatAg/sisymbrium_officinale","iNatAg/sisymbrium_orientale","iNatAg/sisymbrium_sophia","iNatAg/sisyrinchium_montanum","iNatAg/sloanea_woollsii","iNatAg/smilax_aspera","iNatAg/smilax_bona-nox","iNatAg/smilax_laurifolia","iNatAg/smilax_rotundifolia","iNatAg/solanum_aethiopicum","iNatAg/solanum_americanum","iNatAg/solanum_capsicoides","iNatAg/solanum_carolinense","iNatAg/solanum_coriaceum","iNatAg/solanum_dimidiatum","iNatAg/solanum_diphyllum","iNatAg/solanum_dulcamara","iNatAg/solanum_elaeagnifolium","iNatAg/solanum_eleagnifolium","iNatAg/solanum_ellipticum","iNatAg/solanum_ferox","iNatAg/solanum_heterodoxum","iNatAg/solanum_incanum","iNatAg/solanum_jamaicense","iNatAg/solanum_lanceolatum","iNatAg/solanum_macrocarpon","iNatAg/solanum_mammosum","iNatAg/solanum_marginatum","iNatAg/solanum_mauritianum","iNatAg/solanum_melongena","iNatAg/solanum_muricatum","iNatAg/solanum_nigrum","iNatAg/solanum_physalifolium","iNatAg/solanum_pseudo-capsicum","iNatAg/solanum_pseudocapsicum","iNatAg/solanum_quitoense","iNatAg/solanum_sisymbrifolium","iNatAg/solanum_sisymbriifolium","iNatAg/solanum_tampicense","iNatAg/solanum_torvum","iNatAg/solanum_tuberosum","iNatAg/solanum_violaceum","iNatAg/soldanella_alpina","iNatAg/solidago_californica","iNatAg/solidago_canadensis","iNatAg/solidago_fistulosa","iNatAg/solidago_missouriensis","iNatAg/solidago_nemoralis","iNatAg/solidago_rigida","iNatAg/solidago_sempervirens","iNatAg/solidago_virgaurea","iNatAg/sonchus_arvensis","iNatAg/sonchus_oleraceus","iNatAg/sonchus_palustris","iNatAg/sonneratia_apetala","iNatAg/sonneratia_caseolaris","iNatAg/sorbus_aucuparia","iNatAg/sorbus_domestica","iNatAg/sorghum_bicolor","iNatAg/sorghum_drummondii","iNatAg/sorghum_halepense","iNatAg/soymida_febrifuga","iNatAg/sparganium_americanum","iNatAg/sparganium_erectum","iNatAg/spartina_pectinata","iNatAg/spartium_junceum","iNatAg/spathodea_campanulata","iNatAg/spergula_arvensis","iNatAg/spermacoce_verticillata","iNatAg/spinacia_oleracea","iNatAg/spinifex_hirsutus","iNatAg/spirea_tomentosa","iNatAg/spodiopogon_sibiricus","iNatAg/spondias_cythera","iNatAg/spondias_mombin","iNatAg/spondias_purpurea","iNatAg/sporobolus_airoides","iNatAg/sporobolus_fimbriatus","iNatAg/sporobolus_maritimus","iNatAg/sporobolus_neglectus","iNatAg/sporobolus_spicatus","iNatAg/sporobolus_virginicus","iNatAg/stachys_affinis","iNatAg/stachys_palustris","iNatAg/stachytarpheta_incana","iNatAg/stachytarpheta_indica","iNatAg/stellaria_graminea","iNatAg/stellaria_holostea","iNatAg/stellaria_media","iNatAg/stenotaphrum_secundatum","iNatAg/sterculia_foetida","iNatAg/sterculia_urens","iNatAg/sterculia_villosa","iNatAg/stereospermum_kunthianum","iNatAg/stevia_rebaudiana","iNatAg/stipa_baicalensis","iNatAg/stipa_brachychaeta","iNatAg/stipa_capillata","iNatAg/stipa_glareosa","iNatAg/stipa_grandis","iNatAg/stipa_krylovii","iNatAg/stipa_lagascae","iNatAg/stipa_occidentalis","iNatAg/stipa_parviflora","iNatAg/stipa_tenacissima","iNatAg/stipa_trichotoma","iNatAg/stipagrostis_amabilis","iNatAg/stipagrostis_zeyheri","iNatAg/stratiotes_aloides","iNatAg/strychnos_cocculoides","iNatAg/strychnos_innocua","iNatAg/strychnos_spinosa","iNatAg/stylidium_desertorum","iNatAg/stylosanthes_capitata","iNatAg/stylosanthes_fruticosa","iNatAg/stylosanthes_hamata","iNatAg/stylosanthes_humilis","iNatAg/stylosanthes_scabra","iNatAg/stylosanthes_viscosa","iNatAg/succisa_pratensis","iNatAg/swertia_baicalensis","iNatAg/swietenia_macrophylla","iNatAg/swietenia_mahogani","iNatAg/symphoricarpos_mollis","iNatAg/symphoricarpos_occidentalis","iNatAg/symphoricarpos_orbiculatus","iNatAg/symphoricarpos_rotundifolius","iNatAg/symphytum_officinale","iNatAg/syncarpia_glomulifera","iNatAg/syncarpia_hillii","iNatAg/syzygium_cordatum","iNatAg/syzygium_cumini","iNatAg/syzygium_guineense","iNatAg/syzygium_malaccense","iNatAg/syzygium_taiwanicum","iNatAg/tabebuia_rosea","iNatAg/tabebuia_serratifolia","iNatAg/tagetes_minuta","iNatAg/talinum_triangulare","iNatAg/tamarindus_indica","iNatAg/tamarix_aphylla","iNatAg/tamarix_chinensis","iNatAg/tamarix_gallica","iNatAg/tamarix_parviflora","iNatAg/tanacetum_balsamita","iNatAg/tanacetum_vulgare","iNatAg/taraxacum_officinale","iNatAg/taraxia_breviflora","iNatAg/tarchonanthus_camphoratus","iNatAg/taxodium_distichum","iNatAg/taxus_baccata","iNatAg/tecoma_stans","iNatAg/tectona_grandis","iNatAg/tephrosia_candida","iNatAg/tephrosia_lupinifolia","iNatAg/tephrosia_obovata","iNatAg/tephrosia_purpurea","iNatAg/tephrosia_vogelii","iNatAg/teramnus_labialis","iNatAg/terminalia_arjuna","iNatAg/terminalia_bellirica","iNatAg/terminalia_brownii","iNatAg/terminalia_calamansanai","iNatAg/terminalia_catappa","iNatAg/terminalia_chebula","iNatAg/terminalia_ivorensis","iNatAg/terminalia_mantaly","iNatAg/terminalia_myriocarpa","iNatAg/terminalia_paniculata","iNatAg/terminalia_prunioides","iNatAg/terminalia_sericocarpa","iNatAg/terminalia_tomentosa","iNatAg/tetradymia_canescens","iNatAg/tetragonia_tetragonioides","iNatAg/teucrium_botrys","iNatAg/teucrium_canadense","iNatAg/teucrium_chamaedrys","iNatAg/teucrium_polium","iNatAg/thalia_geniculata","iNatAg/thalictrum_pubescens","iNatAg/thaumatococcus_daniellii","iNatAg/themeda_australis","iNatAg/themeda_quadrivalvis","iNatAg/themeda_triandra","iNatAg/theobroma_bicolor","iNatAg/theobroma_cacao","iNatAg/theobroma_grandiflorum","iNatAg/thermopsis_montana","iNatAg/thermopsis_rhombifolia","iNatAg/thespesia_populnea","iNatAg/thlaspi_arvense","iNatAg/thlaspi_perfoliatum","iNatAg/thuja_occidentalis","iNatAg/thymus_serphyllum","iNatAg/thymus_serpyllum","iNatAg/thymus_vulgaris","iNatAg/thyrsostachys_siamensis","iNatAg/thysanolaena_latifolia","iNatAg/tilia_cordata","iNatAg/tilia_platyphyllos","iNatAg/tipuana_tipu","iNatAg/tithonia_diversifolia","iNatAg/toona_ciliata","iNatAg/torenia_glabra","iNatAg/toxicodendron_pubescens","iNatAg/trachypogon_spicatus","iNatAg/tradescantia_bracteata","iNatAg/tradescantia_fluminensis","iNatAg/tradescantia_ohiensis","iNatAg/tradescantia_virginiana","iNatAg/tragia_betonicifolia","iNatAg/tragopogon_lamottei","iNatAg/tragopogon_porrifolius","iNatAg/tragopogon_pratensis","iNatAg/tragus_koelerioides","iNatAg/trapa_natans","iNatAg/trema_orientale","iNatAg/trianthema_portulacastrum","iNatAg/tribulus_cistoides","iNatAg/tribulus_terrestris","iNatAg/trichanthera_gigantea","iNatAg/trichoneura_grandiglumis","iNatAg/trichosanthes_cucumerina","iNatAg/trichostema_lanceolatum","iNatAg/tridax_procumbens","iNatAg/trifolium_africanum","iNatAg/trifolium_alexandrinum","iNatAg/trifolium_ambiguum","iNatAg/trifolium_angustifolium","iNatAg/trifolium_arvense","iNatAg/trifolium_burchellianum","iNatAg/trifolium_campestre","iNatAg/trifolium_carolinianum","iNatAg/trifolium_cherleri","iNatAg/trifolium_dubium","iNatAg/trifolium_fragiferum","iNatAg/trifolium_glanduliferum","iNatAg/trifolium_glomeratum","iNatAg/trifolium_hirtum","iNatAg/trifolium_hybridum","iNatAg/trifolium_incarnatum","iNatAg/trifolium_medium","iNatAg/trifolium_michelianum","iNatAg/trifolium_nigrescens","iNatAg/trifolium_patens","iNatAg/trifolium_pilulare","iNatAg/trifolium_polymorphum","iNatAg/trifolium_pratense","iNatAg/trifolium_reflexum","iNatAg/trifolium_repens","iNatAg/trifolium_resupinatum","iNatAg/trifolium_subterraneum","iNatAg/trifolium_tomentosum","iNatAg/trifolium_variegatum","iNatAg/trifolium_vesiculosum","iNatAg/trifolium_wormskioldii","iNatAg/triglochin_maritima","iNatAg/triglochin_maritimum","iNatAg/triglochin_palustre","iNatAg/trigonella_foenum-graecum","iNatAg/tripsacum_dactyloides","iNatAg/trisetum_flavescens","iNatAg/tristachya_leucothrix","iNatAg/triticum_aestivum","iNatAg/triticum_dicoccoides","iNatAg/triticum_durum","iNatAg/triticum_spelta","iNatAg/triumfetta_rhomboidea","iNatAg/triumfetta_semitriloba","iNatAg/trollius_europaeus","iNatAg/tropaeolum_majus","iNatAg/tropaeolum_tuberosum","iNatAg/tropidocarpum_gracile","iNatAg/turritis_glabra","iNatAg/tussilago_farfara","iNatAg/tylosema_esculentum","iNatAg/typha_angustifolia","iNatAg/typha_domingensis","iNatAg/typha_latifolia","iNatAg/uapaca_kirkiana","iNatAg/ulex_europaeus","iNatAg/ullucus_tuberosus","iNatAg/ulmus_procera","iNatAg/umbilicus_rupestris","iNatAg/uncaria_gambir","iNatAg/urelytrum_agropyroides","iNatAg/urena_lobata","iNatAg/urochloa_mosambicensis","iNatAg/urochloa_panicoides","iNatAg/urtica_chamaedryoides","iNatAg/urtica_dioica","iNatAg/urtica_urens","iNatAg/utricularia_floridana","iNatAg/utricularia_foliosa","iNatAg/utricularia_gibba","iNatAg/utricularia_purpurea","iNatAg/utricularia_radiata","iNatAg/utricularia_vulgaris","iNatAg/uvaria_littoralis","iNatAg/uvularia_sessilifolia","iNatAg/vaccinium_angustifolium","iNatAg/vaccinium_corymbosum","iNatAg/vaccinium_macrocarpon","iNatAg/vaccinium_myrtillus","iNatAg/vaccinium_uliginosum","iNatAg/vaccinium_vitis-idaea","iNatAg/valeriana_officinalis","iNatAg/valerianella_eriocarpa","iNatAg/vallisneria_americana","iNatAg/vangueria_infausta","iNatAg/vangueria_madagascariensis","iNatAg/vanilla_planifolia","iNatAg/vateria_indica","iNatAg/ventilago_viminalis","iNatAg/veratrum_album","iNatAg/veratrum_californicum","iNatAg/verbascum_blattaria","iNatAg/verbascum_lychnitis","iNatAg/verbascum_phlomoides","iNatAg/verbascum_thapsus","iNatAg/verbascum_thaspus","iNatAg/verbena_bonariensis","iNatAg/verbena_brasiliensis","iNatAg/verbena_hastata","iNatAg/verbena_officinalis","iNatAg/verbena_urticifolia","iNatAg/vernonia_altissima","iNatAg/vernonia_amygdalina","iNatAg/vernonia_baldwinii","iNatAg/vernonia_chamaedrys","iNatAg/vernonia_fasciculata","iNatAg/veronica_agrestis","iNatAg/veronica_anagallis-aquatica","iNatAg/veronica_arvensis","iNatAg/veronica_biloba","iNatAg/veronica_chamaedrys","iNatAg/veronica_filiformis","iNatAg/veronica_hederaefolia","iNatAg/veronica_hederifolia","iNatAg/veronica_longifolia","iNatAg/veronica_officinalis","iNatAg/veronica_peregrina","iNatAg/veronica_polita","iNatAg/veronica_serpyllifolia","iNatAg/vetiveria_zizanioides","iNatAg/viburnum_cassinoides","iNatAg/viburnum_lentago","iNatAg/viburnum_prunifolium","iNatAg/viccia_cracca","iNatAg/vicia_augustifolia","iNatAg/vicia_benghalensis","iNatAg/vicia_cracca","iNatAg/vicia_ervilia","iNatAg/vicia_faba","iNatAg/vicia_monantha","iNatAg/vicia_narbonensis","iNatAg/vicia_pannonica","iNatAg/vicia_sativa","iNatAg/vicia_sepium","iNatAg/vigna_adenantha","iNatAg/vigna_angularis","iNatAg/vigna_hosei","iNatAg/vigna_lanceolata","iNatAg/vigna_longifolia","iNatAg/vigna_luteola","iNatAg/vigna_parkeri","iNatAg/vigna_radiata","iNatAg/vigna_trilobata","iNatAg/vigna_umbellata","iNatAg/vigna_unguiculata","iNatAg/vigna_vexillata","iNatAg/vinca_major","iNatAg/vinca_minor","iNatAg/viola_lanceolata","iNatAg/viola_odorata","iNatAg/viola_tricolor","iNatAg/viscum_album","iNatAg/vitellaria_paradoxa","iNatAg/vitex_agnus-castus","iNatAg/vitex_doniana","iNatAg/vitex_negundo","iNatAg/vitis_labrusca","iNatAg/vitis_rotundifolia","iNatAg/vitis_vinifera","iNatAg/vitis_vulpina","iNatAg/waltheria_indica","iNatAg/warburgia_salutaris","iNatAg/warburgia_ugandensis","iNatAg/withania_somnifera","iNatAg/wrightia_tomentosa","iNatAg/xanthium_spinosum","iNatAg/xanthium_strumarium","iNatAg/xanthosoma_sagittifolium","iNatAg/ximenia_americana","iNatAg/xylia_xylocarpa","iNatAg/xylocarpus_granatum","iNatAg/xylocarpus_mekongensis","iNatAg/xylocarpus_moluccensis","iNatAg/xylorhiza_glabriuscula","iNatAg/yucca_elephantipes","iNatAg/zannichellia_palustris","iNatAg/zanthoxylum_americanum","iNatAg/zea_mays","iNatAg/zingiber_officinale","iNatAg/zizania_aquatica","iNatAg/zizania_latifolia","iNatAg/ziziphus_abyssinica","iNatAg/ziziphus_mauritiana","iNatAg/ziziphus_mucronata","iNatAg/zornia_diphylla","iNatAg/zornia_glochidiata","iNatAg/zornia_latifolia","iNatAg/zostera_marina","iNatAg/zoysia_matrella","iNatAg/zygophyllum_fabago","java_plum_leaf_disease_classification","jujube_bruise_classification","jute_disease_classification","leaf_counting_denmark","lemon_leaf_disease_classification","lemongrass_disease_classification","lentil_disease_classification","LSID_bean_segmentation","maize_disease_classification","maize_tomato_weed_classification","maize_weed_detection","malabar_spinach_disease_classification","mandarin_leaf_variety_classification","mangifera2012_variety_classification","mango_detection_australia","mango_growth_detection","mango_leaf_disease_classification","MangoClassify-12_variety_classification","MangoImageBD_classification","MangoLeafBD_disease_classification","Medjool_date_ripeness_detection","MedLeafX_disease_classification","merlot_mildew_segmentation","MH_SoyaHealthVision_disease_classification_leaf","MH_SoyaHealthVision_disease_classification_uav","MH_Weed16_weed_detection","MH_Weed16_weed_variety_classification","mint_leaf_classification","money_plant_disease_classification","MoringaLeafNet_disease_classification","mulberry_leaf_variety_classification","oil_palm_fruit_ripeness_classification","okra_maturity_classification","okra_thermal_maturity_classification","OkraDiseaseNet_disease_classification","olive_tree_crown_detection","onionfoliageset_detection","orange_leaf_disease_classification","oyster_mushroom_maturity_detection","paddy_disease_classification","PaddyVarietyBD_variety_classification","papaya_leaf_disease_classification","papaya_leaf_disease_classification_bangladesh","papaya_leaf_disease_detection","peachpear_flower_segmentation","PFSD_Musa_banana_disease_classification","PFSD_Musa_banana_variety_classification","plant_doc_classification","plant_doc_detection","plant_leaf_disease_classification","plant_seedlings_aarhus","plant_village_classification","plum_leaf_fruit_disease_classification","pomegranate_disease_classification","pomegranate_disease_classification_india","pomegranate_growth_detection","pomegranate_quality_classification","pomegranate_thermal_defect_classification","potato_leaf_blight_classification","potato_leaf_disease_classification","PriBel_betel_leaf_disease_classification","QuinceSet_detection","radish_leaf_disease_classification","rangeland_weeds_australia","red_grapes_and_leaves_segmentation","REMP_plant_classification","rice_disease_classification_bangladesh","rice_field_weed_classification","rice_grain_variety_classification","rice_leaf_disease_classification","rice_leaf_disease_classification_india","rice_panicle_detection","rice_seedling_classification","rice_seedling_segmentation","rice_variety_classification_bangladesh","riseholme_strawberry_classification_2021","RoCoLe_disease_detection","RoseLeafInsight_disease_classification","RoseNet_leaf_disease_classification","SapBark_64_variety_classification","sapota_fruit_size_classification","seasveg_classification_bd","SIMPDV1_plant_classification","sorghum_weed_classification","sorghum_weed_segmentation","soybean_damage_classification","soybean_harvest_damage_segmentation","soybean_insect_classification","soybean_leaf_disease_classification","soybean_leaf_disease_classification_brazil","soybean_variety_classification","soybean_weed_uav_brazil","SoyNet_leaf_health_classification","strawberry_detection_2022","strawberry_detection_2023","strawberry_growth_detection","Strawberry-DS_strawberry_detection","sugarbeet_weed_segmentation","sugarcane_damage_usa","sugarcane_leaf_disease_classification","sunflower_detection","sunflower_disease_classification","susnato_plant_disease_detection_processed","synthetic_cowpea_flower_detection","synthetic_cowpea_pod_detection","taro_blight_stage_classification","tea_leaf_disease_classification","tea_leaf_disease_classification_bangladesh","TealeafAgeQuality_detection","teaLeafBD_disease_classification_classification","three_plant_leaf_disease_classification","three_season_weed_detection","TOM2024_disease_classification","tomato_factory_detection","tomato_leaf_disease","tomato_maturity_classification","tomato_quality_classification","tomato_ripeness_detection","tropical_flower_variety_classification","turmeric_disease_classification","turmeric_leaf_disease_classification","vegann_multicrop_presence_segmentation","vegetable_classification_bangladesh_classification","vegetable_crop_early_detection","VegNet_cauliflower_disease_classification","VegNet_quality_classification","vine_virus_photo_dataset","vineyard_grape_segmentation","vineyard_pruning_segmentation","WaterHyacinth_variety_classification","watermelon_disease_classification","weed_crop_detection","wGrapeUNIPD-DL_white_grape_bunch_detection","wheat_head_counting","white_grapes_and_leaves_segmentation","WisWheat"]}
\ No newline at end of file
diff --git a/static/data/embeddings/vectors.bin b/static/data/embeddings/vectors.bin
index 0d3cfe9..0d6be77 100644
Binary files a/static/data/embeddings/vectors.bin and b/static/data/embeddings/vectors.bin differ
diff --git a/static/data/hf_datasets.json b/static/data/hf_datasets.json
index e816b9e..26fa999 100644
--- a/static/data/hf_datasets.json
+++ b/static/data/hf_datasets.json
@@ -2,7 +2,7 @@
   {
     "name": "wGrapeUNIPD-DL_white_grape_bunch_detection",
     "machine_learning_task": "object_detection",
-    "agricultural_task": "fruit_detection",
+    "agricultural_task": "crop_detection",
     "location": [
       "Italy"
     ],
@@ -124,7 +124,7 @@
     "machine_learning_task": "object_detection",
     "agricultural_task": "crop_detection",
     "environment": "field",
-    "location": "California",
+    "location": "United States of America",
     "real_or_synthetic": "real",
     "crop_types": [
       "cowpea"
@@ -151,7 +151,7 @@
     "name": "GEMINI_cowpea_flower_detection",
     "machine_learning_task": "object_detection",
     "agricultural_task": "crop_detection",
-    "location": "California",
+    "location": "United States of America",
     "environment": "field",
     "real_or_synthetic": "real",
     "crop_types": [
@@ -2922,7 +2922,7 @@
     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/cotton_weed_detection",
     "examples_image_url": "/img/agml/sample_images/cotton_weed_detection_sample.webp",
-    "country": "United States",
+    "country": "United States of America",
     "imaging_equipment": [
       "Autel Robotics EVO II Dual 640T V3, 50 MP 0.8″ RYYB CMOS sensor (XL709 camera module)"
     ],
@@ -3700,7 +3700,7 @@
     "name": "banana_leaf_disease_classification",
     "machine_learning_task": "image_classification",
     "agricultural_task": "disease_classification",
-    "location": "Ethiopia, Africa",
+    "location": "Ethiopia",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -3733,7 +3733,7 @@
     "name": "bean_disease_uganda",
     "machine_learning_task": "image_classification",
     "agricultural_task": "disease_classification",
-    "location": "Uganda, Africa",
+    "location": "Uganda",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "platform": "handheld",
@@ -4017,7 +4017,7 @@
     "name": "corn_maize_leaf_disease",
     "machine_learning_task": "image_classification",
     "agricultural_task": "disease_classification",
-    "location": "Worldwide",
+    "location": null,
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "field",
@@ -4091,7 +4091,7 @@
     "name": "java_plum_leaf_disease_classification",
     "machine_learning_task": "image_classification",
     "agricultural_task": "disease_classification",
-    "location": "Bangladesh, Asia",
+    "location": "Bangladesh",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "lab",
@@ -4218,7 +4218,7 @@
     "name": "paddy_disease_classification",
     "machine_learning_task": "image_classification",
     "agricultural_task": "disease_classification",
-    "location": "India, Asia",
+    "location": "India",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -4250,8 +4250,8 @@
   {
     "name": "almond_bloom_2023",
     "machine_learning_task": "object_detection",
-    "agricultural_task": "flower_detection",
-    "location": "United States, North America",
+    "agricultural_task": "crop_detection",
+    "location": "United States of America",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -4283,8 +4283,8 @@
   {
     "name": "almond_harvest_2021",
     "machine_learning_task": "object_detection",
-    "agricultural_task": "fruit_detection",
-    "location": "United States, North America",
+    "agricultural_task": "crop_detection",
+    "location": "United States of America",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -4316,8 +4316,8 @@
   {
     "name": "apple_detection_drone_brazil",
     "machine_learning_task": "object_detection",
-    "agricultural_task": "fruit_detection",
-    "location": "Brazil, South America",
+    "agricultural_task": "crop_detection",
+    "location": "Brazil",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -4349,8 +4349,8 @@
   {
     "name": "apple_detection_spain",
     "machine_learning_task": "object_detection",
-    "agricultural_task": "fruit_detection",
-    "location": "Spain, Europe",
+    "agricultural_task": "crop_detection",
+    "location": "Spain",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -4382,8 +4382,8 @@
   {
     "name": "apple_detection_usa",
