Dynamic coding network for robust fruit detection in low-visibility agricultural scenes
The results indicate that DCNet achieves a superior balance between detection accuracy and computational efficiency, making it well-suited for real-time deployment in agricultural robotics. Its modular architecture also facilitates generalization to other crops and complex agricultural environments.
Authors
- Huimin Lu (ORCID: https://orcid.org/0000-0001-9794-3221)
- Fangyi Wang (ORCID: https://orcid.org/0009-0002-6567-8464)
- Tai Jin (ORCID: https://orcid.org/0000-0002-5306-6375)
- Xuan Zhou (ORCID: https://orcid.org/0009-0001-3539-9221)
- Wan Chen (ORCID: https://orcid.org/0000-0002-0396-6287)
- Shuifa Sun (ORCID: https://orcid.org/0000-0001-7720-3998)
Institutions
- China Three Gorges University (CN)
- Hangzhou Normal University (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-10-27
- DOI
- https://doi.org/10.3389/fpls.2025.1670790
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00