leaf-disease-classification

Plant disease detection and Solution using Image Classification

This project is developed based on Convolutional Neural Networks.

🍃 Code, studies, and explorations on plant leaf diseases and leaf type classifications. Final project for UC Berkeley MIDS 207 (Machine Learning)

A CNN model for detecting leaf diseases.

Apple Tree Diseases Predication using Image Classification

AI-based plant leaf disease detection using VGG16 CNN with GUI, image upload, webcam, Telegram alerts

Tea Leaf disease Classification

U-Net segmentation algorithm with options of pretrained resnet34 and resnet50 encoders. All of the project dockerized with gpu suppport on anaconda environment with multiple loss support..

CNN model with Transfer Learning to predict bell pepper leaf disease by images using image processing

Membuat Aplikasi Leaf Diseases dengan Flutter | Deteksi Penyakit Pada Tanaman

Image classification of cassava leaf diseases. This is based on the Kaggle competition of the same title.

AI/ML based web application powered by Streamlit.

Final project for undergraduate thesis to create app that detect yellow leaf disease caused by begomovirus

a robust method of classification and recognition of coffee leaf diseases using both classical ma learning and deep learning methods, also a custom CNN. These methods were evaluated on the Arabica coffee leaf dataset known as JMuBEN.

A deep learning based system ot recognize potato leaf disease by capturing or uploading image.

NeuroLeaf is an autonomous AI-powered crop triage and dosing system built to turn plant images into actionable agricultural decisions. It combines computer vision, semantic segmentation, deep learning, fuzzy reasoning, real-time weather data, and agentic AI to detect crop diseases, estimate severity, recommend treatment, and explain the decision.

End-to-end model pipeline consisting of classification and segmentation models to classify crop diseases with high certainty.

A CNN-based AI model to detect and classify leaf diseases from images, helping farmers monitor plant health efficiently.

lighting-invariant soybean disease detection final project for the Ohio State University Computer Vision course