Maturity detection and counting of blueberries in real orchards using a novel STF-YOLO model integrated with ByteTrack algorithm
Overall, these results suggest that the STF-YOLO integrated with the ByteTrack framework can accurately detect and count blueberries in orchards. This lays a solid foundation for the future development of automated blueberry harvesting machinery and improvements in harvest efficiency.
Authors
- Xing Xu (ORCID: https://orcid.org/0000-0001-5685-3123)
- Zhenzhen Wang (ORCID: https://orcid.org/0000-0003-2352-6307)
- Petr Skobelev (ORCID: https://orcid.org/0000-0003-2199-9557)
- Yali Wang (ORCID: https://orcid.org/0000-0002-1739-3053)
- Na Wu (ORCID: https://orcid.org/0000-0002-1024-4573)
- Yun Zhao (ORCID: https://orcid.org/0009-0009-3804-5291)
- Jie Wu (ORCID: https://orcid.org/0009-0001-1613-1641)
- Yanan Mi
Institutions
- Zhejiang University of Science and Technology (CN)
- Metso (Finland) (FI)
- Samara Federal Research Scientific Center (RU)
- Zhejiang Hospital (CN)
- Zhejiang Lab (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-11-27
- DOI
- https://doi.org/10.3389/fpls.2025.1682024
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00