SoyCountNet: a deep learning framework for counting and locating soybean seeds in field environment
These findings indicate that SoyCountNet offers a reliable and scalable solution for precise soybean seed counting and localization in complex field environments. Its lightweight architecture allows deployment on intelligent agricultural platforms, supporting high-throughput phenotyping, yield prediction, and precision breeding, while providing a foundation for the future development of intelligent and sustainable agricultural technologies.
Authors
- Z. Wang (ORCID: https://orcid.org/0000-0001-8425-606X)
- zhongzhi Han
- Haoyu Wang
- Shudong Wang
- Longgang Zhao
- Fei Liu
- Hexiang Luan
- Qiong Wu
Institutions
- Qingdao Agricultural University (CN)
- Shandong Academy of Agricultural Sciences (CN)
- Institute of Software (CN)
- China University of Petroleum, East China (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2026-02-25
- DOI
- https://doi.org/10.3389/fpls.2026.1743104
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00