Study on lightweight rice blast detection method based on improved <scp>YOLOv8</scp>
The YOLOv8-OW model provides a more effective solution, which is suitable for deployment on resource-limited mobile devices, to provide real-time and accurate disease detection support for farmers and further promotes the development of precision agriculture. © 2025 Society of Chemical Industry.
Authors
- Qiang Cao (ORCID: https://orcid.org/0000-0003-3733-2968)
- Xinpeng Wang (ORCID: https://orcid.org/0000-0001-6701-4624)
- Jinpeng Li (ORCID: https://orcid.org/0000-0002-1538-7274)
- Tongyu Xu (ORCID: https://orcid.org/0000-0001-9284-649X)
- Jinxuan Li (ORCID: https://orcid.org/0000-0002-4595-8568)
- Shuai Feng (ORCID: https://orcid.org/0000-0002-9826-8392)
- Sixu Jin
Institutions
- Shenyang Agricultural University (CN)
Publication Details
- Journal
- Pest Management Science
- Published
- 2025-03-22
- DOI
- https://doi.org/10.1002/ps.8790
- Citations
- 7
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 12.25