     "machine_learning_task": "object_detection",
-    "agricultural_task": "fruit_detection",
-    "location": "United States, North America",
+    "agricultural_task": "crop_detection",
+    "location": "United States of America",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -4415,8 +4415,8 @@
   {
     "name": "embrapa_wgisd_grape_detection",
     "machine_learning_task": "object_detection",
-    "agricultural_task": "fruit_detection",
-    "location": "Worldwide",
+    "agricultural_task": "crop_detection",
+    "location": null,
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "platform": "handheld/ground",
@@ -4446,8 +4446,8 @@
   {
     "name": "fruit_detection_worldwide",
     "machine_learning_task": "object_detection",
-    "agricultural_task": "fruit_detection",
-    "location": "Worldwide",
+    "agricultural_task": "crop_detection",
+    "location": null,
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "platform": "mixed",
@@ -4484,8 +4484,8 @@
   {
     "name": "gemini_flower_detection",
     "machine_learning_task": "object_detection",
-    "agricultural_task": "flower_detection",
-    "location": "United States, North America",
+    "agricultural_task": "crop_detection",
+    "location": "United States of America",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -4518,7 +4518,7 @@
     "name": "gemini_leaf_detection",
     "machine_learning_task": "object_detection",
     "agricultural_task": "crop_detection",
-    "location": "United States, North America",
+    "location": "United States of America",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -4551,7 +4551,7 @@
     "name": "gemini_plant_detection",
     "machine_learning_task": "object_detection",
     "agricultural_task": "crop_detection",
-    "location": "United States, North America",
+    "location": "United States of America",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -4584,7 +4584,7 @@
     "name": "gemini_pod_detection",
     "machine_learning_task": "object_detection",
     "agricultural_task": "crop_detection",
-    "location": "United States, North America",
+    "location": "United States of America",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -4617,7 +4617,7 @@
     "name": "ghai_broccoli_detection",
     "machine_learning_task": "object_detection",
     "agricultural_task": "crop_detection",
-    "location": "USA",
+    "location": "United States of America",
     "environment": "field",
     "real_or_synthetic": "real",
     "crop_types": [
@@ -4643,7 +4643,7 @@
     "name": "ghai_green_cabbage_detection",
     "machine_learning_task": "object_detection",
     "agricultural_task": "crop_detection",
-    "location": "USA",
+    "location": "United States of America",
     "environment": "field",
     "real_or_synthetic": "real",
     "crop_types": [
@@ -4668,7 +4668,7 @@
     "name": "ghai_iceberg_lettuce_detection",
     "machine_learning_task": "object_detection",
     "agricultural_task": "crop_detection",
-    "location": "USA",
+    "location": "United States of America",
     "environment": "field",
     "real_or_synthetic": "real",
     "crop_types": [
@@ -4693,7 +4693,7 @@
     "name": "ghai_romaine_detection",
     "machine_learning_task": "object_detection",
     "agricultural_task": "crop_detection",
-    "location": "USA",
+    "location": "United States of America",
     "environment": "field",
     "real_or_synthetic": "real",
     "crop_types": [
@@ -4718,7 +4718,7 @@
     "name": "ghai_strawberry_fruit_detection",
     "machine_learning_task": "object_detection",
     "agricultural_task": "crop_detection",
-    "location": "USA",
+    "location": "United States of America",
     "environment": "field",
     "real_or_synthetic": "real",
     "crop_types": [
@@ -4751,8 +4751,8 @@
   {
     "name": "grape_detection_californiaday",
     "machine_learning_task": "object_detection",
-    "agricultural_task": "fruit_detection",
-    "location": "United States, North America",
+    "agricultural_task": "crop_detection",
+    "location": "United States of America",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -4784,8 +4784,8 @@
   {
     "name": "grape_detection_californianight",
     "machine_learning_task": "object_detection",
-    "agricultural_task": "fruit_detection",
-    "location": "United States, North America",
+    "agricultural_task": "crop_detection",
+    "location": "United States of America",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -4817,8 +4817,8 @@
   {
     "name": "grape_detection_syntheticday",
     "machine_learning_task": "object_detection",
-    "agricultural_task": "fruit_detection",
-    "location": "United States, North America",
+    "agricultural_task": "crop_detection",
+    "location": "United States of America",
     "sensor_modality": "RGB",
     "real_or_synthetic": "synthetic",
     "environment": "field",
@@ -4850,8 +4850,8 @@
   {
     "name": "mango_detection_australia",
     "machine_learning_task": "object_detection",
-    "agricultural_task": "fruit_detection",
-    "location": "Australia, Oceania",
+    "agricultural_task": "crop_detection",
+    "location": "Australia",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -4884,7 +4884,7 @@
     "name": "plant_doc_detection",
     "machine_learning_task": "object_detection",
     "agricultural_task": "disease_classification",
-    "location": "Worldwide",
+    "location": null,
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -4915,7 +4915,7 @@
     "name": "strawberry_detection_2022",
     "machine_learning_task": "object_detection",
     "agricultural_task": "crop_detection",
-    "location": "Worldwide",
+    "location": null,
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -4948,7 +4948,7 @@
     "name": "strawberry_detection_2023",
     "machine_learning_task": "object_detection",
     "agricultural_task": "crop_detection",
-    "location": "Worldwide",
+    "location": null,
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -4981,7 +4981,7 @@
     "name": "tomato_ripeness_detection",
     "machine_learning_task": "object_detection",
     "agricultural_task": "maturity_detection",
-    "location": "Worldwide",
+    "location": null,
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -5014,7 +5014,7 @@
     "name": "wheat_head_counting",
     "machine_learning_task": "object_detection",
     "agricultural_task": "crop_detection",
-    "location": "Worldwide",
+    "location": null,
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -5047,7 +5047,7 @@
     "name": "crop_weeds_greece",
     "machine_learning_task": "image_classification",
     "agricultural_task": "weed_classification",
-    "location": "Greece, Europe",
+    "location": "Greece",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -5081,7 +5081,7 @@
     "name": "plant_seedlings_aarhus",
     "machine_learning_task": "image_classification",
     "agricultural_task": "weed_classification",
-    "location": "Denmark, Europe",
+    "location": "Denmark",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -5115,7 +5115,7 @@
     "name": "soybean_weed_uav_brazil",
     "machine_learning_task": "image_classification",
     "agricultural_task": "weed_classification",
-    "location": "Brazil, South America",
+    "location": "Brazil",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Lab",
@@ -5148,7 +5148,7 @@
     "name": "sugarcane_damage_usa",
     "machine_learning_task": "image_classification",
     "agricultural_task": "damage_classification",
-    "location": "United States, North America",
+    "location": "United States of America",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Lab",
@@ -5181,7 +5181,7 @@
     "name": "guava_disease_pakistan",
     "machine_learning_task": "image_classification",
     "agricultural_task": "disease_classification",
-    "location": "Pakistan, Asia",
+    "location": "Pakistan",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -5214,7 +5214,7 @@
     "name": "plant_doc_classification",
     "machine_learning_task": "image_classification",
     "agricultural_task": "disease_classification",
-    "location": "Worldwide",
+    "location": null,
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -5258,7 +5258,7 @@
     "name": "plant_village_classification",
     "machine_learning_task": "image_classification",
     "agricultural_task": "disease_classification",
-    "location": "United States, North America",
+    "location": "United States of America",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Lab",
@@ -5304,7 +5304,7 @@
     "name": "rangeland_weeds_australia",
     "machine_learning_task": "image_classification",
     "agricultural_task": "weed_classification",
-    "location": "Australia, Oceania",
+    "location": "Australia",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -5335,7 +5335,7 @@
     "name": "rice_leaf_disease_classification",
     "machine_learning_task": "image_classification",
     "agricultural_task": "disease_classification",
-    "location": "Worldwide",
+    "location": null,
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -5401,7 +5401,7 @@
     "name": "tomato_leaf_disease",
     "machine_learning_task": "image_classification",
     "agricultural_task": "disease_classification",
-    "location": "Worldwide",
+    "location": null,
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Lab",
@@ -5434,7 +5434,7 @@
     "name": "vine_virus_photo_dataset",
     "machine_learning_task": "image_classification",
     "agricultural_task": "disease_classification",
-    "location": "Worldwide",
+    "location": null,
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -5508,7 +5508,7 @@
     "name": "betel_leaf_disease_classification",
     "machine_learning_task": "image_classification",
     "agricultural_task": "disease_classification",
-    "location": "Bangladesh, Asia",
+    "location": "Bangladesh",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "field",
@@ -5541,7 +5541,7 @@
     "name": "tea_leaf_disease_classification",
     "machine_learning_task": "image_classification",
     "agricultural_task": "disease_classification",
-    "location": "India, Asia",
+    "location": "India",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5572,8 +5572,8 @@
   {
     "name": "soybean_insect_classification",
     "machine_learning_task": "image_classification",
-    "agricultural_task": "pest_classification",
-    "location": "Brazil, South America",
+    "agricultural_task": "damage_classification",
+    "location": "Brazil",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "platform": "uav",
@@ -5645,7 +5645,7 @@
     "name": "carrot_weeds_germany",
     "machine_learning_task": "semantic_segmentation",
     "agricultural_task": "weed_segmentation",
-    "location": "Germany, Europe",
+    "location": "Germany",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -5677,8 +5677,8 @@
   {
     "name": "apple_flower_segmentation",
     "machine_learning_task": "semantic_segmentation",
-    "agricultural_task": "flower_segmentation",
-    "location": "United States, North America",
+    "agricultural_task": "crop_segmentation",
+    "location": "United States of America",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -5711,7 +5711,7 @@
     "name": "apple_segmentation_minnesota",
     "machine_learning_task": "semantic_segmentation",
     "agricultural_task": "crop_segmentation",
-    "location": "United States, North America",
+    "location": "United States of America",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -5744,7 +5744,7 @@
     "name": "rice_seedling_segmentation",
     "machine_learning_task": "semantic_segmentation",
     "agricultural_task": "weed_segmentation",
-    "location": "China, Asia",
+    "location": "China",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -5777,7 +5777,7 @@
     "name": "sugarbeet_weed_segmentation",
     "machine_learning_task": "semantic_segmentation",
     "agricultural_task": "weed_segmentation",
-    "location": "None, None",
+    "location": null,
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -5835,7 +5835,7 @@
     "name": "growliflower_cauliflower_segmentation",
     "machine_learning_task": "semantic_segmentation",
     "agricultural_task": "crop_segmentation",
-    "location": "Worldwide",
+    "location": null,
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -5859,8 +5859,8 @@
   {
     "name": "peachpear_flower_segmentation",
     "machine_learning_task": "semantic_segmentation",
-    "agricultural_task": "flower_segmentation",
-    "location": "Worldwide",
+    "agricultural_task": "crop_segmentation",
+    "location": null,
     "sensor_modality": null,
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -5893,8 +5893,8 @@
   {
     "name": "red_grapes_and_leaves_segmentation",
     "machine_learning_task": "semantic_segmentation",
-    "agricultural_task": "vineyard_scene_segmentation",
-    "location": "Greece, Europe",
+    "agricultural_task": "crop_segmentation",
+    "location": "Greece",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "environment": "Field",
@@ -5927,7 +5927,7 @@
     "name": "vegann_multicrop_presence_segmentation",
     "machine_learning_task": "semantic_segmentation",
     "agricultural_task": "crop_segmentation",
-    "location": "Worldwide",
+    "location": null,
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "platform": "aerial",
@@ -5956,8 +5956,8 @@
   {
     "name": "white_grapes_and_leaves_segmentation",
     "machine_learning_task": "semantic_segmentation",
-    "agricultural_task": "vineyard_scene_segmentation",
-    "location": "Greece, Europe",
+    "agricultural_task": "crop_segmentation",
+    "location": "Greece",
     "sensor_modality": "RGB",
     "real_or_synthetic": "real",
     "platform": "handheld/ground",
@@ -6846,7 +6846,7 @@
     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/weed_crop_detection",
     "examples_image_url": "/img/agml/sample_images/weed_crop_detection_sample.webp",
-    "country": "USA",
+    "country": "United States of America",
     "imaging_equipment": [
       "Canon EOS 90D mounted on ground robotic platforms"
     ],
@@ -8955,7 +8955,11 @@
     "source": "huggingface",
     "hf_link": "https://huggingface.co/datasets/Project-AgML/fruitseg30_segmentation",
     "examples_image_url": "/img/agml/sample_images/fruitseg30_segmentation_sample.webp",
-    "country": "Malaysia, Bangladesh, Australia",
+    "country": [
+      "Malaysia",
+      "Bangladesh",
+      "Australia"
+    ],
     "imaging_equipment": [
       "Smartphones"
     ],
@@ -11148,7 +11152,7 @@
       "Scene Reasoning"
     ],
     "num_task_types": 13,
-    "location": "multinational",
+    "location": null,
     "environment": "mixed",
     "crop_types": [
       "maize",
@@ -11223,7 +11227,7 @@
       "Traditional Management"
     ],
     "num_task_types": 7,
-    "location": "multinational",
+    "location": null,
     "environment": "mixed",
     "crop_types": [
       "tomato",
@@ -11296,7 +11300,7 @@
       "Environmental Management"
     ],
     "num_task_types": 15,
-    "location": "multinational",
+    "location": null,
     "environment": "mixed",
     "crop_types": [
       "mixed_multiple_sources"
@@ -11431,7 +11435,7 @@
       "Symptom Identification"
     ],
     "num_task_types": 2,
-    "location": "multinational",
+    "location": null,
     "environment": "mixed",
     "crop_types": [
       "tomato",
@@ -11507,7 +11511,7 @@
       "Disease Knowledge QA"
     ],
     "num_task_types": 2,
-    "location": "unspecified",
+    "location": null,
     "environment": "mixed",
     "crop_types": [
       "tomato",
diff --git a/static/data/performance/global.json b/static/data/performance/global.json
index 45f84f6..e3eedf1 100644
--- a/static/data/performance/global.json
+++ b/static/data/performance/global.json
@@ -23,7 +23,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 27030 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: AnaHau, AnaSet, AtrCan, AtrLen, AtrLeu, CerAre, CheAlb, CorMon, GirOpp, HalAmm, HalBel, HalPer, HalStr, HalSub, HamSal, KocSco, KocSte, SalAba, SalDen, SalInc, SalKal, SalKer, SalPra, SalTom, SalTur, SalYaz, SeiCin, SeiRos, SuaAcu, SuaAeg. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: AnaHau, AnaSet, AtrCan, AtrLen, AtrLeu, CerAre, CheAlb, CorMon, GirOpp, HalAmm, HalBel, HalPer, HalStr, HalSub, HamSal, KocSco, KocSte, SalAba, SalDen, SalInc, SalKal, SalKer, SalPra, SalTom, SalTur, SalYaz, SeiCin, SeiRos, SuaAcu, SuaAeg), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -49,7 +50,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 27030 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: AnaHau, AnaSet, AtrCan, AtrLen, AtrLeu, CerAre, CheAlb, CorMon, GirOpp, HalAmm, HalBel, HalPer, HalStr, HalSub, HamSal, KocSco, KocSte, SalAba, SalDen, SalInc, SalKal, SalKer, SalPra, SalTom, SalTur, SalYaz, SeiCin, SeiRos, SuaAcu, SuaAeg. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: AnaHau, AnaSet, AtrCan, AtrLen, AtrLeu, CerAre, CheAlb, CorMon, GirOpp, HalAmm, HalBel, HalPer, HalStr, HalSub, HamSal, KocSco, KocSte, SalAba, SalDen, SalInc, SalKal, SalKer, SalPra, SalTom, SalTur, SalYaz, SeiCin, SeiRos, SuaAcu, SuaAeg), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -80,7 +82,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3167 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: 1st-grade, 2nd-grade, 3rd-grade. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: 1st-grade, 2nd-grade, 3rd-grade), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -109,7 +112,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5246 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Alternaria_Leaf_Blight, Angular_Leaf_Spot, Anthracnose, Bacterial_Blight, Carica_Insect_Hole, Curled_Yellow_Spot, Downy_Mildew, Dry_Leaf, Early_Alternaria_Leaf_Blight, Fungal_Damage_Leaf, Healthy, Healthy_leaf, Insect_Damage, Iron_Chlorosis_Damage, Mosaic, Mosaic_Virus, Pathogen_symptoms, Spot, White_spot, Xanthomonas_Leaf_Spot, Yellow_Mosaic_Virus, Yellow_Necrotic_Spots_Holes. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Alternaria_Leaf_Blight, Angular_Leaf_Spot, Anthracnose, Bacterial_Blight, Carica_Insect_Hole, Curled_Yellow_Spot, Downy_Mildew, Dry_Leaf, Early_Alternaria_Leaf_Blight, Fungal_Damage_Leaf, Healthy, Healthy_leaf, Insect_Damage, Iron_Chlorosis_Damage, Mosaic, Mosaic_Virus, Pathogen_symptoms, Spot, White_spot, Xanthomonas_Leaf_Spot, Yellow_Mosaic_Virus, Yellow_Necrotic_Spots_Holes), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -138,7 +142,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5246 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Alternaria_Leaf_Blight, Angular_Leaf_Spot, Anthracnose, Bacterial_Blight, Carica_Insect_Hole, Curled_Yellow_Spot, Downy_Mildew, Dry_Leaf, Early_Alternaria_Leaf_Blight, Fungal_Damage_Leaf, Healthy, Healthy_leaf, Insect_Damage, Iron_Chlorosis_Damage, Mosaic, Mosaic_Virus, Pathogen_symptoms, Spot, White_spot, Xanthomonas_Leaf_Spot, Yellow_Mosaic_Virus, Yellow_Necrotic_Spots_Holes. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Alternaria_Leaf_Blight, Angular_Leaf_Spot, Anthracnose, Bacterial_Blight, Carica_Insect_Hole, Curled_Yellow_Spot, Downy_Mildew, Dry_Leaf, Early_Alternaria_Leaf_Blight, Fungal_Damage_Leaf, Healthy, Healthy_leaf, Insect_Damage, Iron_Chlorosis_Damage, Mosaic, Mosaic_Virus, Pathogen_symptoms, Spot, White_spot, Xanthomonas_Leaf_Spot, Yellow_Mosaic_Virus, Yellow_Necrotic_Spots_Holes), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -171,7 +176,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1792 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Datura (Datura stramonium), Dhonya Pata ( Coriandrum Sativum), Kalokeshi, Neem (Azadirachta indica), Pathor Kuchi (Kalanchoe pinnata), Pudina (Mentha Arvensis), Thankuni (Centella asiatica), Tulsi (Ocimum tenuiflorum). Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Datura (Datura stramonium), Dhonya Pata ( Coriandrum Sativum), Kalokeshi, Neem (Azadirachta indica), Pathor Kuchi (Kalanchoe pinnata), Pudina (Mentha Arvensis), Thankuni (Centella asiatica), Tulsi (Ocimum tenuiflorum)), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -204,7 +210,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1792 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Datura (Datura stramonium), Dhonya Pata ( Coriandrum Sativum), Kalokeshi, Neem (Azadirachta indica), Pathor Kuchi (Kalanchoe pinnata), Pudina (Mentha Arvensis), Thankuni (Centella asiatica), Tulsi (Ocimum tenuiflorum). Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Datura (Datura stramonium), Dhonya Pata ( Coriandrum Sativum), Kalokeshi, Neem (Azadirachta indica), Pathor Kuchi (Kalanchoe pinnata), Pudina (Mentha Arvensis), Thankuni (Centella asiatica), Tulsi (Ocimum tenuiflorum)), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -230,7 +237,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 837 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Amrapali, Banana, Chaunsa, Fazli, Haribhanga, Himsagar. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Amrapali, Banana, Chaunsa, Fazli, Haribhanga, Himsagar), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -256,7 +264,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 837 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Amrapali, Banana, Chaunsa, Fazli, Haribhanga, Himsagar. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Amrapali, Banana, Chaunsa, Fazli, Haribhanga, Himsagar), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -291,7 +300,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2048 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: azadirachta-indica, calotropis-gigantea, centella-asiatica, hibiscus-rosa-sinensis, justicia-adhatoda, kalanchoe-pinnata, mikania-micrantha, ocimum-tenuiflorum, phyllanthus-emblica, terminalia-arjuna. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: azadirachta-indica, calotropis-gigantea, centella-asiatica, hibiscus-rosa-sinensis, justicia-adhatoda, kalanchoe-pinnata, mikania-micrantha, ocimum-tenuiflorum, phyllanthus-emblica, terminalia-arjuna), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -319,7 +329,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1007 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Anthracnose, Healthy, Leaf Crinckle, Powdery Mildew, Yellow Mosaic. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anthracnose, Healthy, Leaf Crinckle, Powdery Mildew, Yellow Mosaic), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -347,7 +358,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1007 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Anthracnose, Healthy, Leaf Crinckle, Powdery Mildew, Yellow Mosaic. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anthracnose, Healthy, Leaf Crinckle, Powdery Mildew, Yellow Mosaic), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "moonshotai/Kimi-VL-A3B-Thinking-2506",
