Predicting wheat powdery mildew epidemics in China using meteorological data and machine learning approaches
Machine learning models effectively predict WPM severity degree and area of occurrence at a national scale using meteorological data. The disease severity distribution of WPM displays disease severity spatial patterns visually and can improve management strategies for WPM across China. © 2025 Society of Chemical Industry.
Authors
- Chong Huang (ORCID: https://orcid.org/0000-0003-0779-8214)
- Junkai Li (ORCID: https://orcid.org/0000-0002-4926-2488)
- Aolin Wang (ORCID: https://orcid.org/0000-0002-3490-648X)
- Wei Liu (ORCID: https://orcid.org/0000-0003-0420-7300)
- Changwei Su (ORCID: https://orcid.org/0000-0003-0435-6293)
- Dongfang M. Ma (ORCID: https://orcid.org/0000-0002-0724-0870)
- Juan Zeng (ORCID: https://orcid.org/0000-0001-8227-0415)
- Jonathan S. West (ORCID: https://orcid.org/0000-0002-5211-2405)
- Luo Yong
- Yilin Zhou
- Fei Xu (ORCID: https://orcid.org/0000-0002-2469-6183)
- Jianrong Fan (ORCID: https://orcid.org/0000-0003-3426-0751)
- Xiao Nie (ORCID: https://orcid.org/0009-0009-5608-8412)
- X.X. Wei
Institutions
- Yangtze University (CN)
- Rothamsted Research (GB)
- University of Nebraska at Kearney (US)
- Henan Academy of Agricultural Sciences (CN)
- Institute of Plant Protection (CN)
- Zero to Three (US)
- Chinese Academy of Agricultural Sciences (CN)
- National Engineering Research Center for Information Technology in Agriculture (CN)
- Kearney Agricultural Research and Extension Center
Publication Details
- Journal
- Pest Management Science
- Published
- 2025-11-21
- DOI
- https://doi.org/10.1002/ps.70393
- Primary Topic
- Wheat and Barley Genetics and Pathology
- Type
- article
- Field-Weighted Citation Impact
- 0.00