ECS-tea: a bio-inspired high-precision detection and localization algorithm for young shoots of Pu-erh tea
These findings demonstrate that ECS-Tea effectively balances accuracy and computational efficiency, validating the complementary contributions of its integrated modules. As a robust, real-time, and deployable solution, it bridges the gap between algorithmic sophistication and practical application, enabling high-precision tea bud harvesting in unstructured field environments.
Authors
- J. Li (ORCID: https://orcid.org/0000-0003-3842-9016)
- Q. Y. Li
- Chenxi Jiang (ORCID: https://orcid.org/0000-0002-4639-9606)
- Wei Li (ORCID: https://orcid.org/0000-0002-3135-0447)
- Jing Xu (ORCID: https://orcid.org/0000-0002-8339-1777)
- Hongsen Liao
- Jianchao Wang (ORCID: https://orcid.org/0000-0002-8488-1903)
- Hailong Ti (ORCID: https://orcid.org/0009-0002-2652-5254)
Institutions
- Southwest Forestry University (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-12-03
- DOI
- https://doi.org/10.3389/fpls.2025.1697209
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00