A two-stage feature aggregation network for multi-category soybean leaf disease identification

Abstract

Accurate identification of soybean leaf disease is of utmost importance for its cultivation and fine management, as it is a critical factor contributing to the decreased quality and yield of soybean. Nevertheless, several existing studies on the identification of soybean leaf disease encounter the challenge of striking a balance between model expressiveness and practical applicability. To address such issue, we propose a well-designed two-stage feature aggregation network framework (TFANet). The main component of it is the two-stage feature aggregation (TFA) module, which is designed to achieve great feature representation capability by aggregating feature information from different convolutional layers in two stages. Meanwhile, TFA module incorporates the efficient channel attention to extract crucial disease information. Afterwards, to limit the loss of feature information and expand the receptive field, a dilated convolution-based feature fusion (DCFF) module is developed. Additionally, the InceptionC module is introduced to further achieve better performance. The experimental results demonstrated that TFANet obtained an accuracy of 98.18% and an F1 scores of 98.39%, with merely 1.18 M parameters. In comparison with some classic convolutional neural network models, TFANet showed remarkable superiority in multiple evaluation metrics. Hence, TFANet has great potential to be used in practical soybean leaf disease identification.