Tomato Leaf Disease Detection Using Convolution Neural Network

Plant diseases cause low agricultural productivity. Plant diseases are challenging for the majority of farmers to control and identify. To reduce future losses, early disease diagnosis is necessary. This study presents a deep learning approach for detecting tomato leaf diseases using Convolutional Neural Networks (CNNs). The proposed method involves preprocessing the tomato leaf images, followed by training the CNN model to classify them into one of ten categories: healthy, yellow leaf curl virus (YLCV), bacterial spot (BS), early blight (EB), leaf mold (LM), spectoria leaf spot (SLS), target spot (TS), two spotted spider mite spot(TSSMS), mosaic virus(MV) and late blight (LB). The data sets are collected from Kaggle. These results demonstrate the effectiveness of the proposed approach in detecting tomato leaf diseases. The automated approach can aid in the early detection and prevention of tomato diseases, which can ultimately help in improving the yield and quality of tomato crops.