Crop leaf disease recognition based on Self-Attention convolutional neural network

Abstract

The characteristics of the complex background in crop disease image, the small disease area, and the small contrast between the disease region and the background that easily causes confusion between them, seriously affect the recognition robustness and accuracy. To address these issues, we propose a Self-Attention Convolutional Neural Network (SACNN), which extracts effective features of crop disease spots to identify crop diseases. Our SACNN includes a basic network and a self-attention network: the basic network is for extracting the global features of the image, and the self-attention network is for obtaining the local features of the lesion area. Extensive experimental results show that the recognition accuracy of SACNN on AES-CD9214 and MK-D2 is 95.33% and 98.0%, respectively. The recognition accuracy of SACNN on MK-D2 has outperformed the state-of-the-art method by 2.9%, which implies that the CNN with self-attention can focus on the important areas of the image, and thus can improve the recognition accuracy. Adding different levels of noise to the AES-CD9214 test set shows the anti-interference ability and the strong robustness of SACNN. In addition, we discuss the influence of the location selection, channel size setting, network number and other aspects of the self-attention network on the recognition performance, in order to show the self-attention network working mechanism and provide inspiration for future research.