Research on crop disease recognition based on Multi-Branch ResNet-18

Abstract

Traditional image processing has many problems in crop disease identification, such as complicated manual design and low efficiency. This article studies the application of deep learning algorithms in the identification of crop diseases. In this article, attention mechanism and feature fusion are introduced to optimize ResNet-18, and for the problem that the network has only a single output, based on the optimized ResNet-18, a Multi-Branch ResNet-18(MB-ResNet-18) and a joint loss function are proposed to achieve the simultaneous classification of crop-type level, disease-type level, and disease-degree level. The experimental results show that, compared with ResNet-18, the network structure proposed in this article basically maintains the classification accuracy of crop-type level and disease-type level, and the classification accuracy on crop disease-degree level has increased by 2.11%.