ADQ-YOLOv8m: a precise detection model of sugarcane disease in complex environment
Finally, comprehensive evaluation of the ADQ-YOLOv8m model's performance is conducted using visual analysis of image predictions and cross-scenario adaptability testing. The experimental results indicate that the proposed model excels in multi-objective processing and demonstrates strong generalization capabilities, suitable for scenarios involving multiple objectives, multiple categories, and class imbalance. The detection method proposed exhibits excellent detection performance and potential, providing robust support for the development of intelligent sugarcane cultivation and disease control.
Authors
- Haokai Zhang (ORCID: https://orcid.org/0009-0003-9459-2755)
- Ying Yang (ORCID: https://orcid.org/0000-0002-8104-5985)
- Yan Yang (ORCID: https://orcid.org/0000-0003-0131-7993)
- Jing Chen (ORCID: https://orcid.org/0009-0002-8156-8572)
- Jihong Sun (ORCID: https://orcid.org/0009-0000-2058-2675)
- Tao Yin (ORCID: https://orcid.org/0000-0002-1747-6027)
- Yu Qian (ORCID: https://orcid.org/0000-0001-6754-5871)
- Qian Ye
- Zheng Zhou (ORCID: https://orcid.org/0009-0006-7573-2021)
- Zhaowen Li
Institutions
- Kunming University of Science and Technology (CN)
- Yunnan Nationalities University (CN)
- Kunming University (CN)
- Yunnan University (CN)
- Yunnan Agricultural University (CN)
- Yunnan Province Science and Technology Department (CN)
- China Agricultural University (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-10-02
- DOI
- https://doi.org/10.3389/fpls.2025.1669825
- Citations
- 2
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 3.50