Non-destructive detection of microplastics stress in rice seedling: an interpretable deep learning approach using excitation emission matrix fluorescence spectra of root exudates
In summary, this study establishes a novel, non-destructive, and interpretable framework for the early detection of MPs stress in rice seedlings based on EEMF spectra of root exudates combined with deep learning.
Authors
- Zhao‐Jun Wei (ORCID: https://orcid.org/0000-0003-1729-209X)
- Chaojie Wei (ORCID: https://orcid.org/0000-0002-9426-2815)
- Ziwei Song
- Hongxin Xie (ORCID: https://orcid.org/0000-0002-6268-4423)
- F. Z. Chen
- Yufeng Li (ORCID: https://orcid.org/0000-0002-5013-5849)
- Xiaorong Wang (ORCID: https://orcid.org/0000-0001-7241-8180)
Institutions
- Zhongyuan University of Technology (CN)
- Institute of High Energy Physics (CN)
- China Agricultural University (CN)
- University of Hong Kong (HK)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-12-02
- DOI
- https://doi.org/10.3389/fpls.2025.1653451
- Primary Topic
- Microplastics and Plastic Pollution
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- China Scholarship Council