Main architecture of Swin Transformer.

<div><p>The traditional method of corn quality detection relies heavily on the subjective judgment of inspectors and suffers from a high error rate. To address these issues, this study employs the Swin Transformer as an enhanced base model, integrating machine vision and deep learning te...

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Bibliographic Details
Main Author:	Ning Zhang (23771) (author)
Other Authors:	Yuanqi Chen (4526056) (author), Enxu Zhang (20613821) (author), Ziyang Liu (242055) (author), Jie Yue (1597459) (author)
Published:	2025
Subjects:	Biotechnology Science Policy Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified integrating machine vision enhanced base model experimental results demonstrate deep learning techniques recognition accuracy rate high error rate extracted features undergo corn quality detection corn quality assessment 152 valid images broken corn experimental samples deep features xlink "> traditional method technical approach swint high swin transformer subsequently fused subjective judgment novel perspective maize images key metrics fused features f1 score convolutional module attention layer 89 %.
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Main architecture of Swin Transformer.

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