CottonYield is an AI-powered smart agriculture system developed to help farmers to estimate cotton yield accurately using image processing and deep learning techniques. The system analyzes images or videos of cotton plants to detect, count, and classify cotton bolls while also estimating overall crop yield. YOLOv8 is used to detect and count cotton bolls with bounding boxes, EfficientNet is used to classify boll health as Healthy or Weak, and Random Forest Regressor is used for yield estimation. The platform supports three analysis modes including Single Plant Analysis, Full Plant Analysis, and Whole Field Analysis. Users can upload plant images or videos through a simple web application that provides real-time analysis results, historical reports, and dashboard analytics. The system also includes secure user authentication with email and Google login, along with an AI chatbot to assist farmers with agriculture-related guidance. CottonYield is developed using React for the frontend, Node.js and Express for backend services, Python and FastAPI for AI processing, and MySQL for database management, while Cloudinary is used for image storage and Docker is used for deployment. The system helps farmers to know their cotton yield at early stage, so they can plan better and earn maximum.
Tools: VS Code,Docker Files,Python,React,TailwindCSS,Node.js,Pillow,TensorFlow
Department: Department of Computer Science
Poster
