PyTorch Implementation and Assessment of Pre-Trained Convolutional Neural Networks for Tomato Leaf Disease Classification

Plant diseases detection and management practices are a major concern in the agriculture sector. Automating the process of plant disease detection with acceptable accuracy and speed using computer-aided systems could help develop an early diagnosis while substantially reducing economic losses. Recent advancements in deep neural networks have allowed researchers to drastically improve the accuracy of image classification and recognition systems. This paper presents a comprehensive assessment of the deep-learning approaches based on the pre-trained convolutional neural network models and the PyTorch framework to classify disease infections in tomato plants. Models such as EfficientNet-B0, ResNext-50_32x4d and MobileNet-V2, which relatively exhibit improved performance under different trade-offs, were tested using images captured with the natural background. Key performance indices were evaluated with varying hyperparameters during training and validation on a GPU-based system. The results show that ResNext-50_32x4d delivers the best accuracy of 90.14% (0.001 LR; 8 Batch Size), with the right hyperparameter optimization, and MobileNet-V2 delivers the lowest loss of 0.356 (0.001 LR; 16 Batch Size) during model validation for the given dataset and system constraints. ResNext-50_32x4d also performs better in inference than the other two models tested. The assessment performed in this paper will help researchers and developers decide on selecting an appropriate model for precision agriculture and smart farming deployments.