@@ -373,7 +385,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 820 images\n\nkimi classification prompt: 'Classify the image into exactly one of the following categories: Green, Overripe, Ripe, Semi-ripe. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Green, Overripe, Ripe, Semi-ripe), and nothing else.'. Generation sampling params (vLLM): {'temperature': 0.6}, max_tokens=10000, batch_size=32. Chat template kwargs: {}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -399,7 +412,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 820 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Green, Overripe, Ripe, Semi-ripe. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Green, Overripe, Ripe, Semi-ripe), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -425,7 +439,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 820 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Green, Overripe, Ripe, Semi-ripe. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Green, Overripe, Ripe, Semi-ripe), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -451,7 +466,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2471 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Bangla Kola, Champa Kola, Sabri Kola, Sagor Kola. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bangla Kola, Champa Kola, Sabri Kola, Sagor Kola), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -477,7 +493,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2471 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Bangla Kola, Champa Kola, Sabri Kola, Sagor Kola. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bangla Kola, Champa Kola, Sabri Kola, Sagor Kola), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -503,7 +520,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 937 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: cordana, healthy, pestalotiopsis, sigatoka. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: cordana, healthy, pestalotiopsis, sigatoka), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -529,7 +547,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 937 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: cordana, healthy, pestalotiopsis, sigatoka. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: cordana, healthy, pestalotiopsis, sigatoka), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -555,7 +574,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3210 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: 1121, 1509, 1637, 1718, 1728, BAS_370, CSR_30, DHBT_3, PB1, PB_6, Unknown. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: 1121, 1509, 1637, 1718, 1728, BAS_370, CSR_30, DHBT_3, PB1, PB_6, Unknown), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -582,7 +602,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1823 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Brinjal Fruit Creaking, Healty Brinjal, Phomopsis Bright, Shoot and Fruit Borer, Wet Rot. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Brinjal Fruit Creaking, Healty Brinjal, Phomopsis Bright, Shoot and Fruit Borer, Wet Rot), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -608,7 +629,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 532 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: cercospora, healthy, mites_and_trips, nutritional, powdery mildew. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: cercospora, healthy, mites_and_trips, nutritional, powdery mildew), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -634,7 +656,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 532 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: cercospora, healthy, mites_and_trips, nutritional, powdery mildew. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: cercospora, healthy, mites_and_trips, nutritional, powdery mildew), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -660,7 +683,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 815 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Iris yellow virus, Stemphylium leaf blight and collectrichum leaf blight, healthy, purple blotch. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Iris yellow virus, Stemphylium leaf blight and collectrichum leaf blight, healthy, purple blotch), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -686,7 +710,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 815 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Iris yellow virus, Stemphylium leaf blight and collectrichum leaf blight, healthy, purple blotch. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Iris yellow virus, Stemphylium leaf blight and collectrichum leaf blight, healthy, purple blotch), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -712,7 +737,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 902 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: boron-B, calcium-Ca, healthy, iron-Fe, magnesium-Mg, manganese-Mn, nitrogen-N, phosphorus-P, potasium-K. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: boron-B, calcium-Ca, healthy, iron-Fe, magnesium-Mg, manganese-Mn, nitrogen-N, phosphorus-P, potasium-K), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -739,7 +765,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1254 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the Cacao. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: Cacao). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -844,7 +871,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 6900 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Aloevera, Amla, Amruthaballi, Arali, Astma_weed, Badipala, Balloon_Vine, Bamboo, Beans, Betel, Bhrami, Bringaraja, Caricature, Castor, Catharanthus, Chakte, Chilly, Citron lime (herelikai), Coffee, Common rue(naagdalli), Coriender, Curry, Doddpathre, Drumstick, Ekka, Eucalyptus, Ganigale, Ganike, Gasagase, Ginger, Globe Amarnath, Guava, Henna, Hibiscus, Honge, Insulin, Jackfruit, Jasmine, Kambajala, Kasambruga, Kohlrabi, Lantana, Lemon, Lemongrass, Malabar_Nut, Malabar_Spinach, Mango, Marigold, Mint, Neem, Nelavembu, Nerale, Nooni, Onion, Padri, Palak(Spinach), Papaya, Parijatha, Pea, Pepper, Pomoegranate, Pumpkin, Raddish, Rose, Sampige, Sapota, Seethaashoka, Seethapala, Spinach1, Tamarind, Taro, Tecoma, Thumbe, Tomato, Tulsi, Turmeric, ashoka, camphor, kamakasturi, kepala. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Aloevera, Amla, Amruthaballi, Arali, Astma_weed, Badipala, Balloon_Vine, Bamboo, Beans, Betel, Bhrami, Bringaraja, Caricature, Castor, Catharanthus, Chakte, Chilly, Citron lime (herelikai), Coffee, Common rue(naagdalli), Coriender, Curry, Doddpathre, Drumstick, Ekka, Eucalyptus, Ganigale, Ganike, Gasagase, Ginger, Globe Amarnath, Guava, Henna, Hibiscus, Honge, Insulin, Jackfruit, Jasmine, Kambajala, Kasambruga, Kohlrabi, Lantana, Lemon, Lemongrass, Malabar_Nut, Malabar_Spinach, Mango, Marigold, Mint, Neem, Nelavembu, Nerale, Nooni, Onion, Padri, Palak(Spinach), Papaya, Parijatha, Pea, Pepper, Pomoegranate, Pumpkin, Raddish, Rose, Sampige, Sapota, Seethaashoka, Seethapala, Spinach1, Tamarind, Taro, Tecoma, Thumbe, Tomato, Tulsi, Turmeric, ashoka, camphor, kamakasturi, kepala), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -909,7 +937,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5945 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Aloevera, Amla, Amruta_Balli, Arali, Ashoka, Ashwagandha, Avacado, Bamboo, Basale, Betel, Betel_Nut, Brahmi, Castor, Curry_Leaf, Doddapatre, Ekka, Ganike, Gauva, Geranium, Henna, Hibiscus, Honge, Insulin, Jasmine, Lemon, Lemon_grass, Mango, Mint, Nagadali, Neem, Nithyapushpa, Nooni, Pappaya, Pepper, Pomegranate, Raktachandini, Rose, Sapota, Tulasi, Wood_sorel. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Aloevera, Amla, Amruta_Balli, Arali, Ashoka, Ashwagandha, Avacado, Bamboo, Basale, Betel, Betel_Nut, Brahmi, Castor, Curry_Leaf, Doddapatre, Ekka, Ganike, Gauva, Geranium, Henna, Hibiscus, Honge, Insulin, Jasmine, Lemon, Lemon_grass, Mango, Mint, Nagadali, Neem, Nithyapushpa, Nooni, Pappaya, Pepper, Pomegranate, Raktachandini, Rose, Sapota, Tulasi, Wood_sorel), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -974,7 +1003,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5945 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Aloevera, Amla, Amruta_Balli, Arali, Ashoka, Ashwagandha, Avacado, Bamboo, Basale, Betel, Betel_Nut, Brahmi, Castor, Curry_Leaf, Doddapatre, Ekka, Ganike, Gauva, Geranium, Henna, Hibiscus, Honge, Insulin, Jasmine, Lemon, Lemon_grass, Mango, Mint, Nagadali, Neem, Nithyapushpa, Nooni, Pappaya, Pepper, Pomegranate, Raktachandini, Rose, Sapota, Tulasi, Wood_sorel. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Aloevera, Amla, Amruta_Balli, Arali, Ashoka, Ashwagandha, Avacado, Bamboo, Basale, Betel, Betel_Nut, Brahmi, Castor, Curry_Leaf, Doddapatre, Ekka, Ganike, Gauva, Geranium, Henna, Hibiscus, Honge, Insulin, Jasmine, Lemon, Lemon_grass, Mango, Mint, Nagadali, Neem, Nithyapushpa, Nooni, Pappaya, Pepper, Pomegranate, Raktachandini, Rose, Sapota, Tulasi, Wood_sorel), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1000,7 +1030,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1698 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the Flower, Shoot, Maybe, Leaf. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: Flower, Shoot, Maybe, Leaf). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1031,7 +1062,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 19526 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Bad, Good, Mixed. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bad, Good, Mixed), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1061,7 +1093,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 10154 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Formalin-mixed, Fresh, Rotten. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Formalin-mixed, Fresh, Rotten), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1088,7 +1121,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1411 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Tea Algal Spot, Tea Brown Blight, Citruspot, Early_Mild_Spotting, Fungal_Infected, Tea Grey Blight, Healthy, Mild_Edge_Damage, Tea Red Spot, Senescent, Slightly_Diseased, Wrinkled_Leaf. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Tea Algal Spot, Tea Brown Blight, Citruspot, Early_Mild_Spotting, Fungal_Infected, Tea Grey Blight, Healthy, Mild_Edge_Damage, Tea Red Spot, Senescent, Slightly_Diseased, Wrinkled_Leaf), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1114,7 +1148,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 526 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: J, L, R, S. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: J, L, R, S), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -1140,7 +1175,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 526 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: J, L, R, S. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: J, L, R, S), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1167,7 +1203,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3006 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Anthracnose, Bacterial-Spot, Downy-Mildew, Healthy-Leaf, Pest-Damage. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anthracnose, Bacterial-Spot, Downy-Mildew, Healthy-Leaf, Pest-Damage), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1197,7 +1234,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 551 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the ragweed, waterhemp, horseweed, redrootpigweed, kochia. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: ragweed, waterhemp, horseweed, redrootpigweed, kochia). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1227,7 +1265,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3208 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the horseweed, kochia, corn, ragweed, redrootpigweed. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: horseweed, kochia, corn, ragweed, redrootpigweed). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1253,7 +1292,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2782 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Caterpillar_Semilooper_Pest, Frog_Leaf_Eye, Healthy, Mosaic, Rust, Spectoria_Brown_Spot. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Caterpillar_Semilooper_Pest, Frog_Leaf_Eye, Healthy, Mosaic, Rust, Spectoria_Brown_Spot), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1279,7 +1319,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2842 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Healthy, Mosaic, Rust, Semilooper_Pest. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Healthy, Mosaic, Rust, Semilooper_Pest), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -1305,7 +1346,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 19141 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Asian_Pigeonwings_(Clitoria Ternatea), Bilayat_(Mexicana_Argemone), Choti_dudhi_(Euphorbia_hirta), Digitaria_SP_(Digitaria Sanguinalis ), Dwarf_cassia_(Chamaecrista pumila), Gajar_gavat_(Parthenium hysterophorus), Graceful_Sandmart_(Euphorbia hypericifolia), Harali_(Cynodon_dactylon), Kena_(Commplina_benghalensio), Lamber_Quarter_plant(Chenopodium ), Lavhala_(Cyperus_Rotundus), Little_Mallow(Malva parviflora), Moti_dudhi(Euphorbia_geneculata_L), Obscure_morning _glory(Ipomoea obscura), Punarnava _(Boerhaavia diffusa), Sicklepod_(Senna obtusifolia). Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Asian_Pigeonwings_(Clitoria Ternatea), Bilayat_(Mexicana_Argemone), Choti_dudhi_(Euphorbia_hirta), Digitaria_SP_(Digitaria Sanguinalis ), Dwarf_cassia_(Chamaecrista pumila), Gajar_gavat_(Parthenium hysterophorus), Graceful_Sandmart_(Euphorbia hypericifolia), Harali_(Cynodon_dactylon), Kena_(Commplina_benghalensio), Lamber_Quarter_plant(Chenopodium ), Lavhala_(Cyperus_Rotundus), Little_Mallow(Malva parviflora), Moti_dudhi(Euphorbia_geneculata_L), Obscure_morning _glory(Ipomoea obscura), Punarnava _(Boerhaavia diffusa), Sicklepod_(Senna obtusifolia)), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1331,7 +1373,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 19141 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Asian_Pigeonwings_(Clitoria Ternatea), Bilayat_(Mexicana_Argemone), Choti_dudhi_(Euphorbia_hirta), Digitaria_SP_(Digitaria Sanguinalis ), Dwarf_cassia_(Chamaecrista pumila), Gajar_gavat_(Parthenium hysterophorus), Graceful_Sandmart_(Euphorbia hypericifolia), Harali_(Cynodon_dactylon), Kena_(Commplina_benghalensio), Lamber_Quarter_plant(Chenopodium ), Lavhala_(Cyperus_Rotundus), Little_Mallow(Malva parviflora), Moti_dudhi(Euphorbia_geneculata_L), Obscure_morning _glory(Ipomoea obscura), Punarnava _(Boerhaavia diffusa), Sicklepod_(Senna obtusifolia). Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Asian_Pigeonwings_(Clitoria Ternatea), Bilayat_(Mexicana_Argemone), Choti_dudhi_(Euphorbia_hirta), Digitaria_SP_(Digitaria Sanguinalis ), Dwarf_cassia_(Chamaecrista pumila), Gajar_gavat_(Parthenium hysterophorus), Graceful_Sandmart_(Euphorbia hypericifolia), Harali_(Cynodon_dactylon), Kena_(Commplina_benghalensio), Lamber_Quarter_plant(Chenopodium ), Lavhala_(Cyperus_Rotundus), Little_Mallow(Malva parviflora), Moti_dudhi(Euphorbia_geneculata_L), Obscure_morning _glory(Ipomoea obscura), Punarnava _(Boerhaavia diffusa), Sicklepod_(Senna obtusifolia)), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1357,7 +1400,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 4000 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Healthy, Powdery Mildew, Sooty Mould. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Healthy, Powdery Mildew, Sooty Mould), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -1383,7 +1427,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 4000 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Healthy, Powdery Mildew, Sooty Mould. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Healthy, Powdery Mildew, Sooty Mould), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1409,7 +1454,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1415 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the Ripe_DateFruit, Unripe_DateFruit. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: Ripe_DateFruit, Unripe_DateFruit). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1435,7 +1481,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2817 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Bacterial Leaf Spot, Cercospora Leaf Spot, Healthy, Yellow. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bacterial Leaf Spot, Cercospora Leaf Spot, Healthy, Yellow), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/siglip-base-patch16-224",
@@ -1461,7 +1508,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2817 images"
   },
   {
     "model": "openai/clip-vit-base-patch32",
@@ -1487,7 +1535,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2817 images"
   },
   {
     "model": "kakaobrain/align-base",
@@ -1513,7 +1562,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2817 images"
