YOLOV8-CMS: a high-accuracy deep learning model for automated citrus leaf disease classification and grading
The proposed YOLOV8-CMS model outperformed traditional methods in citrus leaf disease classification, while the segmentation-based approach enabled an accurate and quantitative assessment of disease severity. These findings highlighted the potential of deep learning in precision agriculture, contributing to more effective disease management in citrus production.
Authors
- Min‐Ying Su (ORCID: https://orcid.org/0000-0002-3069-0271)
- Yuzhen Wei (ORCID: https://orcid.org/0000-0001-5348-2443)
- Dani Wang
- Pengcheng Wang (ORCID: https://orcid.org/0000-0003-4783-1154)
- Hongyan Zhu (ORCID: https://orcid.org/0000-0002-3395-2222)
Institutions
- Guangxi Normal University (CN)
- Huzhou Normal University (CN)
- Guangxi Academy of Special Crops (CN)
Publication Details
- Journal
- Plant Methods
- Published
- 2025-06-24
- DOI
- https://doi.org/10.1186/s13007-025-01396-3
- Citations
- 5
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 8.75
Funders
- Natural Science Foundation of Guangxi Zhuang Autonomous Region
- British Columbia Innovation Council
- National Natural Science Foundation of China
- Guangxi Normal University
- Division of Graduate Education