AI leaf disease detection system with FastAPI + Streamlit using Llama Vision (Groq) for all diseases, severity and treatment recommendations

Fight plant disease with AI! This project trains a model to identify plant diseases from leaf images. Analyze features like color and texture to catch problems early and boost crop health.

[ 🤖🌿 COMPUTER VISION ] Contain AI models and plants images processing. The programs aim to recognize from leaf pictures, the species and the disease if present.

Ai powered web app that uses computer vision to detect three types of rice leaf diseases - Tensorflow.js

Leaf Disease Detection System is an AI-powered web application that uses computer vision and large language models to diagnose plant leaf diseases from images. Built with FastAPI and modern web technologies, it provides instant disease identification, severity assessment, and treatment recommendations.

Early detection of cotton disease using AI-based systems may help to increase the production of cotton by detecting the leaf disease significantly. So we use Deep Techniques for model building and provide a best accuracy of model for Agriculture domain.

This is a university project that aims to solve an apple leaf disease detection problem. Furthermore, an Android application is implemented for the launch of the model.

Recognition of vine leaf diseases with AI

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PlanteD is an innovative plant leaf disease detection app developed using the powerful Flutter framework, combining the prowess of Artificial Intelligence (AI) and Machine Learning (ML). Designed for plant enthusiasts, gardeners, and farmers, PlanteD revolutionizes the way we identify and combat leaf diseases, ensuring healthier plants.

🍃An AI-powered computer vision system for detecting and classifying 🥔 Potato Leaf Diseases 🥔using Deep Learning Techniques.

This project leverages machine learning to detect and diagnose plant leaf diseases 📸🌱. Using advanced image processing techniques, it identifies affected areas and suggests appropriate treatments 🚑🌾. Ideal for agricultural enthusiasts and farmers looking to maintain crop health with AI technology 💡🌿.

LeafDisease-AI is the first framework for cross-domain tomato leaf disease detection, bridging the gap between laboratory research and real-world agricultural deployment. This repository implements a unified optimization approach integrating ensemble learning, knowledge distillation, and quantization for edge-compatible disease detection.

Identifying Tea Leaf diseases using AI Model and suggesting appropriate Remedies

AI-powered crop disease detection developed by Nihan. I built and trained my own AI model to identify plant diseases. Upload a photo of your plant's leaf to get instant disease diagnosis and treatment recommendations.

An AI-powered Crop Disease Detection Web App built with Flask and TensorFlow that identifies plant diseases from leaf images with high accuracy. Empowering farmers and researchers through deep learning–based plant health insights.

A deep learning web application built with TensorFlow, FastAPI, and LangChain that classifies potato leaf diseases and provides an AI-powered agricultural chatbot for farmers and researchers.

🌱 An advanced ViT-based deep learning pipeline in PyTorch for classifying groundnut leaf diseases with high precision. Trained on a real-world dataset from Indian farmlands, this project empowers sustainable agriculture with AI-driven plant health diagnostics.

An autonomous IoT and AI-powered robot for detecting plant diseases like powdery mildew and rust leaf in greenhouses. It uses image processing and machine learning to analyze plant health and provides real-time reports via a web interface.

Final year project

Plant Leaf Disease Detection using Ensemble Learning and Explainable AI leverages advanced machine learning models to identify plant diseases from leaf images. By combining multiple algorithms, it ensures high accuracy, while Explainable AI provides clear insights into predictions, aiding farmers in timely crop management.

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DEMETER is an AI-powered plant disease detection web app that helps farmers identify crop diseases early through image analysis. Users register, enter basic crop details, and upload leaf images. A trained deep learning model analyzes the photo and predicts disease presence, offering actionable recommendations.

AI in Agriculture: Building an Apple Leaf Disease Detection with Python and Machine Learning

Plant disease detection using AI by just scanning the leaf of the plant on real-time basis

An AI-powered Crop Disease Detection Web App built with Flask and TensorFlow that identifies plant diseases from leaf images with high accuracy. Empowering farmers and researchers through deep learning–based plant health insights.

🌿 An AI-powered web application that enables users to diagnose plant diseases by uploading leaf images. Built with MERN, FastAPI, Redis and Bull Queue. 🌱

TeaCare AI is a full-stack web application designed to help tea farmers and agronomists detect and manage tea plantation diseases. Upload a leaf image to get instant disease identification, AI-generated treatment advice, and recovery tracking - powered by YOLOv8, RAG, and deep learning.

🌱 AgriGuard : AI Smart Crop Disease Detection System A deep learning-powered web app that identifies 38+ plant diseases from leaf images with 95% accuracy. Built using TensorFlow and Streamlit, this project aims to empower farmers with instant, AI-driven diagnostics to combat global crop losses.

An AI-powered leaf disease detection system featuring a FastAPI backend and interactive Streamlit web app. Leveraging Meta’s Llama Vision models via Groq API, it accurately identifies diseases, evaluates severity, and delivers actionable treatment recommendations for agriculture and horticulture applications.