ST-YOLO: a deep learning based intelligent identification model for salt tolerance of wild rice seedlings
The experimental results show that the improved model significantly improves in precision compared with the current mainstream model, and the model evaluates the salt tolerance level of wild rice varieties, and screens out a total of 2 varieties that are extremely salt tolerant and 7 varieties that are salt tolerant, which meets the real-time requirements, and has a certain reference value for the practical application.
Authors
- Qiong Yao (ORCID: https://orcid.org/0000-0002-5484-0502)
- Jianhua Zhang (ORCID: https://orcid.org/0000-0003-2528-6132)
- Pan Pan (ORCID: https://orcid.org/0000-0001-5828-0234)
- Xiaoming Zheng
- Guomin Zhou
Institutions
- Henan University (CN)
- Agricultural Information Institute (CN)
- Xinjiang Academy of Agricultural Sciences (CN)
- Chinese Academy of Agricultural Sciences (CN)
- Ministry of Agriculture and Rural Affairs (CN)
- Institute of Crop Sciences (CN)
- Nanjing Institute of Agricultural Mechanization (CN)
- China Agricultural University (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-06-02
- DOI
- https://doi.org/10.3389/fpls.2025.1595386
- Citations
- 6
- Primary Topic
- Spectroscopy and Chemometric Analyses
- Type
- article
- Field-Weighted Citation Impact
- 4.51
Funders
- National Natural Science Foundation of China
- Chinese Academy of Agricultural Sciences