Tomato Leaf Disease Classification Based on Feature Enhancement and SDE-ResNet50

Plant leaf diseases constitute substantial issues in agriculture. The tomato, being one of the world's most vital crops, incur enormous economic losses for farmers and impair the healthy development of the tomato sector when impacted by illnesses. Therefore, accurate detection and classification of tomato leaf diseases have become crucial. In recent years, more and more academics have begun to use deep learning approaches in the field of plant disease identification and have produced good results. However, these approaches still have limited accuracy in situations with complicated backdrops or multiple interferences and are not lightweight enough, with significant requirements on computational resources. The present study proposes an improved network model for the purpose of recognizing tomato leaf diseases. Based on the original ResNet50 model, a novel network model, SDE-ResNet50, was constructed by adding an Efficient Channel Attention（ECA）module, depth-wise separable convolutions and modifications to the stem structure. The experimental findings indicate that the SDE-ResNet50 attained a classification accuracy of 98% in the identification of tomato leaf diseases, hence outperforming ResNet50 by a margin of 5.6%. Furthermore, the SDE-ResNet50 demonstrates a decrease in size of about 47.73% and a reduction in computational complexity of roughly 70.28% when compared to the ResNet50 model. The SDE-ResNet50 was evaluated against current widely-used classification networks using an identical dataset of tomato leaf diseases, and it exhibited greater performance. The present discovery provides evidence of the efficacy and viability of the proposed enhancement to the model as outlined in this research article.