Automated seed counting using image processing and deep learning
Both automated methods significantly enhance the efficiency of seed counting, providing a practical and accessible solution for various agricultural contexts. The integration of these methods into a mobile app streamlines seed counting for laboratory research, field studies, seed production, and breeding trials, offering a transformative approach to modernizing seed counting practices while reducing time and labor requirements.
Authors
- Jialin Yu (ORCID: https://orcid.org/0000-0003-0287-5081)
- Shu Dang
- Wenpeng Zhu
- Yan Pan (ORCID: https://orcid.org/0000-0001-6476-5457)
- Jinxu Wang
- Zhenyu Zhang (ORCID: https://orcid.org/0000-0001-5727-9450)
- Xinru Song (ORCID: https://orcid.org/0009-0001-2742-7990)
- Teng Liu (ORCID: https://orcid.org/0000-0002-8788-5192)
- Xiaoming Yu (ORCID: https://orcid.org/0000-0002-1395-161X)
- Qiuyu Zu (ORCID: https://orcid.org/0009-0009-1502-9504)
Institutions
- Jilin Agricultural Science and Technology University (CN)
- Weifang University (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-08-29
- DOI
- https://doi.org/10.3389/fpls.2025.1659781
- Citations
- 1
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 1.75