CTDA: an accurate and efficient cherry tomato detection algorithm in complex environments
Experimental results validate the effectiveness of the CTDA model in cherry tomato detection under complex environments. While improving detection accuracy, the model also enhances adaptability to lighting variations, occlusion, and dense small target scenarios, and can be deployed on edge devices for rapid detection, providing strong support for automated cherry tomato picking.
Authors
- Zhonglong Lin (ORCID: https://orcid.org/0009-0002-5463-0334)
- Guoqiang Wang (ORCID: https://orcid.org/0009-0007-8933-9367)
- Xiangjun Zou
- Xiaojuan Li (ORCID: https://orcid.org/0000-0002-0567-9935)
- Zhi Liang
- Caihong Zhang (ORCID: https://orcid.org/0000-0002-9156-0606)
Institutions
- Xinjiang Academy of Agricultural Sciences (CN)
- Xinjiang University (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-03-13
- DOI
- https://doi.org/10.3389/fpls.2025.1492110
- Citations
- 4
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 7.00