Real-time segmentation and phenotypic analysis of rice seeds using YOLOv11-LA and RiceLCNN
Experimental validation confirmed that the YOLOv11-LA model outperforms the original YOLOv11 in terms of both detection speed and accuracy, while also maintaining low computational complexity. The integration of the YOLOv11-LA, RiceLCNN, and DeepSORT algorithms, combined with advanced measurement techniques, underscores the model's potential for industrial applications, particularly in enhancing smart agricultural practices.
Authors
- Shaozhong Song (ORCID: https://orcid.org/0009-0004-6807-779X)
- Dejia Zhang (ORCID: https://orcid.org/0000-0002-5390-2521)
- Jia Liu (ORCID: https://orcid.org/0000-0003-2217-6982)
- Wenjie Xu (ORCID: https://orcid.org/0000-0001-8718-3230)
- Nurdila Xiayidan
Institutions
- Changchun University of Science and Technology (CN)
- Southwest University of Science and Technology (CN)
- Southwest Forestry University (CN)
- Jilin Engineering Normal University (CN)
- Zhongshan Hospital (CN)
- Changchun University (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-12-08
- DOI
- https://doi.org/10.3389/fpls.2025.1673143
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00