Cargando…

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

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 extra...

Descripción completa

Detalles Bibliográficos
Autores principales: Suo, Jiayu, Zhan, Jialei, Zhou, Guoxiong, Chen, Aibin, Hu, Yaowen, Huang, Weiqi, Cai, Weiwei, Hu, Yahui, Li, Liujun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9171378/
https://www.ncbi.nlm.nih.gov/pubmed/35685012
http://dx.doi.org/10.3389/fpls.2022.846767
_version_ 1784721653812428800
author Suo, Jiayu
Zhan, Jialei
Zhou, Guoxiong
Chen, Aibin
Hu, Yaowen
Huang, Weiqi
Cai, Weiwei
Hu, Yahui
Li, Liujun
author_facet Suo, Jiayu
Zhan, Jialei
Zhou, Guoxiong
Chen, Aibin
Hu, Yaowen
Huang, Weiqi
Cai, Weiwei
Hu, Yahui
Li, Liujun
author_sort Suo, Jiayu
collection PubMed
description 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.
format Online
Article
Text
id pubmed-9171378
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-91713782022-06-08 CASM-AMFMNet: A Network Based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases Suo, Jiayu Zhan, Jialei Zhou, Guoxiong Chen, Aibin Hu, Yaowen Huang, Weiqi Cai, Weiwei Hu, Yahui Li, Liujun Front Plant Sci Plant Science 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. Frontiers Media S.A. 2022-05-24 /pmc/articles/PMC9171378/ /pubmed/35685012 http://dx.doi.org/10.3389/fpls.2022.846767 Text en Copyright © 2022 Suo, Zhan, Zhou, Chen, Hu, Huang, Cai, Hu and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Suo, Jiayu
Zhan, Jialei
Zhou, Guoxiong
Chen, Aibin
Hu, Yaowen
Huang, Weiqi
Cai, Weiwei
Hu, Yahui
Li, Liujun
CASM-AMFMNet: A Network Based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases
title CASM-AMFMNet: A Network Based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases
title_full CASM-AMFMNet: A Network Based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases
title_fullStr CASM-AMFMNet: A Network Based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases
title_full_unstemmed CASM-AMFMNet: A Network Based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases
title_short CASM-AMFMNet: A Network Based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases
title_sort casm-amfmnet: a network based on coordinate attention shuffle mechanism and asymmetric multi-scale fusion module for classification of grape leaf diseases
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9171378/
https://www.ncbi.nlm.nih.gov/pubmed/35685012
http://dx.doi.org/10.3389/fpls.2022.846767
work_keys_str_mv AT suojiayu casmamfmnetanetworkbasedoncoordinateattentionshufflemechanismandasymmetricmultiscalefusionmoduleforclassificationofgrapeleafdiseases
AT zhanjialei casmamfmnetanetworkbasedoncoordinateattentionshufflemechanismandasymmetricmultiscalefusionmoduleforclassificationofgrapeleafdiseases
AT zhouguoxiong casmamfmnetanetworkbasedoncoordinateattentionshufflemechanismandasymmetricmultiscalefusionmoduleforclassificationofgrapeleafdiseases
AT chenaibin casmamfmnetanetworkbasedoncoordinateattentionshufflemechanismandasymmetricmultiscalefusionmoduleforclassificationofgrapeleafdiseases
AT huyaowen casmamfmnetanetworkbasedoncoordinateattentionshufflemechanismandasymmetricmultiscalefusionmoduleforclassificationofgrapeleafdiseases
AT huangweiqi casmamfmnetanetworkbasedoncoordinateattentionshufflemechanismandasymmetricmultiscalefusionmoduleforclassificationofgrapeleafdiseases
AT caiweiwei casmamfmnetanetworkbasedoncoordinateattentionshufflemechanismandasymmetricmultiscalefusionmoduleforclassificationofgrapeleafdiseases
AT huyahui casmamfmnetanetworkbasedoncoordinateattentionshufflemechanismandasymmetricmultiscalefusionmoduleforclassificationofgrapeleafdiseases
AT liliujun casmamfmnetanetworkbasedoncoordinateattentionshufflemechanismandasymmetricmultiscalefusionmoduleforclassificationofgrapeleafdiseases