Recent advances in plant disease severity assessment using convolutional neural networks

In modern agricultural production, the severity of diseases is an important factor that directly affects the yield and quality of plants. In order to effectively monitor and control the entire production process of plants, not only the type of disease, but also the severity of the disease must be cl...

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Veröffentlicht in:	Scientific reports Jg. 13; H. 1; S. 2336 - 13
Hauptverfasser:	Shi, Tingting, Liu, Yongmin, Zheng, Xinying, Hu, Kui, Huang, Hao, Liu, Hanlin, Huang, Hongxu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 09.02.2023 Nature Publishing Group Nature Portfolio
Schlagworte:	631/449/447 639/705/117 Agricultural production Comparative analysis Deep learning Humanities and Social Sciences Image Processing, Computer-Assisted - methods multidisciplinary Neural networks Neural Networks, Computer Patient Acuity Plant diseases Science Science (multidisciplinary) Segmentation
ISSN:	2045-2322, 2045-2322
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Abstract	In modern agricultural production, the severity of diseases is an important factor that directly affects the yield and quality of plants. In order to effectively monitor and control the entire production process of plants, not only the type of disease, but also the severity of the disease must be clarified. In recent years, deep learning for plant disease species identification has been widely used. In particular, the application of convolutional neural network (CNN) to plant disease images has made breakthrough progress. However, there are relatively few studies on disease severity assessment. The group first traced the prevailing views of existing disease researchers to provide criteria for grading the severity of plant diseases. Then, depending on the network architecture, this study outlined 16 studies on CNN-based plant disease severity assessment in terms of classical CNN frameworks, improved CNN architectures and CNN-based segmentation networks, and provided a detailed comparative analysis of the advantages and disadvantages of each. Common methods for acquiring datasets and performance evaluation metrics for CNN models were investigated. Finally, this study discussed the major challenges faced by CNN-based plant disease severity assessment methods in practical applications, and provided feasible research ideas and possible solutions to address these challenges.
AbstractList	In modern agricultural production, the severity of diseases is an important factor that directly affects the yield and quality of plants. In order to effectively monitor and control the entire production process of plants, not only the type of disease, but also the severity of the disease must be clarified. In recent years, deep learning for plant disease species identification has been widely used. In particular, the application of convolutional neural network (CNN) to plant disease images has made breakthrough progress. However, there are relatively few studies on disease severity assessment. The group first traced the prevailing views of existing disease researchers to provide criteria for grading the severity of plant diseases. Then, depending on the network architecture, this study outlined 16 studies on CNN-based plant disease severity assessment in terms of classical CNN frameworks, improved CNN architectures and CNN-based segmentation networks, and provided a detailed comparative analysis of the advantages and disadvantages of each. Common methods for acquiring datasets and performance evaluation metrics for CNN models were investigated. Finally, this study discussed the major challenges faced by CNN-based plant disease severity assessment methods in practical applications, and provided feasible research ideas and possible solutions to address these challenges. Abstract In modern agricultural production, the severity of diseases is an important factor that directly affects the yield and quality of plants. In order to effectively monitor and control the entire production process of plants, not only the type of disease, but also the severity of the disease must be clarified. In recent years, deep learning for plant disease species identification has been widely used. In particular, the application of convolutional neural network (CNN) to plant disease images has made breakthrough progress. However, there are relatively few studies on disease severity assessment. The group first traced the prevailing views of existing disease researchers to provide criteria for grading the severity of plant diseases. Then, depending on the network architecture, this study outlined 16 studies on CNN-based plant disease severity assessment in terms of classical CNN frameworks, improved CNN architectures and CNN-based segmentation networks, and provided a detailed comparative analysis of the advantages and disadvantages of each. Common methods for acquiring datasets and performance evaluation metrics for CNN models were investigated. Finally, this study discussed the major challenges faced by CNN-based plant disease severity assessment methods in practical applications, and provided feasible research ideas and possible solutions to address these challenges. In modern agricultural production, the severity of diseases is an important factor that directly affects the yield and quality of plants. In order to effectively monitor and control the entire production process of plants, not only the type of disease, but also the