Tomato Leaf Disease Detection and Diagnosis using Computer Vision

Leveraging computer vision technology, this project focuses on early detection and diagnosis of diseases affecting tomato plant leaves to minimize crop losses and enhance productivity in agriculture. It employs Roboflow's pre-trained model for object detection to analyze images of tomato leaves, captured via webcam, and identify various diseases such as bacterial spots, blight, and viruses. The project utilizes the Panel Python library to create a user-friendly application interface where users can upload images and receive diagnostic results, which are further analyzed by a large language model (LLM) like GPT-3.5 for detailed insights. The application is designed to assist farmers by providing real-time plant health monitoring and treatment recommendations, effectively integrating computer vision, AI-driven diagnostics, and interactive web interfaces to improve plant disease management and agricultural productivity.

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