Multi-class rice seed recognition based on deep space and channel residual network combined with double attention mechanism.
Accurately recognizing rice seed varieties poses significant challenges due to their diverse morphological characteristics and complex classification requirements. Traditional image recognition methods often struggle with both accuracy and efficiency in this context. To address these limitations, th...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0322699 |
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| _version_ | 1850261224309129216 |
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| author | Tingyuan Zhang Changsheng Zhang Zhongyi Yang Meng Wang Fujie Zhang Dekai Li Sen Yang |
| author_facet | Tingyuan Zhang Changsheng Zhang Zhongyi Yang Meng Wang Fujie Zhang Dekai Li Sen Yang |
| author_sort | Tingyuan Zhang |
| collection | DOAJ |
| description | Accurately recognizing rice seed varieties poses significant challenges due to their diverse morphological characteristics and complex classification requirements. Traditional image recognition methods often struggle with both accuracy and efficiency in this context. To address these limitations, this study proposes the Deep Space and Channel Residual Network with Double Attention Mechanism (RSCD-Net) to enhance the recognition accuracy of 36 rice seed varieties. The core innovation of RSCD-Net is the introduction of the Space and Channel Feature Extraction Residual Block (SCR-Block), which improves inter-class differentiation while minimizing redundant features, thereby optimizing computational efficiency. The RSCD-Net architecture consists of 16 layers of SCR-Blocks, structured into four convolutional stages with 3, 4, 6, and 3 units, respectively. Additionally, a Double Attention Mechanism (A2Net) is incorporated to enhance the network's global receptive field, improving its capacity to distinguish subtle variations among seed types. Experimental results on a self-collected dataset demonstrate that RSCD-Net achieves an average accuracy of 81.94%, surpassing the baseline model by 4.16%. Compared with state-of-the-art models such as InceptionResNetV2, ConvNeXt, MobileNetV3, and Swin Transformer, RSCD Net has improved by 1.17%, 3%, 24.72%, and 13.22%, respectively, showcasing its superior performance. These findings confirm that RSCD-Net provides an effective and efficient solution for rice seed classification, offering a promising reference for addressing similar fine-grained recognition challenges in agricultural applications. |
| format | Article |
| id | doaj-art-7e9aa800380749d5a54b4ddb8d16a69e |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-7e9aa800380749d5a54b4ddb8d16a69e2025-08-20T01:55:28ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01205e032269910.1371/journal.pone.0322699Multi-class rice seed recognition based on deep space and channel residual network combined with double attention mechanism.Tingyuan ZhangChangsheng ZhangZhongyi YangMeng WangFujie ZhangDekai LiSen YangAccurately recognizing rice seed varieties poses significant challenges due to their diverse morphological characteristics and complex classification requirements. Traditional image recognition methods often struggle with both accuracy and efficiency in this context. To address these limitations, this study proposes the Deep Space and Channel Residual Network with Double Attention Mechanism (RSCD-Net) to enhance the recognition accuracy of 36 rice seed varieties. The core innovation of RSCD-Net is the introduction of the Space and Channel Feature Extraction Residual Block (SCR-Block), which improves inter-class differentiation while minimizing redundant features, thereby optimizing computational efficiency. The RSCD-Net architecture consists of 16 layers of SCR-Blocks, structured into four convolutional stages with 3, 4, 6, and 3 units, respectively. Additionally, a Double Attention Mechanism (A2Net) is incorporated to enhance the network's global receptive field, improving its capacity to distinguish subtle variations among seed types. Experimental results on a self-collected dataset demonstrate that RSCD-Net achieves an average accuracy of 81.94%, surpassing the baseline model by 4.16%. Compared with state-of-the-art models such as InceptionResNetV2, ConvNeXt, MobileNetV3, and Swin Transformer, RSCD Net has improved by 1.17%, 3%, 24.72%, and 13.22%, respectively, showcasing its superior performance. These findings confirm that RSCD-Net provides an effective and efficient solution for rice seed classification, offering a promising reference for addressing similar fine-grained recognition challenges in agricultural applications.https://doi.org/10.1371/journal.pone.0322699 |
| spellingShingle | Tingyuan Zhang Changsheng Zhang Zhongyi Yang Meng Wang Fujie Zhang Dekai Li Sen Yang Multi-class rice seed recognition based on deep space and channel residual network combined with double attention mechanism. PLoS ONE |
| title | Multi-class rice seed recognition based on deep space and channel residual network combined with double attention mechanism. |
| title_full | Multi-class rice seed recognition based on deep space and channel residual network combined with double attention mechanism. |
| title_fullStr | Multi-class rice seed recognition based on deep space and channel residual network combined with double attention mechanism. |
| title_full_unstemmed | Multi-class rice seed recognition based on deep space and channel residual network combined with double attention mechanism. |
| title_short | Multi-class rice seed recognition based on deep space and channel residual network combined with double attention mechanism. |
| title_sort | multi class rice seed recognition based on deep space and channel residual network combined with double attention mechanism |
| url | https://doi.org/10.1371/journal.pone.0322699 |
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