Swin-HSSAM: A green coffee bean grading method by Swin transformer.
A novel shifted window (Swin) Transformer coffee bean grading model called Swin-HSSAM has been proposed to address the challenges of accurately classifying green coffee beans and low identification accuracy. This model integrated the Swin Transformer as the backbone network; fused features from the...
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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.0322198 |
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| author | Yujie Jiao Yuqing Zhao Aoying Jia Tianyun Wang Jiashun Li Kaiming Xiang Hangyu Deng Maochang He Rui Jiang Yue Zhang |
| author_facet | Yujie Jiao Yuqing Zhao Aoying Jia Tianyun Wang Jiashun Li Kaiming Xiang Hangyu Deng Maochang He Rui Jiang Yue Zhang |
| author_sort | Yujie Jiao |
| collection | DOAJ |
| description | A novel shifted window (Swin) Transformer coffee bean grading model called Swin-HSSAM has been proposed to address the challenges of accurately classifying green coffee beans and low identification accuracy. This model integrated the Swin Transformer as the backbone network; fused features from the second, third, and fourth stages using the high-level screening-feature pyramid networks module; and incorporated the selective attention module (SAM) for discriminative power enhancement to enhance the feature outputs before classification. Fusion Loss was employed as the loss function. Experimental results on a proprietary coffee bean dataset demonstrate that the Swin-HSSAM model achieved an average grading accuracy of 96.34% for the three grading as well as the nine defect subdivision levels, outperforming the AlexNet, VGG16, ResNet50, MobileNet-v2, Vision Transformer (ViT), and CrossViT models by 3.86%, 2.56%, 0.44%, 4.05%, 5.36%, and 5.40% percentage points, respectively. Evaluations on a public coffee bean dataset revealed that, compared with the aforementioned models, the Swin-HSSAM model improved the average grading accuracy by 1.01%, 0.13%, 4.75%, 0.85%, 0.73%, and 0.27% percentage points, respectively. These results indicate that the Swin-HSSAM model not only achieved high grading accuracy but also exhibited broad applicability, providing a novel solution for the automated grading and identification of green coffee beans. |
| format | Article |
| id | doaj-art-5a26044d851247e6b1a6da2ec34b36b2 |
| 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-5a26044d851247e6b1a6da2ec34b36b22025-08-20T02:33:23ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01205e032219810.1371/journal.pone.0322198Swin-HSSAM: A green coffee bean grading method by Swin transformer.Yujie JiaoYuqing ZhaoAoying JiaTianyun WangJiashun LiKaiming XiangHangyu DengMaochang HeRui JiangYue ZhangA novel shifted window (Swin) Transformer coffee bean grading model called Swin-HSSAM has been proposed to address the challenges of accurately classifying green coffee beans and low identification accuracy. This model integrated the Swin Transformer as the backbone network; fused features from the second, third, and fourth stages using the high-level screening-feature pyramid networks module; and incorporated the selective attention module (SAM) for discriminative power enhancement to enhance the feature outputs before classification. Fusion Loss was employed as the loss function. Experimental results on a proprietary coffee bean dataset demonstrate that the Swin-HSSAM model achieved an average grading accuracy of 96.34% for the three grading as well as the nine defect subdivision levels, outperforming the AlexNet, VGG16, ResNet50, MobileNet-v2, Vision Transformer (ViT), and CrossViT models by 3.86%, 2.56%, 0.44%, 4.05%, 5.36%, and 5.40% percentage points, respectively. Evaluations on a public coffee bean dataset revealed that, compared with the aforementioned models, the Swin-HSSAM model improved the average grading accuracy by 1.01%, 0.13%, 4.75%, 0.85%, 0.73%, and 0.27% percentage points, respectively. These results indicate that the Swin-HSSAM model not only achieved high grading accuracy but also exhibited broad applicability, providing a novel solution for the automated grading and identification of green coffee beans.https://doi.org/10.1371/journal.pone.0322198 |
| spellingShingle | Yujie Jiao Yuqing Zhao Aoying Jia Tianyun Wang Jiashun Li Kaiming Xiang Hangyu Deng Maochang He Rui Jiang Yue Zhang Swin-HSSAM: A green coffee bean grading method by Swin transformer. PLoS ONE |
| title | Swin-HSSAM: A green coffee bean grading method by Swin transformer. |
| title_full | Swin-HSSAM: A green coffee bean grading method by Swin transformer. |
| title_fullStr | Swin-HSSAM: A green coffee bean grading method by Swin transformer. |
| title_full_unstemmed | Swin-HSSAM: A green coffee bean grading method by Swin transformer. |
| title_short | Swin-HSSAM: A green coffee bean grading method by Swin transformer. |
| title_sort | swin hssam a green coffee bean grading method by swin transformer |
| url | https://doi.org/10.1371/journal.pone.0322198 |
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