A diagnosis model of soybean leaf diseases based on improved residual neural network

Abstract

To solve the problems of high complexity and large computation of current disease diagnosis models based on deep learning, which are difficult to be applied to commonly used portable mobile terminals, a lightweight soybean disease diagnosis model based on attention mechanism and residual neural network was proposed. The residual attention layer (RAL) was constructed by using the attention mechanism, continuous convolution layer and shortcut connection on the traditional residual neural network, and was inserted into the residual neural network (ResNet18) in order to replace the residual structural layer. And a novel residual attention network model (RANet18) was built for soybean disease diagnosis. Based on 4301 images of soybean brown leaf spot, soybean frogeye leaf spot and soybean phyllosticta leaf spot, the RANet18 was used to conduct simulation experiments. The test result showed that the recognition accuracy was 96.50%, while the recognition time was 0.047184s, and the model size was only 40.64 MB with the F1 value was 96.43. To further verify the performance of the RANet18 on the above small sample data set, a comparative test was conducted between RANet18 and the original model, and the training time of RANet18 was 49.76% less than that of ResNet18. The model size was simplified by 4.94% and the average recognition time was saved by 45.60%. This achievement constructed a fast, efficient and accurate lightweight disease recognition model suitable for small sample data sets, and provided relevant basis and guarantee for rapid diagnosis and accurate control of crop diseases.