An optimized capsule neural networks for tomato leaf disease classification

Abstract

Plant diseases have a significant impact on leaves, with each disease exhibiting specific spots characterized by unique colors and locations. Therefore, it is crucial to develop a method for detecting these diseases based on spot shape, color, and location within the leaves. While Convolutional Neural Networks (CNNs) have been widely used in deep learning applications, they suffer from limitations in capturing relative spatial and orientation relationships. This paper presents a computer vision methodology that utilizes an optimized capsule neural network (CapsNet) to detect and classify ten tomato leaf diseases using standard dataset images. To mitigate overfitting, data augmentation, and preprocessing techniques were employed during the training phase. CapsNet was chosen over CNNs due to its superior ability to capture spatial positioning within the image. The proposed CapsNet approach achieved an accuracy of 96.39% with minimal loss, relying on a 0.00001 Adam optimizer. By comparing the results with existing state-of-the-art approaches, the study demonstrates the effectiveness of CapsNet in accurately identifying and classifying tomato leaf diseases based on spot shape, color, and location. The findings highlight the potential of CapsNet as an alternative to CNNs for improving disease detection and classification in plant pathology research.