Intelligent grading of sugarcane leaf disease severity by integrating physiological traits with the SSA-XGBoost algorithm
Compared to image-based deep learning approaches, the proposed method offers advantages in terms of data accessibility, computational efficiency, and model transparency, making it well-suited for rapid on-site diagnosis in agricultural settings. This study provides an efficient and reliable technical framework for intelligent diagnosis and early warning of sugarcane disease severity.
Authors
- Canyu Wang (ORCID: https://orcid.org/0000-0002-5178-3776)
- Jiawei Zhao (ORCID: https://orcid.org/0000-0002-1625-1176)
- Jing Chen (ORCID: https://orcid.org/0009-0002-8156-8572)
- Jihong Sun (ORCID: https://orcid.org/0009-0000-2058-2675)
- Xinrui Wang (ORCID: https://orcid.org/0000-0003-0139-3144)
- Peng Tian (ORCID: https://orcid.org/0009-0005-7313-2885)
- Ye Qian (ORCID: https://orcid.org/0009-0006-6620-0971)
- Mengyao Wu (ORCID: https://orcid.org/0000-0001-5748-5780)
Institutions
- Kunming University (CN)
- Yunnan Agricultural University (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-10-15
- DOI
- https://doi.org/10.3389/fpls.2025.1698808
- Citations
- 1
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 1.75