Tomato plants are highly susceptible to various diseases that can significantly reduce crop yield and quality. Early and accurate detection of these diseases is crucial for preventing large-scale agricultural losses. This research presents an AI-based tomato leaf disease detection system using deep learning and computer vision techniques. A Convolutional Neural Network (CNN) model is trained on a labeled dataset of tomato leaf images to classify leaves as healthy or diseased, identifying specific diseases such as Early Blight, Late Blight, and Leaf Mold. The system is integrated into a web-based application for easy accessibility, allowing farmers to upload leaf images for real-time disease diagnosis. Extensive experiments demonstrate that the proposed model achieves high accuracy (>90%), making it a reliable and efficient tool for disease detection. The study also explores potential improvements, including mobile deployment, IoT integration, and multi-crop expansion. This AI-driven solution can revolutionize modern agriculture by enabling early disease detection, reducing pesticide overuse, and improving overall crop productivity.
Python, CNN, ML, AI, Image processing, Deep learning.
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