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1539,7 +1589,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1495 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Alternaria Leaf Spot, Cercospora Leaf Spot, Downy Mildew, Healthy, Leaf curly virus, Phyllosticta leaf spot. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Alternaria Leaf Spot, Cercospora Leaf Spot, Downy Mildew, Healthy, Leaf curly virus, Phyllosticta leaf spot), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1565,7 +1616,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 6700 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: BACTERIAL SOFT ROT, BANANA APHIDS, BANANA FRUIT- SCARRING BEETLE, BLACK SIGATOKA, PANAMA DISEASE, POTASSIUM DEFICIENCY, PSEUDOSTEM WEEVIL, YELLOW SIGATOKA. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: BACTERIAL SOFT ROT, BANANA APHIDS, BANANA FRUIT- SCARRING BEETLE, BLACK SIGATOKA, PANAMA DISEASE, POTASSIUM DEFICIENCY, PSEUDOSTEM WEEVIL, YELLOW SIGATOKA), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -1591,7 +1643,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 6700 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: BACTERIAL SOFT ROT, BANANA APHIDS, BANANA FRUIT- SCARRING BEETLE, BLACK SIGATOKA, PANAMA DISEASE, POTASSIUM DEFICIENCY, PSEUDOSTEM WEEVIL, YELLOW SIGATOKA. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: BACTERIAL SOFT ROT, BANANA APHIDS, BANANA FRUIT- SCARRING BEETLE, BLACK SIGATOKA, PANAMA DISEASE, POTASSIUM DEFICIENCY, PSEUDOSTEM WEEVIL, YELLOW SIGATOKA), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1617,7 +1670,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2763 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: BHIMKOL, JAHAJI FRUIT, JAHAJI LEAF, JAHAJI STEM, KACHKOL FRUIT, MALBHOG FRUIT, MALBHOG LEAF. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: BHIMKOL, JAHAJI FRUIT, JAHAJI LEAF, JAHAJI STEM, KACHKOL FRUIT, MALBHOG FRUIT, MALBHOG LEAF), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -1643,7 +1697,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2763 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: BHIMKOL, JAHAJI FRUIT, JAHAJI LEAF, JAHAJI STEM, KACHKOL FRUIT, MALBHOG FRUIT, MALBHOG LEAF. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: BHIMKOL, JAHAJI FRUIT, JAHAJI LEAF, JAHAJI STEM, KACHKOL FRUIT, MALBHOG FRUIT, MALBHOG LEAF), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1670,7 +1725,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1800 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Diseased, Dried, Healthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Diseased, Dried, Healthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1697,7 +1753,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1515 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the Ripe quince, Unripe quince. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: Ripe quince, Unripe quince). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -1723,7 +1780,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 917 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Black Spot, Downy Mildew, Fresh Leaf. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Black Spot, Downy Mildew, Fresh Leaf), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1749,7 +1807,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 917 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Black Spot, Downy Mildew, Fresh Leaf. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Black Spot, Downy Mildew, Fresh Leaf), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1794,7 +1853,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2513 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Abutilon Indicum, Aloe barbadensis miller, Calotropis gigantea, Canna indica, Cissus quadrangularis, Curcuma longa, Eclipta prostrate, Eichhornia Crassipes, Hibiscus Rosasinensis, Ixora coccinea, Justica adhatoda, Murraya koenigii, Ocimum tenuiflorum, Ouretlanata, Phyllanthus amarus, Ricinus communis, Senna Atriculata, Sesbania grandiflora, Trifolium Repens, Ziziphus mauritiana. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Abutilon Indicum, Aloe barbadensis miller, Calotropis gigantea, Canna indica, Cissus quadrangularis, Curcuma longa, Eclipta prostrate, Eichhornia Crassipes, Hibiscus Rosasinensis, Ixora coccinea, Justica adhatoda, Murraya koenigii, Ocimum tenuiflorum, Ouretlanata, Phyllanthus amarus, Ricinus communis, Senna Atriculata, Sesbania grandiflora, Trifolium Repens, Ziziphus mauritiana), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1820,7 +1880,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 247 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the Early-Turning, Green, Late-Turning, Red, Turning, White. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: Early-Turning, Green, Late-Turning, Red, Turning, White). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1846,7 +1907,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2208 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the T1, T2, T3, T4. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: T1, T2, T3, T4). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1873,7 +1935,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5472 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Anthracnose, Black spots, Brown clumps, Brown spots, Downy mildew, Flea Beetles, Healthy, Mealy bugs, Mosaic Viruses, Powdery mildew, Scale insect, Sooty mold, Spiders, Translucent lesion. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anthracnose, Black spots, Brown clumps, Brown spots, Downy mildew, Flea Beetles, Healthy, Mealy bugs, Mosaic Viruses, Powdery mildew, Scale insect, Sooty mold, Spiders, Translucent lesion), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -1899,7 +1962,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 100 images\n\nqwen detection prompt: 'Locate every instance of the Visible Fruit/Flower in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: Visible Fruit/Flower). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1925,7 +1989,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 100 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the Visible Fruit/Flower. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: Visible Fruit/Flower). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -1951,7 +2016,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 50 images\n\nqwen detection prompt: 'Locate every instance of the Visible Fruit/Flower, Partially Visible Fruit/Flower, Visible Occluded Fruit/Flower, Partially Visible Occluded Fruit/Flower in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: Visible Fruit/Flower, Partially Visible Fruit/Flower, Visible Occluded Fruit/Flower, Partially Visible Occluded Fruit/Flower). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -1977,7 +2043,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 50 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the Visible Fruit/Flower, Partially Visible Fruit/Flower, Visible Occluded Fruit/Flower, Partially Visible Occluded Fruit/Flower. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: Visible Fruit/Flower, Partially Visible Fruit/Flower, Visible Occluded Fruit/Flower, Partially Visible Occluded Fruit/Flower). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2003,7 +2070,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 689 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the apple. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: apple). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -2029,7 +2097,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 689 images\n\nqwen detection prompt: 'Locate every instance of the apple in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: apple). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -2055,7 +2124,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 967 images\n\nqwen detection prompt: 'Locate every instance of the apple in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: apple). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2081,7 +2151,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 967 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the apple. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: apple). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2107,7 +2178,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2290 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the apple. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: apple). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -2133,7 +2205,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2290 images\n\nqwen detection prompt: 'Locate every instance of the apple in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: apple). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -2159,7 +2232,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2676 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Aphid, Downy mildew, Healthy, Leaf curl, Leaf miner. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Aphid, Downy mildew, Healthy, Leaf curl, Leaf miner), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2185,7 +2259,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2676 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Aphid, Downy mildew, Healthy, Leaf curl, Leaf miner. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Aphid, Downy mildew, Healthy, Leaf curl, Leaf miner), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/siglip-base-patch16-224",
@@ -2211,7 +2286,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2676 images"
   },
   {
     "model": "openai/clip-vit-base-patch32",
@@ -2237,7 +2313,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2676 images"
   },
   {
     "model": "kakaobrain/align-base",
@@ -2263,7 +2340,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2676 images"
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2289,7 +2367,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2179 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the 0. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: 0). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2316,7 +2395,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1748 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Class_A, Class_B, Defect. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Class_A, Class_B, Defect), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -2343,7 +2423,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1748 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Class_A, Class_B, Defect. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Class_A, Class_B, Defect), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2369,7 +2450,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1288 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: healthy, segatoka, xamthomonas. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: healthy, segatoka, xamthomonas), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -2395,7 +2477,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1288 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: healthy, segatoka, xamthomonas. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: healthy, segatoka, xamthomonas), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2421,7 +2504,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5348 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Boron, Calcium, Healthy, Iron, Magnesium, Manganese, Potassium, Sulphur, Zinc. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Boron, Calcium, Healthy, Iron, Magnesium, Manganese, Potassium, Sulphur, Zinc), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -2447,7 +2531,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5348 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Boron, Calcium, Healthy, Iron, Magnesium, Manganese, Potassium, Sulphur, Zinc. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Boron, Calcium, Healthy, Iron, Magnesium, Manganese, Potassium, Sulphur, Zinc), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2473,7 +2558,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1166 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Anaji, Bichi, Champa, Deshi, Shagor, Shobri. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anaji, Bichi, Champa, Deshi, Shagor, Shobri), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2500,7 +2586,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5696 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Disease, Dry Leaf, Healthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Disease, Dry Leaf, Healthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -2527,7 +2614,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5696 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Disease, Dry Leaf, Healthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Disease, Dry Leaf, Healthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -2554,7 +2642,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 4467 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Bacterial wilt, Blight, Fresh Leaf, Mosaic Virus, Rust, Septoria leaf spot. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bacterial wilt, Blight, Fresh Leaf, Mosaic Virus, Rust, Septoria leaf spot), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2581,7 +2670,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 4467 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Bacterial wilt, Blight, Fresh Leaf, Mosaic Virus, Rust, Septoria leaf spot. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bacterial wilt, Blight, Fresh Leaf, Mosaic Virus, Rust, Septoria leaf spot), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -2607,7 +2697,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1034 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: angular_leaf_spot, bean_rust, healthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: angular_leaf_spot, bean_rust, healthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2633,7 +2724,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1034 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: angular_leaf_spot, bean_rust, healthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: angular_leaf_spot, bean_rust, healthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2659,7 +2751,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3589 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Bacterial_Leaf_Disease, Dried_Leaf, Fungal_Brown_Spot_Disease, Healthy_Leaf. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bacterial_Leaf_Disease, Dried_Leaf, Fungal_Brown_Spot_Disease, Healthy_Leaf), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2685,7 +2778,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2037 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Healthy_Leaf, Leaf_Rot, Leaf_Spot. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Healthy_Leaf, Leaf_Rot, Leaf_Spot), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -2711,7 +2805,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2037 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Healthy_Leaf, Leaf_Rot, Leaf_Spot. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Healthy_Leaf, Leaf_Rot, Leaf_Spot), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2738,7 +2833,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 4038 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Cercospora leaf spot, Healthy, Insect, Leaf Crinkle, Yellow Mosaic. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Cercospora leaf spot, Healthy, Insect, Leaf Crinkle, Yellow Mosaic), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -2766,7 +2862,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1007 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: anthracnose, healthy, leaf_crinckle, powdery_mildew, yellow_mosaic. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: anthracnose, healthy, leaf_crinckle, powdery_mildew, yellow_mosaic), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2794,7 +2891,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1007 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: anthracnose, healthy, leaf_crinckle, powdery_mildew, yellow_mosaic. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: anthracnose, healthy, leaf_crinckle, powdery_mildew, yellow_mosaic), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2825,7 +2923,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 4464 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Damaged, Fresh, Severely Damaged. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Damaged, Fresh, Severely Damaged), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2852,7 +2951,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2618 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Healthy Fruits, Healthy Leaves, Insect Hole leaves, Unhealthy Fruits, Yellow Leaves. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Healthy Fruits, Healthy Leaves, Insect Hole leaves, Unhealthy Fruits, Yellow Leaves), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -2878,7 +2978,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3098 images\n\nqwen detection prompt: 'Locate every instance of the tree, flower, premature, unripe, ripe, spoiled in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: tree, flower, premature, unripe, ripe, spoiled). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2904,7 +3005,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3098 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the tree, flower, premature, unripe, ripe, spoiled. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: tree, flower, premature, unripe, ripe, spoiled). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2930,7 +3032,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2661 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Black Rot, Healthy, Insect Hole. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Black Rot, Healthy, Insect Hole), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -2956,7 +3059,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 9094 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Dried, Healthy, Unhealthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Dried, Healthy, Unhealthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -2982,7 +3086,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 9094 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Dried, Healthy, Unhealthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Dried, Healthy, Unhealthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -3008,7 +3113,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 10660 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Dried, Healthy, Unhealthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Dried, Healthy, Unhealthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3034,7 +3140,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 10660 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Dried, Healthy, Unhealthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Dried, Healthy, Unhealthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -3060,7 +3167,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 759 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: black_spot, canker, greening, healthy, melanose, scab. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: black_spot, canker, greening, healthy, melanose, scab), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3086,7 +3194,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 759 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: black_spot, canker, greening, healthy, melanose, scab. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: black_spot, canker, greening, healthy, melanose, scab), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3115,7 +3224,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1379 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: murcott, ponkan, tangerine, tankan. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: murcott, ponkan, tangerine, tankan), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3141,7 +3251,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 953 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Citrus_leafminer, Fe, Greasy_spot, HLB, Healthy, Mg, Mn, N, Red_scale, Red_scale_sequelae, Texas_mite, Zn. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Citrus_leafminer, Fe, Greasy_spot, HLB, Healthy, Mg, Mn, N, Red_scale, Red_scale_sequelae, Texas_mite, Zn), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -3167,7 +3278,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5798 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Bud_Root_Dropping, Bud_Rot, Gray_Leaf_Spot, Leaf_Rot, Stem_Bleeding. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bud_Root_Dropping, Bud_Rot, Gray_Leaf_Spot, Leaf_Rot, Stem_Bleeding), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3193,7 +3305,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5798 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Bud_Root_Dropping, Bud_Rot, Gray_Leaf_Spot, Leaf_Rot, Stem_Bleeding. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bud_Root_Dropping, Bud_Rot, Gray_Leaf_Spot, Leaf_Rot, Stem_Bleeding), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3219,7 +3332,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 464 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: A, AA, AAA, AB, Bits, Bulk, C, PB-I, PB-II. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: A, AA, AAA, AB, Bits, Bulk, C, PB-I, PB-II), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3246,7 +3360,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 4188 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Blight, Common_Rust, Gray_Leaf_Spot, Healthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Blight, Common_Rust, Gray_Leaf_Spot, Healthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/siglip-base-patch16-224",
@@ -3272,7 +3387,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1373 images"
