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Disease diagnostic method based on cascade backbone network for apple leaf disease classification

文献类型： 外文期刊

作者： Sheng, Xing 1 ; Wang, Fengyun 1 ; Ruan, Huaijun 1 ; Fan, Yangyang 1 ; Zheng, Jiye 1 ; Zhang, Yangyang 2 ; Lyu, Chen 2 ;

作者机构： 1.Shandong Acad Agr Sci, Inst Agr Informat & Econ, Jinan, Peoples R China

2.Shandong Normal Univ, Sch Informat Sci & Engn, Jinan, Peoples R China

关键词： cascade decoder; cascade backbone network; Transformer; applet; disease classification

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：6.627； 五年影响因子：7.255 ）

ISSN： 1664-462X

年卷期： 2022 年 13 卷

页码：

收录情况： SCI

摘要： 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.

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