A dataset with Space (Sentinel-1/2) and Ground (street-level images) components, annotated with crop-type labels for agriculture monitoring.

A crop monitoring system made using a Raspberry Pi 4B, a 7-in-1 NPK sensor, an ultrasonic sensor, and an ESP32 Wi-Fi module.

A curated collection of 45 high-quality RGB image datasets for computer vision in agriculture. Features datasets for weed detection, disease identification, and crop monitoring, focusing on natural field scenes. Part of our GIL 2025 survey paper.

AI-powered plant disease detection system using deep learning. Upload plant images to instantly identify 30+ diseases across Apple, Corn, Grape, Potato, Tomato & more crops. Built with FastAPI + React TypeScript. Ready for cloud deployment.

CS3282 - Industrial Computer Engineering Project. Helps farmers to apply the right amount of fertilizers to the fields.

Corn Health Monitoring System (Designed for Ceylon Biscuit Limited) using Aerial Imagery

Sugar Beet Leaf Damage Regression Model for Smart Plant Monitoring

DroneUI is a Python-based system that allows farmers to manually control a DJI Tello EDU drone, capture video of tomato crops, and automatically detect signs of leaf disease using a custom-trained YOLOv11 model. It features a user-friendly interface built with PyQt6 and generates detailed flight reports with visual and statistical summaries.

This project uses alternative data to monitor economic trends in Niger following the 2023 coup and civil unrest. It analyzes indicators such as conflict events, nighttime lights, crop conditions, and population movement patterns using geospatial, mobility, and remote sensing data to assess the impacts of conflict and instability.

AI-powered smart agricultural IoT monitoring system using Raspberry Pi sensors and Llama 3.2 LLM for offline crop management. Provides real-time environmental data analysis and natural language farming insights without internet dependency.

Solution IA dédiée à la surveillance des cultures tropicales, permettant la détection automatique de la mosaïque du manioc et des dégâts causés par la chenille légionnaire d'automne sur le maïs grâce à la vision par ordinateur et à YOLOv11. — https://huggingface.co/kjd-dktech/agbledo01

Agro-Vision – Multilingual AI Dashboard for Indian Farmers | Crop Health, Weather, Market Prices | AgriTech + AI | NDVI Satellite Integration

A website to monitor crop through collected data on Firebase.

Python tool for displaying time series of Radar backscatter and NDVI values in a web app.

Precision agricultural intelligence for small and medium farms — drone imagery + ancestral knowledge, validated with sensor data. WhatsApp-first, Spanish. Mexico + Canada.

This project develops a Convolutional Neural Network (CNN) model to automatically classify vine leaf images as healthy or diseased. The system was created to help Grape Valley Winery improve grape quality by enabling early detection of leaf diseases, reducing agricultural losses, and promoting sustainable vineyard monitoring practices.

An iOS application to monitor crops through collected data.

This project uses alternative data to monitor economic and poverty trends in Ethiopia. It analyzes nighttime lights, air pollution, crop productivity, conflict events, Google search trends, and crowd-sourced survey data to track economic activity, agricultural conditions, and societal responses to conflict and crisis.

AI-Powered Agronomist Decision Support System integrating GIS, Satellite Remote Sensing, Weather Intelligence, and Generative AI for precision agriculture.

Django web application for monitoring waterlogging risk in agricultural fields using Sentinel-1 satellite data