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -3298,7 +3414,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1373 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Alternaria_Leaf, Bacterial_Blight, Fusarium_Wilt, Healthy_Leaf, Verticillium_Wilt. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Alternaria_Leaf, Bacterial_Blight, Fusarium_Wilt, Healthy_Leaf, Verticillium_Wilt), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3324,7 +3441,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1373 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Alternaria_Leaf, Bacterial_Blight, Fusarium_Wilt, Healthy_Leaf, Verticillium_Wilt. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Alternaria_Leaf, Bacterial_Blight, Fusarium_Wilt, Healthy_Leaf, Verticillium_Wilt), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "openai/clip-vit-base-patch32",
@@ -3350,7 +3468,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1373 images"
   },
   {
     "model": "kakaobrain/align-base",
@@ -3376,7 +3495,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1373 images"
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -3402,7 +3522,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2137 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Bacterial Blight, Curl Virus, Healthy Leaf, Herbicide Growth Damage, Leaf Hopper Jassids, Leaf Redding, Leaf Variegation. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bacterial Blight, Curl Virus, Healthy Leaf, Herbicide Growth Damage, Leaf Hopper Jassids, Leaf Redding, Leaf Variegation), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3428,7 +3549,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2137 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Bacterial Blight, Curl Virus, Healthy Leaf, Herbicide Growth Damage, Leaf Hopper Jassids, Leaf Redding, Leaf Variegation. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bacterial Blight, Curl Virus, Healthy Leaf, Herbicide Growth Damage, Leaf Hopper Jassids, Leaf Redding, Leaf Variegation), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -3459,7 +3581,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1176 images\n\nqwen detection prompt: 'Locate every instance of the weed, crop in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: weed, crop). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3490,7 +3613,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1176 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the weed, crop. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: weed, crop). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3517,7 +3641,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 508 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: black_nightshade, cotton, tomato, velvet_leaf. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: black_nightshade, cotton, tomato, velvet_leaf), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -3543,7 +3668,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 7689 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Anthracnose, Bacterial_Wilt, Belly_Rot, Downy_Mildew, Fresh_Cucumber, Fresh_Leaf, Gummy_Stem_Blight, Pythium_Fruit_Rot. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anthracnose, Bacterial_Wilt, Belly_Rot, Downy_Mildew, Fresh_Cucumber, Fresh_Leaf, Gummy_Stem_Blight, Pythium_Fruit_Rot), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3569,7 +3695,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 7689 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Anthracnose, Bacterial_Wilt, Belly_Rot, Downy_Mildew, Fresh_Cucumber, Fresh_Leaf, Gummy_Stem_Blight, Pythium_Fruit_Rot. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anthracnose, Bacterial_Wilt, Belly_Rot, Downy_Mildew, Fresh_Cucumber, Fresh_Leaf, Gummy_Stem_Blight, Pythium_Fruit_Rot), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -3597,7 +3724,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 8226 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Athracnose, Blank Canker, Diplodia Rot, Leaf spot on Leaves, Leaf spot on fruit, Mealy Bug. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Athracnose, Blank Canker, Diplodia Rot, Leaf spot on Leaves, Leaf spot on fruit, Mealy Bug), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3625,7 +3753,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 8226 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Athracnose, Blank Canker, Diplodia Rot, Leaf spot on Leaves, Leaf spot on fruit, Mealy Bug. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Athracnose, Blank Canker, Diplodia Rot, Leaf spot on Leaves, Leaf spot on fruit, Mealy Bug), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -3651,7 +3780,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5530 images\n\nqwen detection prompt: 'Locate every instance of the 0 in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: 0). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3677,7 +3807,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5530 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the 0. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: 0). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3703,7 +3834,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 9010 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the 0, 1, 2, 3. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: 0, 1, 2, 3). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3729,7 +3861,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3004 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: grade-1, grade-2, grade-3. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: grade-1, grade-2, grade-3), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -3755,7 +3888,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3004 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: grade-1, grade-2, grade-3. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: grade-1, grade-2, grade-3), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -3781,7 +3915,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3089 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Black Scorch, Fusarium Wilt, Healthy sample, Leaf Spots, Magnesium Deficiency, Manganese Deficiency, Parlatoria Blanchardi, Potassium Deficiency, Rachis Blight. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Black Scorch, Fusarium Wilt, Healthy sample, Leaf Spots, Magnesium Deficiency, Manganese Deficiency, Parlatoria Blanchardi, Potassium Deficiency, Rachis Blight), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3807,7 +3942,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3089 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Black Scorch, Fusarium Wilt, Healthy sample, Leaf Spots, Magnesium Deficiency, Manganese Deficiency, Parlatoria Blanchardi, Potassium Deficiency, Rachis Blight. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Black Scorch, Fusarium Wilt, Healthy sample, Leaf Spots, Magnesium Deficiency, Manganese Deficiency, Parlatoria Blanchardi, Potassium Deficiency, Rachis Blight), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3834,7 +3970,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3000 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Bug, Dubas, Healthy, Honey. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bug, Dubas, Healthy, Honey), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -3861,7 +3998,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3000 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Bug, Dubas, Healthy, Honey. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bug, Dubas, Healthy, Honey), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3887,7 +4025,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 4497 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Bad fruit, Bad leaf, Good fruit, Good leaf. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bad fruit, Bad leaf, Good fruit, Good leaf), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3913,7 +4052,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2127 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Immature Dragon Fruit, Mature Dragon Fruit. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Immature Dragon Fruit, Mature Dragon Fruit), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -3939,7 +4079,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2127 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Immature Dragon Fruit, Mature Dragon Fruit. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Immature Dragon Fruit, Mature Dragon Fruit), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -3965,7 +4106,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1652 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Defect Dragon Fruit, Fresh Dragon Fruit. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Defect Dragon Fruit, Fresh Dragon Fruit), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -3991,7 +4133,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1652 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Defect Dragon Fruit, Fresh Dragon Fruit. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Defect Dragon Fruit, Fresh Dragon Fruit), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -4017,7 +4160,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5451 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: anthracnose_disease, canker_disease, fruit_rot, mealybug_infestation, pink_disease, sooty_mold, stem_blight, stem_cracking_ gummosis, thrips_disease, yellow_leaf. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: anthracnose_disease, canker_disease, fruit_rot, mealybug_infestation, pink_disease, sooty_mold, stem_blight, stem_cracking_ gummosis, thrips_disease, yellow_leaf), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -4043,7 +4187,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5451 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: anthracnose_disease, canker_disease, fruit_rot, mealybug_infestation, pink_disease, sooty_mold, stem_blight, stem_cracking_ gummosis, thrips_disease, yellow_leaf. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: anthracnose_disease, canker_disease, fruit_rot, mealybug_infestation, pink_disease, sooty_mold, stem_blight, stem_cracking_ gummosis, thrips_disease, yellow_leaf), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -4069,7 +4214,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2595 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Leaf_Algal, Leaf_Blight, Leaf_Colletotrichum, Leaf_Healthy, Leaf_Phomopsis, Leaf_Rhizoctonia. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Leaf_Algal, Leaf_Blight, Leaf_Colletotrichum, Leaf_Healthy, Leaf_Phomopsis, Leaf_Rhizoctonia), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -4095,7 +4241,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2595 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Leaf_Algal, Leaf_Blight, Leaf_Colletotrichum, Leaf_Healthy, Leaf_Phomopsis, Leaf_Rhizoctonia. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Leaf_Algal, Leaf_Blight, Leaf_Colletotrichum, Leaf_Healthy, Leaf_Phomopsis, Leaf_Rhizoctonia), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -4121,7 +4268,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 4089 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Healthy Leaf, Insect Pest Disease, Leaf Spot Disease, Mosaic Virus Disease, White Mold Disease, Wilt Disease. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Healthy Leaf, Insect Pest Disease, Leaf Spot Disease, Mosaic Virus Disease, White Mold Disease, Wilt Disease), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -4147,7 +4295,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3116 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Aphids, Cercospora Leaf Spot, Defect Eggplant, Flea Beetles, Fresh Eggplant, Fresh Eggplant Leaf, Leaf Wilt, Phytophthora Blight, Powdery Mildew, Tobacco Mosaic Virus. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Aphids, Cercospora Leaf Spot, Defect Eggplant, Flea Beetles, Fresh Eggplant, Fresh Eggplant Leaf, Leaf Wilt, Phytophthora Blight, Powdery Mildew, Tobacco Mosaic Virus), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/siglip-base-patch16-224",
@@ -4173,7 +4322,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3116 images"
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -4199,7 +4349,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3116 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Aphids, Cercospora Leaf Spot, Defect Eggplant, Flea Beetles, Fresh Eggplant, Fresh Eggplant Leaf, Leaf Wilt, Phytophthora Blight, Powdery Mildew, Tobacco Mosaic Virus. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Aphids, Cercospora Leaf Spot, Defect Eggplant, Flea Beetles, Fresh Eggplant, Fresh Eggplant Leaf, Leaf Wilt, Phytophthora Blight, Powdery Mildew, Tobacco Mosaic Virus), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "openai/clip-vit-base-patch32",
@@ -4225,7 +4376,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3116 images"
   },
   {
     "model": "kakaobrain/align-base",
@@ -4251,7 +4403,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3116 images"
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -4277,7 +4430,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 249 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the grape. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: grape). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -4303,7 +4457,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2321 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: healthy, infected. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: healthy, infected), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -4329,7 +4484,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2321 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: healthy, infected. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: healthy, infected), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -4355,7 +4511,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 6611 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the Immature, Growing, Mature, cavity. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: Immature, Growing, Mature, cavity). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -4388,7 +4545,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3200 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: fresh, rotten. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: fresh, rotten), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -4420,7 +4578,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 565 images\n\nqwen detection prompt: 'Locate every instance of the avocado, rockmelon, apple, orange, strawberry, mango, capsicum in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: avocado, rockmelon, apple, orange, strawberry, mango, capsicum). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -4452,7 +4611,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 565 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the avocado, rockmelon, apple, orange, strawberry, mango, capsicum. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: avocado, rockmelon, apple, orange, strawberry, mango, capsicum). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -4487,7 +4647,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3173 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Aegle marmelos, Black plum, Custard Apple, Guava, Jackfruit, Lotkon, Lychee, Mango, Plum, Star Fruit. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Aegle marmelos, Black plum, Custard Apple, Guava, Jackfruit, Lotkon, Lychee, Mango, Plum, Star Fruit), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -4513,7 +4674,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 134 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the object. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: object). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -4539,7 +4701,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 134 images\n\nqwen detection prompt: 'Locate every instance of the object in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: object). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -4565,7 +4728,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 25 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the object. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: object). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -4591,7 +4755,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 25 images\n\nqwen detection prompt: 'Locate every instance of the object in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: object). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -4617,7 +4782,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 402 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the plant, weed. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: plant, weed). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -4643,7 +4809,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 98 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the object. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: object). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -4669,7 +4836,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 98 images\n\nqwen detection prompt: 'Locate every instance of the object in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: object). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -4695,7 +4863,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 500 images\n\nqwen detection prompt: 'Locate every instance of the Canopy, Crown, crop in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: Canopy, Crown, crop). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -4721,7 +4890,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 500 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the Canopy, Crown, crop. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: Canopy, Crown, crop). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -4747,7 +4917,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 500 images\n\nqwen detection prompt: 'Locate every instance of the Crop in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: Crop). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -4773,7 +4944,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 500 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the Crop. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: Crop). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -4799,7 +4971,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 500 images\n\nqwen detection prompt: 'Locate every instance of the Crop in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: Crop). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -4825,7 +4998,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 500 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the Crop. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: Crop). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -4851,7 +5025,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 500 images\n\nqwen detection prompt: 'Locate every instance of the Crop in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: Crop). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -4877,7 +5052,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 500 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the Crop. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: Crop). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -4903,7 +5079,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 500 images\n\nqwen detection prompt: 'Locate every instance of the Bud, Calyx, Detached Fruit, Flower, Large green, Leaf, Ripe fruit, Small Green, Stem, Unripe fruit in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: Bud, Calyx, Detached Fruit, Flower, Large green, Leaf, Ripe fruit, Small Green, Stem, Unripe fruit). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -4929,7 +5106,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 500 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the Bud, Calyx, Detached Fruit, Flower, Large green, Leaf, Ripe fruit, Small Green, Stem, Unripe fruit. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: Bud, Calyx, Detached Fruit, Flower, Large green, Leaf, Ripe fruit, Small Green, Stem, Unripe fruit). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -4955,7 +5133,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 126 images\n\nqwen detection prompt: 'Locate every instance of the grape in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: grape). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -4981,7 +5160,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 126 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the grape. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: grape). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -5007,7 +5187,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 150 images\n\nqwen detection prompt: 'Locate every instance of the grape in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: grape). