YOLOv8-FDA: lightweight wheat ear detection and counting in drone images based on improved YOLOv8
The proposed YOLOv8-FDA model demonstrates strong detection performance, lightweight characteristics, and efficient real-time capability, indicating its high practicality and suitability for deployment in real-world agricultural applications.
Authors
- Xiao Xiao (ORCID: https://orcid.org/0000-0002-2319-5563)
- Haifeng Lin (ORCID: https://orcid.org/0000-0001-5256-3292)
- Yuxuan Lin (ORCID: https://orcid.org/0009-0003-1473-7898)
Institutions
- Nanjing Forestry University (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-09-24
- DOI
- https://doi.org/10.3389/fpls.2025.1682243
- Citations
- 2
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 3.50
Funders
- Nvidia
- Nanjing Forestry University
- Strong
- National Natural Science Foundation of China