Academic Journal
Classification of field wheat varieties based on a lightweight G-PPW-VGG11 model.
| Τίτλος: | Classification of field wheat varieties based on a lightweight G-PPW-VGG11 model. |
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| Συγγραφείς: | Yu Pan, Xun Yu, Jihua Dong, Yonghang Zhao, Shuanming Li, Xiuliang Jin |
| Πηγή: | Frontiers in Plant Science; 2024, p1-15, 15p |
| Θεματικοί όροι: | Wheat, Convolutional neural networks, Artificial neural networks, Classification, Wheat harvesting |
| Περίληψη: | Introduction: In agriculture, especially wheat cultivation, farmers often use multi-variety planting strategies to reduce monoculture-related harvest risks. However, the subtle morphological differences among wheat varieties make accurate discrimination technically challenging. Traditional variety classification methods, reliant on expert knowledge, are inefficient for modern intelligent agricultural management. Numerous existing classification models are computationally complex, memory-intensive, and difficult to deploy on mobile devices effectively. This study introduces G-PPW-VGG11, an innovative lightweight convolutional neural network model, to address these issues. Methods: G-PPW-VGG11 ingeniously combines partial convolution (PConv) and partially mixed depthwise separable convolution (PMConv), reducing computational complexity and feature redundancy. Simultaneously, incorporating ECANet, an efficient channel attention mechanism, enables precise leaf information capture and effective background noise suppression. Additionally, G-PPW-VGG11 replaces traditional VGG11's fully connected layers with two pointwise convolutional layers and a global average pooling layer, significantly reducing memory footprint and enhancing nonlinear expressiveness and training efficiency. Results: Rigorous testing showed G-PPW-VGG11's superior performance, with an impressive 93.52% classification accuracy and only 1.79MB memory usage. Compared to VGG11, G-PPW-VGG11 showed a 5.89% increase in accuracy, 35.44% faster inference, and a 99.64% reduction in memory usage. G-PPWVGG11 also surpasses traditional lightweight networks in classification accuracy and inference speed. Notably, G-PPW-VGG11 was successfully deployed on Android and its performance evaluated in real-world settings. The results showed an 84.67% classification accuracy with an average time of 291.04ms per image. Discussion: This validates the model's feasibility for practical agricultural wheat variety classification, establishing a foundation for intelligent management. For future research, the trained model and complete dataset are made publicly available. [ABSTRACT FROM AUTHOR] |
| Copyright of Frontiers in Plant Science is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Classification of field wheat varieties based on a lightweight G-PPW-VGG11 model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yu+Pan%22">Yu Pan</searchLink><br /><searchLink fieldCode="AR" term="%22Xun+Yu%22">Xun Yu</searchLink><br /><searchLink fieldCode="AR" term="%22Jihua+Dong%22">Jihua Dong</searchLink><br /><searchLink fieldCode="AR" term="%22Yonghang+Zhao%22">Yonghang Zhao</searchLink><br /><searchLink fieldCode="AR" term="%22Shuanming+Li%22">Shuanming Li</searchLink><br /><searchLink fieldCode="AR" term="%22Xiuliang+Jin%22">Xiuliang Jin</searchLink> – Name: TitleSource Label: Source Group: Src Data: Frontiers in Plant Science; 2024, p1-15, 15p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Wheat%22">Wheat</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Wheat+harvesting%22">Wheat harvesting</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Introduction: In agriculture, especially wheat cultivation, farmers often use multi-variety planting strategies to reduce monoculture-related harvest risks. However, the subtle morphological differences among wheat varieties make accurate discrimination technically challenging. Traditional variety classification methods, reliant on expert knowledge, are inefficient for modern intelligent agricultural management. Numerous existing classification models are computationally complex, memory-intensive, and difficult to deploy on mobile devices effectively. This study introduces G-PPW-VGG11, an innovative lightweight convolutional neural network model, to address these issues. Methods: G-PPW-VGG11 ingeniously combines partial convolution (PConv) and partially mixed depthwise separable convolution (PMConv), reducing computational complexity and feature redundancy. Simultaneously, incorporating ECANet, an efficient channel attention mechanism, enables precise leaf information capture and effective background noise suppression. Additionally, G-PPW-VGG11 replaces traditional VGG11's fully connected layers with two pointwise convolutional layers and a global average pooling layer, significantly reducing memory footprint and enhancing nonlinear expressiveness and training efficiency. Results: Rigorous testing showed G-PPW-VGG11's superior performance, with an impressive 93.52% classification accuracy and only 1.79MB memory usage. Compared to VGG11, G-PPW-VGG11 showed a 5.89% increase in accuracy, 35.44% faster inference, and a 99.64% reduction in memory usage. G-PPWVGG11 also surpasses traditional lightweight networks in classification accuracy and inference speed. Notably, G-PPW-VGG11 was successfully deployed on Android and its performance evaluated in real-world settings. The results showed an 84.67% classification accuracy with an average time of 291.04ms per image. Discussion: This validates the model's feasibility for practical agricultural wheat variety classification, establishing a foundation for intelligent management. For future research, the trained model and complete dataset are made publicly available. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Frontiers in Plant Science is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3389/fpls.2024.1375245 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1 Subjects: – SubjectFull: Wheat Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Classification Type: general – SubjectFull: Wheat harvesting Type: general Titles: – TitleFull: Classification of field wheat varieties based on a lightweight G-PPW-VGG11 model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yu Pan – PersonEntity: Name: NameFull: Xun Yu – PersonEntity: Name: NameFull: Jihua Dong – PersonEntity: Name: NameFull: Yonghang Zhao – PersonEntity: Name: NameFull: Shuanming Li – PersonEntity: Name: NameFull: Xiuliang Jin IsPartOfRelationships: – BibEntity: Dates: – D: 03 M: 06 Text: 2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 1664462X Titles: – TitleFull: Frontiers in Plant Science Type: main |
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