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -5033,7 +5214,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 150 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the grape. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: grape). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -5059,7 +5241,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 448 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the grape, 1. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: grape, 1). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -5085,7 +5268,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 448 images\n\nqwen detection prompt: 'Locate every instance of the grape, 1 in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: grape, 1). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -5111,7 +5295,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2726 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Bacterial Leaf Spot, Downy Mildew, Healthy Leaves, Powdery Mildew. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bacterial Leaf Spot, Downy Mildew, Healthy Leaves, Powdery Mildew), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -5137,7 +5322,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2726 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Bacterial Leaf Spot, Downy Mildew, Healthy Leaves, Powdery Mildew. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bacterial Leaf Spot, Downy Mildew, Healthy Leaves, Powdery Mildew), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -5163,7 +5349,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 8000 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Aswud Balad, Deas Al-Annz, Frinsi, Halawani, Kamali, Riasi, Shdah, Thompson Seedless. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Aswud Balad, Deas Al-Annz, Frinsi, Halawani, Kamali, Riasi, Shdah, Thompson Seedless), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -5189,7 +5376,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 8000 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Aswud Balad, Deas Al-Annz, Frinsi, Halawani, Kamali, Riasi, Shdah, Thompson Seedless. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Aswud Balad, Deas Al-Annz, Frinsi, Halawani, Kamali, Riasi, Shdah, Thompson Seedless), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -5215,7 +5403,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 611 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: conf, conf+, esca, fd. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: conf, conf+, esca, fd), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -5241,7 +5430,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 611 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: conf, conf+, esca, fd. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: conf, conf+, esca, fd), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -5267,7 +5457,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1770 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: esca, healthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: esca, healthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -5293,7 +5484,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1770 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: esca, healthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: esca, healthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -5319,7 +5511,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1212 images\n\nqwen detection prompt: 'Locate every instance of the 0 in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: 0). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -5345,7 +5538,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1212 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the 0. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: 0). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -5384,7 +5578,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 28000 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Blackbean, Canola, Corn, Field Pea, Flax, Horseweed, Kochia, Lentil, Palmer Amaranth, Ragweed, Redroot Pigweed, Soybean, Sugar beet, Waterhemp. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Blackbean, Canola, Corn, Field Pea, Flax, Horseweed, Kochia, Lentil, Palmer Amaranth, Ragweed, Redroot Pigweed, Soybean, Sugar beet, Waterhemp), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -5423,7 +5618,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 28000 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Blackbean, Canola, Corn, Field Pea, Flax, Horseweed, Kochia, Lentil, Palmer Amaranth, Ragweed, Redroot Pigweed, Soybean, Sugar beet, Waterhemp. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Blackbean, Canola, Corn, Field Pea, Flax, Horseweed, Kochia, Lentil, Palmer Amaranth, Ragweed, Redroot Pigweed, Soybean, Sugar beet, Waterhemp), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -5449,7 +5645,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1720 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: ALTERNARIA LEAF SPOT, HEALTHY, LEAF SPOT, ROSETTE, RUST. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: ALTERNARIA LEAF SPOT, HEALTHY, LEAF SPOT, ROSETTE, RUST), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "kakaobrain/align-base",
@@ -5475,7 +5672,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1720 images"
   },
   {
     "model": "google/siglip-base-patch16-224",
@@ -5501,7 +5699,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1720 images"
   },
   {
     "model": "openai/clip-vit-base-patch32",
@@ -5527,7 +5726,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1720 images"
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -5553,7 +5753,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3058 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: early_leaf_spot, healthy leaf, late leaf spot, nutrition deficiency, rust. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: early_leaf_spot, healthy leaf, late leaf spot, nutrition deficiency, rust), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -5579,7 +5780,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3058 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: early_leaf_spot, healthy leaf, late leaf spot, nutrition deficiency, rust. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: early_leaf_spot, healthy leaf, late leaf spot, nutrition deficiency, rust), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -5605,7 +5807,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3049 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Anthracnose, Canker, Dot, Healthy, Rust, Scab, Styler end root. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anthracnose, Canker, Dot, Healthy, Rust, Scab, Styler end root), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -5631,7 +5834,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 527 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Disease Free, Phytopthora, Red rust, Scab, Styler and Root. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Disease Free, Phytopthora, Red rust, Scab, Styler and Root), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -5657,7 +5861,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 306 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Canker, Dot, Mummification, Rust. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Canker, Dot, Mummification, Rust), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -5683,7 +5888,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 306 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Canker, Dot, Mummification, Rust. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Canker, Dot, Mummification, Rust), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -5709,7 +5915,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2309 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: green, mature-green, ripe. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: green, mature-green, ripe), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -5735,7 +5942,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2309 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: green, mature-green, ripe. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: green, mature-green, ripe), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -5761,7 +5969,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3782 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: healthy, not_healthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: healthy, not_healthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -5787,7 +5996,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2400 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Bacterial_Spot, Brown_Blight, Dry, Healthy, Powdery_Mildew, Sooty_Mold. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bacterial_Spot, Brown_Blight, Dry, Healthy, Powdery_Mildew, Sooty_Mold), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -5813,7 +6023,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2400 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Bacterial_Spot, Brown_Blight, Dry, Healthy, Powdery_Mildew, Sooty_Mold. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bacterial_Spot, Brown_Blight, Dry, Healthy, Powdery_Mildew, Sooty_Mold), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -5839,7 +6050,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1464 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: bruised, healthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: bruised, healthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -5865,7 +6077,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1464 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: bruised, healthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: bruised, healthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -5891,7 +6104,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1390 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Dieback, Fresh, Holed, Mosaic, Stem Soft Rot. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Dieback, Fresh, Holed, Mosaic, Stem Soft Rot), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -5917,7 +6131,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1390 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Dieback, Fresh, Holed, Mosaic, Stem Soft Rot. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Dieback, Fresh, Holed, Mosaic, Stem Soft Rot), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -5943,7 +6158,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1354 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Anthracnose, Bacterial Blight, Citrus Canker, Curl Virus, Deficiency Leaf, Dry Leaf, Healthy Leaf, Sooty Mould, Spider Mites. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anthracnose, Bacterial Blight, Citrus Canker, Curl Virus, Deficiency Leaf, Dry Leaf, Healthy Leaf, Sooty Mould, Spider Mites), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -5969,7 +6185,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1354 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Anthracnose, Bacterial Blight, Citrus Canker, Curl Virus, Deficiency Leaf, Dry Leaf, Healthy Leaf, Sooty Mould, Spider Mites. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anthracnose, Bacterial Blight, Citrus Canker, Curl Virus, Deficiency Leaf, Dry Leaf, Healthy Leaf, Sooty Mould, Spider Mites), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -5995,7 +6212,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 10042 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Dried, Healthy, Unhealthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Dried, Healthy, Unhealthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6021,7 +6239,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 10042 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Dried, Healthy, Unhealthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Dried, Healthy, Unhealthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6047,7 +6266,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5349 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Ascochyta blight, Lentil Rust, Normal, Powdery Mildew. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Ascochyta blight, Lentil Rust, Normal, Powdery Mildew), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -6073,7 +6293,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5349 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Ascochyta blight, Lentil Rust, Normal, Powdery Mildew. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Ascochyta blight, Lentil Rust, Normal, Powdery Mildew), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -6099,7 +6320,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 9356 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Healthy, MLN, MSV. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Healthy, MLN, MSV), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6125,7 +6347,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 9356 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Healthy, MLN, MSV. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Healthy, MLN, MSV), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -6152,7 +6375,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 55828 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: atriplex, chenopodium, convolvulus, cyperus, datura, lolium, maize, portulaca, salsola, solanum, sorghum, tomato. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: atriplex, chenopodium, convolvulus, cyperus, datura, lolium, maize, portulaca, salsola, solanum, sorghum, tomato), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6179,7 +6403,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 55828 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: atriplex, chenopodium, convolvulus, cyperus, datura, lolium, maize, portulaca, salsola, solanum, sorghum, tomato. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: atriplex, chenopodium, convolvulus, cyperus, datura, lolium, maize, portulaca, salsola, solanum, sorghum, tomato), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -6205,7 +6430,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 500 images\n\nqwen detection prompt: 'Locate every instance of the maize, weed in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: maize, weed). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6231,7 +6457,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 500 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the maize, weed. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: maize, weed). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6257,7 +6484,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 603 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: anthracnose_leaf_spot, healthy, straw_mite. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: anthracnose_leaf_spot, healthy, straw_mite), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6287,7 +6515,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1917 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: China Mishti, Darjeling, Mandaring, Nagpuri. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: China Mishti, Darjeling, Mandaring, Nagpuri), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6313,7 +6542,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2012 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Amrapali, Bari-4, Bari-7, Fazlee, Harivanga, Kanchon Langra, Katimon, Langra, Mollika, Nilambori. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Amrapali, Bari-4, Bari-7, Fazlee, Harivanga, Kanchon Langra, Katimon, Langra, Mollika, Nilambori), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -6339,7 +6569,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1242 images\n\nqwen detection prompt: 'Locate every instance of the mango in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: mango). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6365,7 +6596,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1242 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the mango. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: mango). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6391,7 +6623,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2000 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the Early-Fruit, Mature, Premature, Ripe. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: Early-Fruit, Mature, Premature, Ripe). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6417,7 +6650,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 4000 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Anthracnose, Bacterial_Canker, Cutting_Weevil, Die_Back, Gall_Midge, Healthy, Powdery_Mildew, Sooty_Mold. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anthracnose, Bacterial_Canker, Cutting_Weevil, Die_Back, Gall_Midge, Healthy, Powdery_Mildew, Sooty_Mold), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -6443,7 +6677,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 4000 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Anthracnose, Bacterial_Canker, Cutting_Weevil, Die_Back, Gall_Midge, Healthy, Powdery_Mildew, Sooty_Mold. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anthracnose, Bacterial_Canker, Cutting_Weevil, Die_Back, Gall_Midge, Healthy, Powdery_Mildew, Sooty_Mold), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -6469,7 +6704,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5384 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Dried, Fresh, Spoiled, Sunlight. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Dried, Fresh, Spoiled, Sunlight), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6495,7 +6731,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5384 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Dried, Fresh, Spoiled, Sunlight. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Dried, Fresh, Spoiled, Sunlight), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -6522,7 +6759,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1872 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Bacterial wilt disease, Healthy, Manganese Toxicity. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bacterial wilt disease, Healthy, Manganese Toxicity), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6549,7 +6787,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1872 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Bacterial wilt disease, Healthy, Manganese Toxicity. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bacterial wilt disease, Healthy, Manganese Toxicity), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -6575,7 +6814,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5262 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: BlackAustralia, BlackOodTurkey, Buriram60, ChiangMai60, ChiangMaiBuriram60, Kamphaengsaeng42, RedKing, TaiwanMeacho, TaiwanStraberry, WhiteKing. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: BlackAustralia, BlackOodTurkey, Buriram60, ChiangMai60, ChiangMaiBuriram60, Kamphaengsaeng42, RedKing, TaiwanMeacho, TaiwanStraberry, WhiteKing), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6601,7 +6841,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5262 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: BlackAustralia, BlackOodTurkey, Buriram60, ChiangMai60, ChiangMaiBuriram60, Kamphaengsaeng42, RedKing, TaiwanMeacho, TaiwanStraberry, WhiteKing. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: BlackAustralia, BlackOodTurkey, Buriram60, ChiangMai60, ChiangMaiBuriram60, Kamphaengsaeng42, RedKing, TaiwanMeacho, TaiwanStraberry, WhiteKing), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6627,7 +6868,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 364 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Mature, Over-mature, Under-mature. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Mature, Over-mature, Under-mature), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -6653,7 +6895,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 364 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Mature, Over-mature, Under-mature. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Mature, Over-mature, Under-mature), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -6679,7 +6922,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 501 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: adequate_matured, over_matured. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: adequate_matured, over_matured), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6705,7 +6949,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 501 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: adequate_matured, over_matured. