<scp>FG</scp>‐<scp>UNet</scp>: fine‐grained feature‐guided <scp>UNet</scp> for segmentation of weeds and crops in <scp>UAV</scp> images
The proposed method in this study lays a solid foundation for accurate detection and intelligent management of weeds. It will have a positive impact on the development of smart agriculture. © 2024 Society of Chemical Industry.
Authors
- Xiaoyulong Chen (ORCID: https://orcid.org/0000-0001-7524-1149)
- Xingtian Wen
- Tomislav Cernava (ORCID: https://orcid.org/0000-0001-7772-4080)
- Yongbin Qin (ORCID: https://orcid.org/0000-0002-1960-8628)
- Shengxian Yang (ORCID: https://orcid.org/0000-0002-7060-1595)
- Xin Zhang (ORCID: https://orcid.org/0000-0002-8376-617X)
- Jianwu Lin (ORCID: https://orcid.org/0009-0008-3715-611X)
Institutions
- Guizhou University (CN)
- Guizhou Education University (CN)
- Guizhou Provincial Science and Technology Department (CN)
- University of Southampton (GB)
Publication Details
- Journal
- Pest Management Science
- Published
- 2024-10-17
- DOI
- https://doi.org/10.1002/ps.8489
- Citations
- 4
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 2.40
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China