GrainNet: efficient detection and counting of wheat grains based on an improved YOLOv7 modeling
Overall, the GrainNet model presented in this study enables accurate and rapid recognition and quantification of wheat grains, which can provide a reference for effective seed examination of wheat grains in real scenarios. Related content can be accessed through the following link: https://github.com/1371530728/grainnet.git .
Authors
- Xin Wang (ORCID: https://orcid.org/0000-0002-7552-455X)
- Changchun Li (ORCID: https://orcid.org/0000-0002-8030-8632)
- Yinghua Jiao
- Huabin Chai
- Chenyi Zhao
- Xifang Wu
- Hengmao Xiang
Institutions
- Henan Polytechnic University (CN)
Publication Details
- Journal
- Plant Methods
- Published
- 2025-03-25
- DOI
- https://doi.org/10.1186/s13007-025-01363-y
- Citations
- 7
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 12.25