LAUSR

Fruit tree diseases are one of the major agricultural disasters in China. With the popularity of smartphones, there is a trend to use mobile devices to identify agricultural pests and diseases. In order to identify leaf diseases of apples more easily and efficiently, this paper proposes a cascade backbone network-based (CBNet) disease identification method to detect leaf diseases of apple trees in the field. The method first replaces traditional convolutional blocks with MobileViT-based convolutional blocks particularly for feature extraction. Compared with the traditional convolutional block, the MobileViT-based convolutional block is able to mine feature information in the image better. In order to refine the mined feature information, a feature refinement module is proposed in this paper. At the same time, this paper proposes a cascaded backbone network for effective fusion of features using a pyramidal cascaded multiplication operation. The results conducted on field datasets collected using mobile devices showed that the network proposed in this paper can achieve 96.76% accuracy and 96.71% F1-score. To the best of our knowledge, this paper is the first to introduce Transformer into apple leaf disease identification, and the results are promising.