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Automated soybean quality detection system using deep learning
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DOI：10.1016/j.atech.2025.101473.png)
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Abstract
En 中文
• A soybean kernel harvest quality inspection device has been designed. • Efficiently acquires high-quality image datasets. • Accurate calculation of soybean with impurity rate and crushing rate. • Improved scSE-UNet accurately segments soybeans and impurities. • Integrating moisture content analysis enables dynamic correction of impurity and fragmentation rate predictions in soybeans.
Keywords:
Image segmentation
Soybean
Impurity rate
Crushing rate
Deep learning
Real-time detection
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