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: adequate_matured, over_matured), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6732,7 +6977,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 245 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the onion, no_crop, foliage. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: onion, no_crop, foliage). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6758,7 +7004,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5813 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: citrus_canker, citrus_greening, citrus_mealybugs, die_back, foliage_damaged, healthy_leaf, powdery_mildew, shot_hole, spiny_whitefly, yellow_dragon, yellow_leaves. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: citrus_canker, citrus_greening, citrus_mealybugs, die_back, foliage_damaged, healthy_leaf, powdery_mildew, shot_hole, spiny_whitefly, yellow_dragon, yellow_leaves), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -6784,7 +7031,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 10407 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: bacterial_leaf_blight, bacterial_leaf_streak, bacterial_panicle_blight, blast, brown_spot, dead_heart, downy_mildew, hispa, normal, tungro. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: bacterial_leaf_blight, bacterial_leaf_streak, bacterial_panicle_blight, blast, brown_spot, dead_heart, downy_mildew, hispa, normal, tungro), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6810,7 +7058,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 10407 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: bacterial_leaf_blight, bacterial_leaf_streak, bacterial_panicle_blight, blast, brown_spot, dead_heart, downy_mildew, hispa, normal, tungro. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: bacterial_leaf_blight, bacterial_leaf_streak, bacterial_panicle_blight, blast, brown_spot, dead_heart, downy_mildew, hispa, normal, tungro), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6836,7 +7085,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1400 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Healthy Leaf, Leaf Curl, Mealybug, Mite Disease, Mosaic, Ring Spot. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Healthy Leaf, Leaf Curl, Mealybug, Mite Disease, Mosaic, Ring Spot), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6873,7 +7123,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2569 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Apple Scab Leaf, Apple leaf, Apple rust leaf, Bell_pepper leaf, Bell_pepper leaf spot, Blueberry leaf, Cherry leaf, Corn Gray leaf spot, Corn leaf blight, Corn rust leaf, Peach leaf, Potato leaf early blight, Potato leaf late blight, Raspberry leaf, Soyabean leaf, Squash Powdery mildew leaf, Strawberry leaf, Tomato Early blight leaf, Tomato Septoria leaf spot, Tomato leaf, Tomato leaf bacterial spot, Tomato leaf late blight, Tomato leaf mosaic virus, Tomato leaf yellow virus, Tomato mold leaf, Tomato two spotted spider mites leaf, grape leaf, grape leaf black rot. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Apple Scab Leaf, Apple leaf, Apple rust leaf, Bell_pepper leaf, Bell_pepper leaf spot, Blueberry leaf, Cherry leaf, Corn Gray leaf spot, Corn leaf blight, Corn rust leaf, Peach leaf, Potato leaf early blight, Potato leaf late blight, Raspberry leaf, Soyabean leaf, Squash Powdery mildew leaf, Strawberry leaf, Tomato Early blight leaf, Tomato Septoria leaf spot, Tomato leaf, Tomato leaf bacterial spot, Tomato leaf late blight, Tomato leaf mosaic virus, Tomato leaf yellow virus, Tomato mold leaf, Tomato two spotted spider mites leaf, grape leaf, grape leaf black rot), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6897,7 +7148,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2346 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the Corn leaf blight, Tomato Early blight leaf, Potato leaf early blight, Potato leaf late blight, Blueberry leaf, grape leaf black rot, Bell_pepper leaf spot, Cherry leaf, Peach leaf, Soyabean leaf, Strawberry leaf, Apple Scab Leaf, Corn rust leaf, Apple leaf, Corn Gray leaf spot, Tomato leaf mosaic virus, Tomato mold leaf, Tomato leaf yellow virus, Tomato leaf bacterial spot, Tomato leaf late blight, Squash Powdery mildew leaf, Bell_pepper leaf, grape leaf, Apple rust leaf, Tomato Septoria leaf spot, Tomato leaf, Raspberry leaf, Potato leaf, Tomato two spotted spider mites leaf. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: Corn leaf blight, Tomato Early blight leaf, Potato leaf early blight, Potato leaf late blight, Blueberry leaf, grape leaf black rot, Bell_pepper leaf spot, Cherry leaf, Peach leaf, Soyabean leaf, Strawberry leaf, Apple Scab Leaf, Corn rust leaf, Apple leaf, Corn Gray leaf spot, Tomato leaf mosaic virus, Tomato mold leaf, Tomato leaf yellow virus, Tomato leaf bacterial spot, Tomato leaf late blight, Squash Powdery mildew leaf, Bell_pepper leaf, grape leaf, Apple rust leaf, Tomato Septoria leaf spot, Tomato leaf, Raspberry leaf, Potato leaf, Tomato two spotted spider mites leaf). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -6928,7 +7180,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 12786 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Anthracnose, Anthracnose lesions, Black Rot, Downey mildew, Downy mildew, Eggplant Cercopora leaf spot, Eggplant begomovirus, Eggplant fresh leaf, Eggplant verticillium wilt, Fresh leaf, Fusarium wilt, Mosaic virus, Tomato Bacterial spot, Tomato Fresh leaf, Tomato leaf curl virus, Tomato spotted wilt. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anthracnose, Anthracnose lesions, Black Rot, Downey mildew, Downy mildew, Eggplant Cercopora leaf spot, Eggplant begomovirus, Eggplant fresh leaf, Eggplant verticillium wilt, Fresh leaf, Fusarium wilt, Mosaic virus, Tomato Bacterial spot, Tomato Fresh leaf, Tomato leaf curl virus, Tomato spotted wilt), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6959,7 +7212,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 12786 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Anthracnose, Anthracnose lesions, Black Rot, Downey mildew, Downy mildew, Eggplant Cercopora leaf spot, Eggplant begomovirus, Eggplant fresh leaf, Eggplant verticillium wilt, Fresh leaf, Fusarium wilt, Mosaic virus, Tomato Bacterial spot, Tomato Fresh leaf, Tomato leaf curl virus, Tomato spotted wilt. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anthracnose, Anthracnose lesions, Black Rot, Downey mildew, Downy mildew, Eggplant Cercopora leaf spot, Eggplant begomovirus, Eggplant fresh leaf, Eggplant verticillium wilt, Fresh leaf, Fusarium wilt, Mosaic virus, Tomato Bacterial spot, Tomato Fresh leaf, Tomato leaf curl virus, Tomato spotted wilt), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -6986,7 +7240,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5539 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: black_grass, charlock, cleavers, common_chickweed, common_wheat, fat_hen, loose_silkybent, maize, scentless_mayweed, shepherds_purse, smallflowered_cranesbill, sugar_beet. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: black_grass, charlock, cleavers, common_chickweed, common_wheat, fat_hen, loose_silkybent, maize, scentless_mayweed, shepherds_purse, smallflowered_cranesbill, sugar_beet), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7025,7 +7280,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 55448 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Apple___Apple_scab, Apple___Black_rot, Apple___Cedar_apple_rust, Apple___healthy, Background_without_leaves, Blueberry___healthy, Cherry___Powdery_mildew, Cherry___healthy, Corn___Cercospora_leaf_spot Gray_leaf_spot, Corn___Common_rust, Corn___Northern_Leaf_Blight, Corn___healthy, Grape___Black_rot, Grape___Esca_(Black_Measles), Grape___Leaf_blight_(Isariopsis_Leaf_Spot), Grape___healthy, Orange___Haunglongbing_(Citrus_greening), Peach___Bacterial_spot, Peach___healthy, Pepper,_bell___Bacterial_spot, Pepper,_bell___healthy, Potato___Early_blight, Potato___Late_blight, Potato___healthy, Raspberry___healthy, Soybean___healthy, Squash___Powdery_mildew, Strawberry___Leaf_scorch, Strawberry___healthy, Tomato___Bacterial_spot, Tomato___Early_blight, Tomato___Late_blight, Tomato___Leaf_Mold, Tomato___Septoria_leaf_spot, Tomato___Spider_mites Two-spotted_spider_mite, Tomato___Target_Spot, Tomato___Tomato_Yellow_Leaf_Curl_Virus, Tomato___Tomato_mosaic_virus, Tomato___healthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Apple___Apple_scab, Apple___Black_rot, Apple___Cedar_apple_rust, Apple___healthy, Background_without_leaves, Blueberry___healthy, Cherry___Powdery_mildew, Cherry___healthy, Corn___Cercospora_leaf_spot Gray_leaf_spot, Corn___Common_rust, Corn___Northern_Leaf_Blight, Corn___healthy, Grape___Black_rot, Grape___Esca_(Black_Measles), Grape___Leaf_blight_(Isariopsis_Leaf_Spot), Grape___healthy, Orange___Haunglongbing_(Citrus_greening), Peach___Bacterial_spot, Peach___healthy, Pepper,_bell___Bacterial_spot, Pepper,_bell___healthy, Potato___Early_blight, Potato___Late_blight, Potato___healthy, Raspberry___healthy, Soybean___healthy, Squash___Powdery_mildew, Strawberry___Leaf_scorch, Strawberry___healthy, Tomato___Bacterial_spot, Tomato___Early_blight, Tomato___Late_blight, Tomato___Leaf_Mold, Tomato___Septoria_leaf_spot, Tomato___Spider_mites Two-spotted_spider_mite, Tomato___Target_Spot, Tomato___Tomato_Yellow_Leaf_Curl_Virus, Tomato___Tomato_mosaic_virus, Tomato___healthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7053,7 +7309,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3554 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Dead Leaf, Healthy Fruit, Healthy Leaf, Insect Hole, Unhealthy Fruit, Yellow. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Dead Leaf, Healthy Fruit, Healthy Leaf, Insect Hole, Unhealthy Fruit, Yellow), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -7079,7 +7336,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2178 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Colletotrichum spp, Ectomyelois ceratoniae, Healthy, Sunburn. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Colletotrichum spp, Ectomyelois ceratoniae, Healthy, Sunburn), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7105,7 +7363,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2178 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Colletotrichum spp, Ectomyelois ceratoniae, Healthy, Sunburn. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Colletotrichum spp, Ectomyelois ceratoniae, Healthy, Sunburn), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7131,7 +7390,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5099 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Alternaria, Anthracnose, Bacterial_Blight, Cercospora, Healthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Alternaria, Anthracnose, Bacterial_Blight, Cercospora, Healthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -7157,7 +7417,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5857 images\n\nqwen detection prompt: 'Locate every instance of the bud, flower, early-fruit, mid-growth, mature in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: bud, flower, early-fruit, mid-growth, mature). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7183,7 +7444,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5857 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the bud, flower, early-fruit, mid-growth, mature. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: bud, flower, early-fruit, mid-growth, mature). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -7209,7 +7471,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1080 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: grade-1, grade-2, grade-3. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: grade-1, grade-2, grade-3), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7235,7 +7498,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1080 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: grade-1, grade-2, grade-3. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: grade-1, grade-2, grade-3), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7261,7 +7525,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 982 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Major Defect, Minor Defect, No Defect. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Major Defect, Minor Defect, No Defect), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -7287,7 +7552,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3076 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Bacteria, Fungi, Healthy, Nematode, Pest, Phytopthora, Virus. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bacteria, Fungi, Healthy, Nematode, Pest, Phytopthora, Virus), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7313,7 +7579,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3076 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Bacteria, Fungi, Healthy, Nematode, Pest, Phytopthora, Virus. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bacteria, Fungi, Healthy, Nematode, Pest, Phytopthora, Virus), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -7339,7 +7606,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2801 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Black leaf spot, Downey mildew, Fresh leaf, Mosaic virus, flea beetle. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Black leaf spot, Downey mildew, Fresh leaf, Mosaic virus, flea beetle), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7365,7 +7633,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2801 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Black leaf spot, Downey mildew, Fresh leaf, Mosaic virus, flea beetle. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Black leaf spot, Downey mildew, Fresh leaf, Mosaic virus, flea beetle), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7389,7 +7658,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 17509 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: chinee_apple, lantana, negative, parkinsonia, parthenium, prickly_acacia, rubber_vine, siam_weed, snake_weed. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: chinee_apple, lantana, negative, parkinsonia, parthenium, prickly_acacia, rubber_vine, siam_weed, snake_weed), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -7413,7 +7683,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 17509 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: chinee_apple, lantana, negative, parkinsonia, parthenium, prickly_acacia, rubber_vine, siam_weed, snake_weed. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: chinee_apple, lantana, negative, parkinsonia, parthenium, prickly_acacia, rubber_vine, siam_weed, snake_weed), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7439,7 +7710,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2769 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Healthy, Insect, Leaf Scald, Rice, Rice Blast, Rice Leaffolder, Rice Stripes, Rice Tungro. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Healthy, Insect, Leaf Scald, Rice, Rice Blast, Rice Leaffolder, Rice Stripes, Rice Tungro), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -7465,7 +7737,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3632 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Alternanthera philoxeroide, Centella asiatica, Commelina benghalensis, Cyperus ochraceus, Fimbristylis littoralis, Ipomoea aquatic, Marsilea minuta, Panicum repens, Paspalum scrobiculatum, Pteris vittata, Synedrella nodiflora. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Alternanthera philoxeroide, Centella asiatica, Commelina benghalensis, Cyperus ochraceus, Fimbristylis littoralis, Ipomoea aquatic, Marsilea minuta, Panicum repens, Paspalum scrobiculatum, Pteris vittata, Synedrella nodiflora), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7491,7 +7764,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3632 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Alternanthera philoxeroide, Centella asiatica, Commelina benghalensis, Cyperus ochraceus, Fimbristylis littoralis, Ipomoea aquatic, Marsilea minuta, Panicum repens, Paspalum scrobiculatum, Pteris vittata, Synedrella nodiflora. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Alternanthera philoxeroide, Centella asiatica, Commelina benghalensis, Cyperus ochraceus, Fimbristylis littoralis, Ipomoea aquatic, Marsilea minuta, Panicum repens, Paspalum scrobiculatum, Pteris vittata, Synedrella nodiflora), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -7517,7 +7791,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 4730 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Amon, BR28, BR29, Bashful, Bashmoti, Chinigura_Polao, Ganjiya, Gutisharna, Jirashail, Katari_Polao, Katarivog, Lal_Aush, Lal_Binni, Lal_Biroi, Najirshail, Paijam, Red_Cargo, Shampakatari, Shorna5, Subol_Lota. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Amon, BR28, BR29, Bashful, Bashmoti, Chinigura_Polao, Ganjiya, Gutisharna, Jirashail, Katari_Polao, Katarivog, Lal_Aush, Lal_Binni, Lal_Biroi, Najirshail, Paijam, Red_Cargo, Shampakatari, Shorna5, Subol_Lota), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7543,7 +7818,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 4730 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Amon, BR28, BR29, Bashful, Bashmoti, Chinigura_Polao, Ganjiya, Gutisharna, Jirashail, Katari_Polao, Katarivog, Lal_Aush, Lal_Binni, Lal_Biroi, Najirshail, Paijam, Red_Cargo, Shampakatari, Shorna5, Subol_Lota. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Amon, BR28, BR29, Bashful, Bashmoti, Chinigura_Polao, Ganjiya, Gutisharna, Jirashail, Katari_Polao, Katarivog, Lal_Aush, Lal_Binni, Lal_Biroi, Najirshail, Paijam, Red_Cargo, Shampakatari, Shorna5, Subol_Lota), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7569,7 +7845,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3829 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Bacterial_Leaf_Blight, Brown_Spot, Healthy_Rice_Leaf, Leaf_Blast, Leaf_Scald, Sheath_Blight. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bacterial_Leaf_Blight, Brown_Spot, Healthy_Rice_Leaf, Leaf_Blast, Leaf_Scald, Sheath_Blight), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -7595,7 +7872,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5932 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Bacterialblight, Blast, Brownspot, Tungro. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bacterialblight, Blast, Brownspot, Tungro), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7621,7 +7899,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5932 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Bacterialblight, Blast, Brownspot, Tungro. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bacterialblight, Blast, Brownspot, Tungro), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -7647,7 +7926,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5364 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: clustered, single, undefined. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: clustered, single, undefined), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7673,7 +7953,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5364 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: clustered, single, undefined. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: clustered, single, undefined), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -7699,7 +7980,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 19000 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: BD30, BD33, BD39, BD49, BD51, BD52, BD56, BD57, BD70, BD72, BD75, BD76, BD79, BD85, BD87, BD91, BD93, BD95, BR22, BR23, BRRI102, BRRI67, BRRI74, Binadhan10, Binadhan11, Binadhan12, Binadhan14, Binadhan16, Binadhan17, Binadhan19, Binadhan20, Binadhan21, Binadhan23, Binadhan24, Binadhan25, Binadhan26, Binadhan7, Binadhan8. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: BD30, BD33, BD39, BD49, BD51, BD52, BD56, BD57, BD70, BD72, BD75, BD76, BD79, BD85, BD87, BD91, BD93, BD95, BR22, BR23, BRRI102, BRRI67, BRRI74, Binadhan10, Binadhan11, Binadhan12, Binadhan14, Binadhan16, Binadhan17, Binadhan19, Binadhan20, Binadhan21, Binadhan23, Binadhan24, Binadhan25, Binadhan26, Binadhan7, Binadhan8), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7725,7 +8007,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 19000 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: BD30, BD33, BD39, BD49, BD51, BD52, BD56, BD57, BD70, BD72, BD75, BD76, BD79, BD85, BD87, BD91, BD93, BD95, BR22, BR23, BRRI102, BRRI67, BRRI74, Binadhan10, Binadhan11, Binadhan12, Binadhan14, Binadhan16, Binadhan17, Binadhan19, Binadhan20, Binadhan21, Binadhan23, Binadhan24, Binadhan25, Binadhan26, Binadhan7, Binadhan8. