Risk prediction of pine wilt disease based on graphical convolutional network in <scp>China</scp>
These findings suggest that GCN technology demonstrates high accuracy and potential in the spatiotemporal risk prediction of pine wilt disease, providing important technical support for relevant departments in early warning and the formulation of prevention and control strategies. © 2025 Society of Chemical Industry.
Authors
- Junhao Zhao (ORCID: https://orcid.org/0000-0001-9896-5959)
- Guofei Fang (ORCID: https://orcid.org/0000-0002-5739-6339)
- Jixia Huang (ORCID: https://orcid.org/0000-0001-8777-6239)
- Xiumei Mo (ORCID: https://orcid.org/0009-0009-9913-0561)
- Xiaoting Zhao (ORCID: https://orcid.org/0000-0002-1838-4255)
Institutions
- Beijing Normal University (CN)
- Beijing Forestry University (CN)
- State Forestry and Grassland Administration (CN)
Publication Details
- Journal
- Pest Management Science
- Published
- 2025-06-27
- DOI
- https://doi.org/10.1002/ps.70008
- Citations
- 3
- Primary Topic
- Plant Pathogens and Fungal Diseases
- Type
- article
- Field-Weighted Citation Impact
- 3.45
Funders
- National Forestry and Grassland Administration