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CASM-AMFMNet: A Network Based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases

文献类型： 外文期刊

作者： Suo, Jiayu 1 ; Zhan, Jialei 1 ; Zhou, Guoxiong 1 ; Chen, Aibin 1 ; Hu, Yaowen 1 ; Huang, Weiqi 1 ; Cai, Weiwei 1 ; Hu, Yahui 2 ; Li, Liujun 3 ;

作者机构： 1.Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha, Peoples R China

2.Hunan Acad Agr Sci HNAAS, Plant Protect Res Inst, Changsha, Peoples R China

3.Missouri Univ Sci & Technol, Dept Civil Architectural & Environm Engn, Rolla, MO USA

关键词： CASM-AMFMNet; coordinate attention shuffle mechanism asymmetric; multi-scale fusion module; grape leaf diseases; GSSL; image enhancement

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： Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases.

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