Abstract
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.
Recommended Citation
J. Suo et al., "CASM-AMFMNet: A Network based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases," Frontiers in Plant Science, vol. 13, article no. 846767, Frontiers Media, May 2022.
The definitive version is available at https://doi.org/10.3389/fpls.2022.846767
Department(s)
Civil, Architectural and Environmental Engineering
Keywords and Phrases
CASM-AMFMNet; Coordinate Attention Shuffle Mechanism Asymmetric; Grape Leaf Diseases; GSSL; Image Enhancement; Multi-Scale Fusion Module
International Standard Serial Number (ISSN)
1664-462X
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2022 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
24 May 2022
Comments
This work was supported by the Scientific Research Project of Education Department of Hunan Province (Grant No. 21A0179), in part by Changsha Municipal Natural Science Foundation (Grant No. kq2014160), in part by the Natural Science Foundation of Hunan Province (Grant No. 2021JJ41087), in part by the Natural Science Foundation of China (Grant No. 61902436), and in part by Hunan Key Laboratory of Intelligent Logistics Technology (2019TP1015).