TOMATO LEAF DISEASE DETECTION

Abstract: Tomato cultivation is susceptible to various diseases, leading to significant yield loss and economic impact. Rapid and accurate prediction is essential for timely intervention and mitigation. Deep learning techniques, specifically Convolutional Neural Networks (CNN), are applied for automated detection of tomato leaf diseases. The methodology involves acquiring high-resolution images of tomato leaves and training a CNN model to classify them into healthy or diseased categories. The dataset comprises labeled images representing Early Blight, Late Blight, and healthy leaves. The CNN architecture is optimized to achieve high accuracy, precision, recall, and F1-score. The trained model demonstrates promising results in identifying tomato leaf diseases even under environmental variations and leaf deformities. The approach also allows for near real-time detection, enabling timely agricultural interventions. This research contributes to automated agricultural monitoring systems, aiding farmers in early disease detection and management, thereby enhancing crop productivity and sustainability.

Keywords: Tomato Leaf Disease Detection, Convolutional Neural Network (CNN), Deep Learning, Image Classification, Early Blight, Late Blight, Real-time Detection, Precision Agriculture