Enhanced tomato detection in greenhouse environments: a lightweight model based on S-YOLO with high accuracy
The lightweight characteristics of the S-YOLO model make it suitable for the visual system of tomato-picking robots, providing technical support for robot target recognition and harvesting operations in facility environments based on mobile edge computing.
Authors
- Xiangyang Sun (ORCID: https://orcid.org/0000-0003-4385-3479)
Institutions
- Shandong Agricultural University (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2024-08-22
- DOI
- https://doi.org/10.3389/fpls.2024.1451018
- Citations
- 23
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 13.79