Detection and Categorization of Tomato Plant Diseases using A Convolutional Neural Network

Early disease diagnosis and classification in tomato plants may save farmers money on crop treatments and lead to enhanced food output. There has been a lot of work done by researchers to categorize tomato plant illnesses, but it is still difficult to quickly identify many tomato leaf diseases since healthy and diseased regions of the plant's leaves seem so similar. Convolution Neural Network (CNN) is a powerful deep learning (DL) approach for disease classification in tomato plant leaves that we have developed to overcome the concerns mentioned above. The Plant Village Kaggle dataset, which is often used and readily accessible, was utilized. The proposed method provides a low-cost, image-resilient solution for tomato leaf disease classification that holds up under different lighting conditions, different colors, and different orientations of the affected area. Upon evaluating the suggested CNN model across many parameters, it attains an accuracy rate of 95%.