A real-time application-based convolutional neural network approach for tomato leaf disease classification

Abstract

Early diagnosis and treatment of tomato leaf diseases increase a plant's production volume, efficiency, and quality. Misdiagnosis of disease by farmers can lead to an inadequate treatment strategy that hurts the tomato plants and agroecosystem. Therefore, it is crucial to detect the disease precisely. Finding a rapid, accurate approach to take care of the issue of misdiagnosis and early disease identification will be advantageous to the farmers. This study proposed a lightweight custom convolutional neural network (CNN) model and utilized transfer learning (TL)-based models VGG-16 and VGG-19 to classify tomato leaf diseases. In this study, eleven classes, one of which is healthy, are used to simulate various tomato leaf diseases. In addition, an ablation study has been performed in order to find the optimal parameters for the proposed model. Furthermore, evaluation metrics have been used to analyze and compare the performance of the proposed model with the TL-based model. The proposed model, by applying data augmentation techniques, has achieved the highest accuracy and recall of 95.00% among all the models. Finally, the best-performing model has been utilized in order to construct a Web-based and Android-based end-to-end (E2E) system for tomato cultivators to classify tomato leaf disease.