Estimating rice yield-related traits using machine learning models integrating hyperspectral and texture features
The optimized method can significantly increase the accuracy of estimation models. The results provide a scientific basis and technical data for the precise diagnosis of rice yield-related traits.
Authors
- Meijuan Li (ORCID: https://orcid.org/0000-0002-3816-4823)
- Renyu Hu (ORCID: https://orcid.org/0000-0003-2215-8485)
- Feifei Zhu (ORCID: https://orcid.org/0000-0002-0843-5872)
- Qunhuan Ye
- Zhaowen Mo (ORCID: https://orcid.org/0000-0002-1887-9389)
- Chusheng Lu (ORCID: https://orcid.org/0000-0003-4539-9601)
- Yufen Zhang
- Youqiang Fu (ORCID: https://orcid.org/0000-0001-6471-4465)
- Zhongyuan Lu (ORCID: https://orcid.org/0000-0002-0897-7006)
- Xinyu Wang (ORCID: https://orcid.org/0000-0001-9434-2906)
- Junfeng Pan
- Qiang Cai (ORCID: https://orcid.org/0000-0001-7012-9887)
- Yuanhong Yin
- Yibo Chen (ORCID: https://orcid.org/0000-0002-3969-2679)
- Xiangyu Hu (ORCID: https://orcid.org/0009-0006-4113-6045)
- Xuhua Zhong
Institutions
- South China Agricultural University (CN)
- Guangdong Academy of Agricultural Sciences (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-11-07
- DOI
- https://doi.org/10.3389/fpls.2025.1713014
- Citations
- 1
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 1.72