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Research on cassava disease classification using the multi-scale fusion model based on EfficientNet and attention mechanism

Cassava disease is one of the leading causes to the serious decline of cassava yield. Because it is difficult to identify the characteristics of cassava disease, if not professional cassava growers, it will be prone to misjudgment. In order to strengthen the judgment of cassava diseases, the identif...

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Detalles Bibliográficos
Autores principales: Liu, Mingxin, Liang, Haofeng, Hou, Mingxin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9815107/
https://www.ncbi.nlm.nih.gov/pubmed/36618625
http://dx.doi.org/10.3389/fpls.2022.1088531
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author Liu, Mingxin
Liang, Haofeng
Hou, Mingxin
author_facet Liu, Mingxin
Liang, Haofeng
Hou, Mingxin
author_sort Liu, Mingxin
collection PubMed
description Cassava disease is one of the leading causes to the serious decline of cassava yield. Because it is difficult to identify the characteristics of cassava disease, if not professional cassava growers, it will be prone to misjudgment. In order to strengthen the judgment of cassava diseases, the identification characteristics of cassava diseases such as different color of cassava leaf disease spots, abnormal leaf shape and disease spot area were studied. In this paper, deep convolutional neural network was used to classify cassava leaf diseases, and image classification technology was used to recognize and classify cassava leaf diseases. A lightweight module Multi-scale fusion model (MSFM) based on attention mechanism was proposed to extract disease features of cassava leaves to enhance the classification of disease features. The resulting feature map contained key disease identification information. The study used 22,000 cassava disease leaf images as a data set, including four different cassava leaf disease categories and healthy cassava leaves. The experimental results show that the cassava leaf disease classification model based on multi-scale fusion Convolutional Neural Network (CNN) improves EfficientNet compared with the original model, with the average recognition rate increased by nearly 4% and the average recognition rate up to 88.1%. It provides theoretical support and practical tools for the recognition and early diagnosis of plant disease leaves.
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spelling pubmed-98151072023-01-06 Research on cassava disease classification using the multi-scale fusion model based on EfficientNet and attention mechanism Liu, Mingxin Liang, Haofeng Hou, Mingxin Front Plant Sci Plant Science Cassava disease is one of the leading causes to the serious decline of cassava yield. Because it is difficult to identify the characteristics of cassava disease, if not professional cassava growers, it will be prone to misjudgment. In order to strengthen the judgment of cassava diseases, the identification characteristics of cassava diseases such as different color of cassava leaf disease spots, abnormal leaf shape and disease spot area were studied. In this paper, deep convolutional neural network was used to classify cassava leaf diseases, and image classification technology was used to recognize and classify cassava leaf diseases. A lightweight module Multi-scale fusion model (MSFM) based on attention mechanism was proposed to extract disease features of cassava leaves to enhance the classification of disease features. The resulting feature map contained key disease identification information. The study used 22,000 cassava disease leaf images as a data set, including four different cassava leaf disease categories and healthy cassava leaves. The experimental results show that the cassava leaf disease classification model based on multi-scale fusion Convolutional Neural Network (CNN) improves EfficientNet compared with the original model, with the average recognition rate increased by nearly 4% and the average recognition rate up to 88.1%. It provides theoretical support and practical tools for the recognition and early diagnosis of plant disease leaves. Frontiers Media S.A. 2022-12-22 /pmc/articles/PMC9815107/ /pubmed/36618625 http://dx.doi.org/10.3389/fpls.2022.1088531 Text en Copyright © 2022 Liu, Liang and Hou 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
Liu, Mingxin
Liang, Haofeng
Hou, Mingxin
Research on cassava disease classification using the multi-scale fusion model based on EfficientNet and attention mechanism
title Research on cassava disease classification using the multi-scale fusion model based on EfficientNet and attention mechanism
title_full Research on cassava disease classification using the multi-scale fusion model based on EfficientNet and attention mechanism
title_fullStr Research on cassava disease classification using the multi-scale fusion model based on EfficientNet and attention mechanism
title_full_unstemmed Research on cassava disease classification using the multi-scale fusion model based on EfficientNet and attention mechanism
title_short Research on cassava disease classification using the multi-scale fusion model based on EfficientNet and attention mechanism
title_sort research on cassava disease classification using the multi-scale fusion model based on efficientnet and attention mechanism
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9815107/
https://www.ncbi.nlm.nih.gov/pubmed/36618625
http://dx.doi.org/10.3389/fpls.2022.1088531
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