severity of the disease must be clarified. In recent years, deep learning for plant disease species identification has been widely used. In particular, the application of convolutional neural network (CNN) to plant disease images has made breakthrough progress. However, there are relatively few studies on disease severity assessment. The group first traced the prevailing views of existing disease researchers to provide criteria for grading the severity of plant diseases. Then, depending on the network architecture, this study outlined 16 studies on CNN-based plant disease severity assessment in terms of classical CNN frameworks, improved CNN architectures and CNN-based segmentation networks, and provided a detailed comparative analysis of the advantages and disadvantages of each. Common methods for acquiring datasets and performance evaluation metrics for CNN models were investigated. Finally, this study discussed the major challenges faced by CNN-based plant disease severity assessment methods in practical applications, and provided feasible research ideas and possible solutions to address these challenges.In modern agricultural production, the severity of diseases is an important factor that directly affects the yield and quality of plants. In order to effectively monitor and control the entire production process of plants, not only the type of disease, but also the severity of the disease must be clarified. In recent years, deep learning for plant disease species identification has been widely used. In particular, the application of convolutional neural network (CNN) to plant disease images has made breakthrough progress. However, there are relatively few studies on disease severity assessment. The group first traced the prevailing views of existing disease researchers to provide criteria for grading the severity of plant diseases. Then, depending on the network architecture, this study outlined 16 studies on CNN-based plant disease severity assessment in terms of classical CNN frameworks, improved CNN architectures and CNN-based segmentation networks, and provided a detailed comparative analysis of the advantages and disadvantages of each. Common methods for acquiring datasets and performance evaluation metrics for CNN models were investigated. Finally, this study discussed the major challenges faced by CNN-based plant disease severity assessment methods in practical applications, and provided feasible research ideas and possible solutions to address these challenges.
ArticleNumber	2336
Author	Liu, Yongmin Liu, Hanlin Zheng, Xinying Huang, Hao Shi, Tingting Hu, Kui Huang, Hongxu
Author_xml	– sequence: 1 givenname: Tingting surname: Shi fullname: Shi, Tingting organization: College of Computer and Information Engineering, Central South University of Forestry and Technology, Research Center of Smart Forestry Cloud, Central South University of Forestry and Technology – sequence: 2 givenname: Yongmin surname: Liu fullname: Liu, Yongmin email: T20040550@csuft.edu.cn organization: College of Computer and Information Engineering, Central South University of Forestry and Technology, Research Center of Smart Forestry Cloud, Central South University of Forestry and Technology – sequence: 3 givenname: Xinying surname: Zheng fullname: Zheng, Xinying organization: Business School of Hunan Normal University – sequence: 4 givenname: Kui surname: Hu fullname: Hu, Kui organization: College of Computer and Information Engineering, Central South University of Forestry and Technology, Research Center of Smart Forestry Cloud, Central South University of Forestry and Technology – sequence: 5 givenname: Hao surname: Huang fullname: Huang, Hao organization: College of Computer and Information Engineering, Central South University of Forestry and Technology, Research Center of Smart Forestry Cloud, Central South University of Forestry and Technology – sequence: 6 givenname: Hanlin surname: Liu fullname: Liu, Hanlin organization: College of Computer and Information Engineering, Central South University of Forestry and Technology, Research Center of Smart Forestry Cloud, Central South University of Forestry and Technology – sequence: 7 givenname: Hongxu surname: Huang fullname: Huang, Hongxu organization: College of Computer and Information Engineering, Central South University of Forestry and Technology, Research Center of Smart Forestry Cloud, Central South University of Forestry and Technology
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/36759626$$D View this record in MEDLINE/PubMed
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Snippet	In modern agricultural production, the severity of diseases is an important factor that directly affects the yield and quality of plants. In order to... Abstract In modern agricultural production, the severity of diseases is an important factor that directly affects the yield and quality of plants. In order to...
SourceID	doaj pubmedcentral proquest pubmed crossref springer
SourceType	Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher
StartPage	2336
SubjectTerms	631/449/447 639/705/117 Agricultural production Comparative analysis Deep learning Humanities and Social Sciences Image Processing, Computer-Assisted - methods multidisciplinary Neural networks Neural Networks, Computer Patient Acuity Plant diseases Science Science (multidisciplinary) Segmentation
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Title	Recent advances in plant disease severity assessment using convolutional neural networks
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