Fine tuning parameters.

<div><p>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 t...

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Bibliographic Details
Main Author:	Tingyuan Zhang (21376939) (author)
Other Authors:	Changsheng Zhang (1450165) (author), Zhongyi Yang (206009) (author), Meng Wang (124646) (author), Fujie Zhang (155391) (author), Dekai Li (21376942) (author), Sen Yang (88300) (author)
Published:	2025
Subjects:	Biotechnology Sociology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified rice seed classification minimizing redundant features global receptive field four convolutional stages double attention mechanism diverse morphological characteristics complex classification requirements collected dataset demonstrate addressing similar fine 94 %, surpassing grained recognition challenges channel residual network net architecture consists div >< p 2 </ sup 22 %, respectively 72 %, network ’ recognition accuracy swin transformer superior performance study proposes promising reference net provides net achieves improves inter findings confirm experimental results efficient solution core innovation class differentiation block ), baseline model art models 16 layers 16 %.
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Fine tuning parameters.

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