DO-MDS&DSCA: A New Method for Seed Vigor Detection in Hyperspectral Images Targeting Significant Information Loss and High Feature Similarity

Liangquan Jia, Jianhao He, Jinsheng Wang, Miao Huan, Guangzeng Du, Lu Gao () and Yang Wang ()
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Liangquan Jia: School of Information Engineering, Huzhou University, Huzhou 313000, China
Jianhao He: School of Information Engineering, Huzhou University, Huzhou 313000, China
Jinsheng Wang: School of Information Engineering, Huzhou University, Huzhou 313000, China
Miao Huan: School of Information Engineering, Huzhou University, Huzhou 313000, China
Guangzeng Du: School of Information Engineering, Huzhou University, Huzhou 313000, China
Lu Gao: School of Information Engineering, Huzhou University, Huzhou 313000, China
Yang Wang: College of Modern Agriculture, Zhejiang A&F University, Hangzhou 311300, China

Agriculture, 2025, vol. 15, issue 15, 1-24

Abstract: Hyperspectral imaging for seed vigor detection faces the challenges of handling high-dimensional spectral data, information loss after dimensionality reduction, and low feature differentiation between vigor levels. To address the above issues, this study proposes an improved dynamic optimize MDS (DO-MDS) dimensionality reduction algorithm based on multidimensional scaling transformation. DO-MDS better preserves key features between samples during dimensionality reduction. Secondly, a dual-stream spectral collaborative attention (DSCA) module is proposed. The DSCA module adopts a dual-modal fusion approach combining global feature capture and local feature enhancement, deepening the characterization capability of spectral features. This study selected commonly used rice seed varieties in Zhejiang Province and constructed three individual spectral datasets and a mixed dataset through aging, spectral acquisition, and germination experiments. The experiments involved using the DO-MDS processed datasets with a convolutional neural network embedded with the DSCA attention module, and the results demonstrate vigor discrimination accuracy rates of 93.85%, 93.4%, and 96.23% for the Chunyou 83, Zhongzao 39, and Zhongzu 53 datasets, respectively, achieving 94.8% for the mixed dataset. This study provides effective strategies for spectral dimensionality reduction in hyperspectral seed vigor detection and enhances the differentiation of spectral information for seeds with similar vigor levels.

Keywords: hyperspectral imaging; machine learning; deep learning; dimensionality reduction algorithm; spectral differentiation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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