Convolutional Neural Network Architectures for Enhanced Tomato Leaf Disease Classification using ResNet-152 and VGGNet Model

Background: Tomato is one of the most extensively grown and consumed crops in the world. Diseases, such as bacterial spots and bacterial specks, cause significant economic losses by reducing both yield and quality. These diseases damage and destroy the leaves of tomato plants, making it difficult for the plant to produce fruit. Methods: The purpose of this work is to use Convolutional Neural Network (CNN) models to diagnose diseases in tomato plants more quickly and accurately. This paper compares the ResNet-152 and VGGNet models for the classification of bacterially-induced tomato leaf diseases. Result: An accuracy of 98% is achieved using the ResNet-152 model for disease classification.