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: BD30, BD33, BD39, BD49, BD51, BD52, BD56, BD57, BD70, BD72, BD75, BD76, BD79, BD85, BD87, BD91, BD93, BD95, BR22, BR23, BRRI102, BRRI67, BRRI74, Binadhan10, Binadhan11, Binadhan12, Binadhan14, Binadhan16, Binadhan17, Binadhan19, Binadhan20, Binadhan21, Binadhan23, Binadhan24, Binadhan25, Binadhan26, Binadhan7, Binadhan8), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7751,7 +8034,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3520 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: anomalous, occluded, ripe, unripe. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: anomalous, occluded, ripe, unripe), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -7777,7 +8061,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3520 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: anomalous, occluded, ripe, unripe. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: anomalous, occluded, ripe, unripe), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -7805,7 +8090,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 6010 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Large, Medium, Small, Spoiled. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Large, Medium, Small, Spoiled), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7833,7 +8119,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 6010 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Large, Medium, Small, Spoiled. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Large, Medium, Small, Spoiled), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -7859,7 +8146,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 4312 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: BroadLeafWeed, Grass, Sorghum. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: BroadLeafWeed, Grass, Sorghum), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7885,7 +8173,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 4312 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: BroadLeafWeed, Grass, Sorghum. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: BroadLeafWeed, Grass, Sorghum), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7911,7 +8200,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5513 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Broken, Immature, Intact, Skin-damaged, Spotted. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Broken, Immature, Intact, Skin-damaged, Spotted), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -7937,7 +8227,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 6410 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Caterpillar, Diabrotica_speciosa, Healthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Caterpillar, Diabrotica_speciosa, Healthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7963,7 +8254,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 6410 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Caterpillar, Diabrotica_speciosa, Healthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Caterpillar, Diabrotica_speciosa, Healthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -7989,7 +8281,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1176 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Bacterial leaf Blight, Dry_leaf, Healthy, Root_images, Septoria_Brown_Spot, Vein Necrosis. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bacterial leaf Blight, Dry_leaf, Healthy, Root_images, Septoria_Brown_Spot, Vein Necrosis), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8015,7 +8308,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 6410 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Caterpillar, Diabrotica speciosa, Healthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Caterpillar, Diabrotica speciosa, Healthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -8041,7 +8335,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 6410 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Caterpillar, Diabrotica speciosa, Healthy. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Caterpillar, Diabrotica speciosa, Healthy), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -8067,7 +8362,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 12683 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Anjasmoro, Dega, Grobogan. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anjasmoro, Dega, Grobogan), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8093,7 +8389,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 12683 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Anjasmoro, Dega, Grobogan. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anjasmoro, Dega, Grobogan), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -8119,7 +8416,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 15336 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: broadleaf, grass, soil, soybean. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: broadleaf, grass, soil, soybean), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8145,7 +8443,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 15336 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: broadleaf, grass, soil, soybean. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: broadleaf, grass, soil, soybean), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -8171,7 +8470,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 175 images\n\nqwen detection prompt: 'Locate every instance of the strawberry, flower, green, large_white, pink, red, small_white in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: strawberry, flower, green, large_white, pink, red, small_white). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8197,7 +8497,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 175 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the strawberry, flower, green, large_white, pink, red, small_white. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: strawberry, flower, green, large_white, pink, red, small_white). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -8223,7 +8524,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 204 images\n\nqwen detection prompt: 'Locate every instance of the strawberry, flower, green, lw, pink, red, sw in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: strawberry, flower, green, lw, pink, red, sw). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8249,7 +8551,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 204 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the strawberry, flower, green, lw, pink, red, sw. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: strawberry, flower, green, lw, pink, red, sw). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -8275,7 +8578,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 153 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: cracked, crushed, no_buds, no_damage, single_damaged_buds, two_buds. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: cracked, crushed, no_buds, no_damage, single_damaged_buds, two_buds), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8301,7 +8605,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 153 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: cracked, crushed, no_buds, no_damage, single_damaged_buds, two_buds. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: cracked, crushed, no_buds, no_damage, single_damaged_buds, two_buds), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -8327,7 +8632,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 6748 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Banded Chlorosis, Brown Spot, BrownRust, Dried, Grassy shoot, Healthy, Pokkah Boeng, Sett Rot, Smut, Viral Disease, Yellow Leaf. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Banded Chlorosis, Brown Spot, BrownRust, Dried, Grassy shoot, Healthy, Pokkah Boeng, Sett Rot, Smut, Viral Disease, Yellow Leaf), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8353,7 +8659,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 6748 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Banded Chlorosis, Brown Spot, BrownRust, Dried, Grassy shoot, Healthy, Pokkah Boeng, Sett Rot, Smut, Viral Disease, Yellow Leaf. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Banded Chlorosis, Brown Spot, BrownRust, Dried, Grassy shoot, Healthy, Pokkah Boeng, Sett Rot, Smut, Viral Disease, Yellow Leaf), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8379,7 +8686,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1649 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the Sunflower, Backside-Sunflower. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: Sunflower, Backside-Sunflower). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8405,7 +8713,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2358 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Downy_mildew, Fresh_leaf, Gray_mold, Leaf_scars. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Downy_mildew, Fresh_leaf, Gray_mold, Leaf_scars), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -8431,7 +8740,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 18248 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Early_Blight, Healthy, Late_Blight, Mid_Blight. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Early_Blight, Healthy, Late_Blight, Mid_Blight), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8457,7 +8767,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 18248 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Early_Blight, Healthy, Late_Blight, Mid_Blight. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Early_Blight, Healthy, Late_Blight, Mid_Blight), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -8483,7 +8794,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5867 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: algal_spot, brown_blight, gray_blight, healthy, helopeltis, red_spot. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: algal_spot, brown_blight, gray_blight, healthy, helopeltis, red_spot), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8509,7 +8821,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 5867 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: algal_spot, brown_blight, gray_blight, healthy, helopeltis, red_spot. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: algal_spot, brown_blight, gray_blight, healthy, helopeltis, red_spot), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8535,7 +8848,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3960 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Healthy, Tea leaf blight, Tea red leaf spot, Tea red scab. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Healthy, Tea leaf blight, Tea red leaf spot, Tea red scab), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -8561,7 +8875,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3960 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Healthy, Tea leaf blight, Tea red leaf spot, Tea red scab. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Healthy, Tea leaf blight, Tea red leaf spot, Tea red scab), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8589,7 +8904,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3941 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Anthracnose, Caterpillars_Infestation, Cercospora_Leaf, Citrus, Healthy_Leaf, Heathy_Leaf, Leaf_Curl, Leaf_Spot, Sooty_Mold. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anthracnose, Caterpillars_Infestation, Cercospora_Leaf, Citrus, Healthy_Leaf, Heathy_Leaf, Leaf_Curl, Leaf_Spot, Sooty_Mold), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -8617,7 +8933,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3941 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Anthracnose, Caterpillars_Infestation, Cercospora_Leaf, Citrus, Healthy_Leaf, Heathy_Leaf, Leaf_Curl, Leaf_Spot, Sooty_Mold. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anthracnose, Caterpillars_Infestation, Cercospora_Leaf, Citrus, Healthy_Leaf, Heathy_Leaf, Leaf_Curl, Leaf_Spot, Sooty_Mold), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8643,7 +8960,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 520 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the green, red. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: green, red). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8669,7 +8987,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 11000 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Bacterial Spot, Early Blight, Healthy, Late Blight, Leaf Mold, Septoria Leaf Spot, Spider Mites Two-spotted Spider Mite, Target Spot, Tomato Mosaic Virus, Tomato Yellow Leaf Curl Virus. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bacterial Spot, Early Blight, Healthy, Late Blight, Leaf Mold, Septoria Leaf Spot, Spider Mites Two-spotted Spider Mite, Target Spot, Tomato Mosaic Virus, Tomato Yellow Leaf Curl Virus), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -8695,7 +9014,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 11000 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Bacterial Spot, Early Blight, Healthy, Late Blight, Leaf Mold, Septoria Leaf Spot, Spider Mites Two-spotted Spider Mite, Target Spot, Tomato Mosaic Virus, Tomato Yellow Leaf Curl Virus. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bacterial Spot, Early Blight, Healthy, Late Blight, Leaf Mold, Septoria Leaf Spot, Spider Mites Two-spotted Spider Mite, Target Spot, Tomato Mosaic Virus, Tomato Yellow Leaf Curl Virus), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8721,7 +9041,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1000 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Immature, Mature. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Immature, Mature), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -8747,7 +9068,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1000 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Immature, Mature. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Immature, Mature), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8773,7 +9095,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1986 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Fresh, Rotten. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Fresh, Rotten), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -8799,7 +9122,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1986 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Fresh, Rotten. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Fresh, Rotten), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8825,7 +9149,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 804 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the b_fully_ripened, b_half_ripened, b_green, l_fully_ripened, l_half_ripened, l_green. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: b_fully_ripened, b_half_ripened, b_green, l_fully_ripened, l_half_ripened, l_green). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8857,7 +9182,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 4319 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Bougainvillea, Crown of thorns, Hibiscus, Jungle geranium, Madagascar periwinkle, Marigold, Rose. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Bougainvillea, Crown of thorns, Hibiscus, Jungle geranium, Madagascar periwinkle, Marigold, Rose), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8883,7 +9209,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1063 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Dry Leaf, Healthy Leaf, Leaf Blotch, Rhizome Disease Root, Rhizome Healthy Root. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Dry Leaf, Healthy Leaf, Leaf Blotch, Rhizome Disease Root, Rhizome Healthy Root), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -8909,7 +9236,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1063 images\n\nqwen classification prompt: 'Classify the image into exactly one of the following categories: Dry Leaf, Healthy Leaf, Leaf Blotch, Rhizome Disease Root, Rhizome Healthy Root. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Dry Leaf, Healthy Leaf, Leaf Blotch, Rhizome Disease Root, Rhizome Healthy Root), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=256, batch_size=32. Chat template kwargs: {'enable_thinking': False}. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). Vision-tower input resolution used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8935,7 +9263,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 865 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Aphids_Disease, Blotch, Healthy_Leaf, Leaf_Spot. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Aphids_Disease, Blotch, Healthy_Leaf, Leaf_Spot), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "qwen/Qwen3.6-27B-FP8",
@@ -8963,7 +9292,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2801 images\n\nqwen detection prompt: 'Locate every instance of the bean, leek, maize, stem_bean, stem_leek, stem_maize in the image. Output a JSON list where each entry contains the bounding box in \"bbox_2d\" and a text label in \"label\" (one of: bean, leek, maize, stem_bean, stem_leek, stem_maize). The bbox_2d coordinates are [x1, y1, x2, y2], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 20, 'min_p': 0.0, 'presence_penalty': 0.0, 'repetition_penalty': 1.0}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). Vision-tower input resolution (orthogonal to box coordinates) used min_pixels=784, max_pixels=78400000, factor=32 (see resolve_image_processor_config). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -8991,7 +9321,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 2801 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the bean, leek, maize, stem_bean, stem_leek, stem_maize. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: bean, leek, maize, stem_bean, stem_leek, stem_maize). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -9017,7 +9348,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 3866 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Leafroll 3, No Virus, Other Red, Red Blotch. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Leafroll 3, No Virus, Other Red, Red Blotch), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -9043,7 +9375,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1155 images\n\ngemma classification prompt: 'Classify the image into exactly one of the following categories: Anthracnose, Downy_Mildew, Healthy, Mosaic_Virus. Respond with a JSON object containing a single key \"label\" whose value is the single best-matching category name, exactly as written above (one of: Anthracnose, Downy_Mildew, Healthy, Mosaic_Virus), and nothing else.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=256, batch_size=32. Predicted labels are extracted from a JSON {\"label\": ...} response and matched to the dataset's category names by exact string match only (see parse_predicted_label); samples with no exact match are excluded from metrics (see metrics['skipped_samples']). f1/precision/recall are macro-averaged multiclass metrics (see compute_classification_metrics)."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -9076,7 +9409,8 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 1120 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the Blackbean, Canola, Corn, Field Pea, Flax, Horseweed, Kochia, Lentil, Ragweed, Redroot Pigweed, Soybean, Sugar beet, Waterhemp. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: Blackbean, Canola, Corn, Field Pea, Flax, Horseweed, Kochia, Lentil, Ragweed, Redroot Pigweed, Soybean, Sugar beet, Waterhemp). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   },
   {
     "model": "google/gemma-4-12b-it",
@@ -9102,6 +9436,7 @@
     "optimized": false,
     "platform": "cuda",
     "splitBreakdown": "-/100/-",
-    "datasetConfig": "raw"
+    "datasetConfig": "raw",
+    "notes": "Zero-shot · evaluated on 6512 images\n\ngemma detection prompt: 'Detect the 2d bounding boxes of the Wheat Head. Output a JSON list where each entry contains the 2D bounding box in \"box_2d\" and a text label in \"label\" (one of: Wheat Head). The box_2d coordinates are [y_min, x_min, y_max, x_max], normalized to 0-1000.'. Generation sampling params (vLLM): {'temperature': 1.0, 'top_p': 0.95, 'top_k': 64}, max_tokens=2048, batch_size=32. Box predictions are normalized to a 0-1000 grid (BOX_SCALE=1000) relative to the original image and rescaled to pixel coordinates before matching (see detections_to_prediction). precision_at_iou50/recall_at_iou50/f1_at_iou50 come from greedy same-class box matching at IoU>=0.5 (see match_detections); no mAP is computed since neither model emits a per-box confidence score to rank predictions by."
   }
 ]
\ No newline at end of file