Performance Analysis of Deep Neural Networks for Tomato Leaf Disease Classification with Server-Based Computing

In recent years, automatic plant disease recognition has gained huge interest in academia and industry. It is considered one of the promising technologies in precision agriculture. With the advancement of deep neural networks (DNNs), it is possible to develop various solutions for plant disease recognition. This paper analyzes the feasibility of using CPU-based desktop computers and GPU-based cloud-hosted services as back-end systems to develop tomato leaf disease classification models. The paper conducts a comprehensive analysis of state-of-the-art DNN architectures proposed for image classification. For each DNNs, various performance indices are measured. The attributes of these indices and their combinations are analyzed and discussed. The results show that EfficientNetBO and MobileNetV2 will provide the best results under most of the circumstances compared to other DNNs considered. In comparison with CPU-based systems, GPU-based systems perform better in almost every analysis performed in this study. The experiments conducted in this paper will help researchers and practitioners to select appropriate DNN architectures that better fit their resource constraints for practical deployment and applications.