Early detection of soybean mosaic virus using portable Raman spectroscopy coupled with machine learning
This evidences the superior capability of Raman spectroscopy for monitoring the dynamics of SMV infection and its potential to considerably reduce the duration of diagnosis. This study confirms the feasibility and efficiency of Raman spectroscopy combined with deep learning for in situ early detection of plant viral diseases and provides a promising reference for non-destructive diagnosis of early-stage foliar infections.
Authors
- Longgang Zhao (ORCID: https://orcid.org/0000-0002-0074-7556)
- Z. Wang (ORCID: https://orcid.org/0000-0001-8425-606X)
- Dagang Wang (ORCID: https://orcid.org/0000-0002-6424-6398)
- Tingting Wu (ORCID: https://orcid.org/0000-0003-0888-7075)
- Y. Zhang
- Yanru Zhao (ORCID: https://orcid.org/0009-0000-7532-5297)
- Hongpu Guan
- Weixuan Zhang
- Hexiang Luan
- Yiming Zhao
- Yujia Han
Institutions
- Qingdao Agricultural University (CN)
- North West Agriculture and Forestry University (CN)
- Shandong Academy of Agricultural Sciences (CN)
- Anhui Academy of Agricultural Sciences (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2026-01-06
- DOI
- https://doi.org/10.3389/fpls.2025.1750535
- Primary Topic
- Spectroscopy Techniques in Biomedical and Chemical Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China
- Program on Industrial Technology System of National Soybean