Confusion matrix of ResNet50.

Confusion matrix of ResNet50.

<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
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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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