A dataset with Space (Sentinel-1/2) and Ground (street-level images) components, annotated with crop-type labels for agriculture monitoring.
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Updated
Aug 2, 2022 - Jupyter Notebook
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.
CS3282 - Industrial Computer Engineering Project. Helps farmers to apply the right amount of fertilizers to the fields.
Sugar Beet Leaf Damage Regression Model for Smart Plant Monitoring
Corn Health Monitoring System (Designed for Ceylon Biscuit Limited) using Aerial Imagery
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.
A website to monitor crop through collected data on Firebase.
A new release of the previous MoniCrop iOS application, but now connected to Firebase.
Python tool for displaying time series of Radar backscatter and NDVI values in a web app.
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.
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
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.
Django web application for monitoring waterlogging risk in agricultural fields using Sentinel-1 satellite data
An iOS application to monitor crops through collected data.
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