CDPNet: a deformable ProtoPNet for interpretable wheat leaf disease identification
Compared to other models, this approach delivers superior performance and provides clinically interpretable decision support for the identification of real-world wheat diseases in field settings.
Authors
- Bingjing Jia (ORCID: https://orcid.org/0000-0002-7555-0723)
- Chenguang Song (ORCID: https://orcid.org/0000-0001-9650-880X)
- Bo Sung Kang (ORCID: https://orcid.org/0009-0001-0453-0127)
- Lei Shi (ORCID: https://orcid.org/0000-0002-9831-1872)
- Jianhui Zeng
- Ge Hua
Institutions
- Anhui University of Science and Technology (CN)
- Anhui University of Finance and Economics (CN)
- Anhui Science and Technology University (CN)
- Anhui Institute of Information Technology (CN)
- Communication University of China (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-11-06
- DOI
- https://doi.org/10.3389/fpls.2025.1676798
- Citations
- 2
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 3.50