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.
