Swin-HSSAM: A green coffee bean grading method by Swin transformer

Yujie Jiao, Yuqing Zhao, Aoying Jia, Tianyun Wang, Jiashun Li, Kaiming Xiang, Hangyu Deng, Maochang He, Rui Jiang and Yue Zhang

PLOS ONE, 2025, vol. 20, issue 5, 1-20

Abstract: 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.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0322198

DOI: 10.1371/journal.pone.0322198

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