Machine learning-enabled UAV hyperspectral identification of tomato spotted wilt virus in tobacco
This indicated that red-edge bands were of significant value in distinguishing healthy and TSWV-infected tobacco plants. Our study indicates the significant potential of integrating UAV-based hyperspectral imaging with machine learning techniques for rapid, non-destructive detection of tobacco TSWV at the field scale. The proposed approach offers a novel and efficient pathway for remote sensing-based monitoring of viral diseases in crops, with implications for precision agriculture and plant disease management.
Authors
- Weiheng Xu (ORCID: https://orcid.org/0000-0002-9588-4931)
- Huan Wang (ORCID: https://orcid.org/0000-0002-3878-2760)
- Xiaolong Zhang (ORCID: https://orcid.org/0000-0001-5284-2742)
- Guangzhi Di (ORCID: https://orcid.org/0009-0008-4258-1110)
- Ziyi Yang (ORCID: https://orcid.org/0000-0003-1194-3680)
- Mao Chuntang
- Weili Kou (ORCID: https://orcid.org/0000-0003-4390-7895)
- Ning Lu (ORCID: https://orcid.org/0000-0001-9329-3823)
- Yanan Zhao (ORCID: https://orcid.org/0009-0008-4729-900X)
- Leiguang Wang
Institutions
- Southwest Forestry University (CN)
- Yunnan Yingmao Biotechnology (China) (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-12-09
- DOI
- https://doi.org/10.3389/fpls.2025.1728043
- Citations
- 1
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 1.72
Funders
- National Natural Science Foundation of China