DEVELOPMENT OF A WEB BASED APPLICATION FOR DETECTION AND CLASSIFICATION OF TOMATO LEAF DISEASE USING CONVOLUTIONAL NEURAL NETWORK
Abstract
Tomato Leaf Disease Pose significant threat to agricultural productivity and food security, requiring efficient and accurate detection methods to minimise crop losses. This study present the development of a Convolutional Neural Network (CNN-Based System) for detecting and classifying tomato leaf diseases leveraging deep learning techniques to address limitations of traditional manual Diagnosis. Using a curated datasets of Tomato Leaf images, the system was trained to classify diseases such as early blight, Late Blight, and Bacterial Spot with with high accuracy. The model achieved a validation accuracy of 97.1% after 50 training epochs, demonstrating it effectiveness in recognizing disease patterns.
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