Optimizing polymorphic tomato picking detection: improved YOLOv8n architecture to tackle data under complex environments
In conclusion, this study solves detection problems of ripe and small-target tomatoes in polymorphic environments, improves the model's accuracy and robustness, provides reliable technical support for automated harvesting, and contributes to modern agricultural intelligent development.
Authors
- Jie Mao (ORCID: https://orcid.org/0000-0002-6171-5734)
- Qijun Lv (ORCID: https://orcid.org/0009-0003-7018-4343)
- Chao Fu
- Qiang Li
- Pengxin Zhao
Institutions
- Hebei Normal University (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2026-01-14
- DOI
- https://doi.org/10.3389/fpls.2025.1660480
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00