Green Shadow Crop Management System, a robust backend API crafted to streamline farm operations for Green Shadow (Pvt) Ltd.. This system supports efficient management of fields, crops, staff, and resources, enabling scalability for farms expanding nationally and globally

IoT-based soil moisture, temperature, and humidity monitoring system using ESP32, DHT22, and Blynk for smart farmingReal time crop monitoring Using IOT

Farmonaut is a frontend admin panel for a satellite-based crop monitoring and precision agriculture platform. It includes NDVI insights, weather data, field mapping, and automated crop health analysis using Sentinel imagery. This repository contains only the frontend. For the backend or API, feel free to contact me.

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.

IVDisplays Website

Low-cost UAV-based crop phenotyping using RGB imagery without GCPs or RTK-GPS. Includes dual-altitude flight strategy, RANSAC-based DEM correction, and pretrained models for plant counting, height estimation, and panicle detection across rice growth stages.

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

🍇 Classify grape leaf diseases with a CNN to enhance vineyard health, reduce losses, and enable early detection for better grape quality.

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.

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

A website to monitor crop through collected data on Firebase.

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

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.

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

Sugar Beet Leaf Damage Regression Model for Smart Plant Monitoring

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.

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.