YOLO11-ECA: A Lightweight Tea Bud Detection Method for Complex Tea Plantation Environments.

Zhaodong Wang, Xinqiang Liao, Yi Jiang, Hao You, Wenzhi Liao, Yang Li

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Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Zhaodong WangSchool of Mechanical and Electronic Engineering, Jingdezhen Ceramic University, Jingdezhen 333403, China.

Xinqiang LiaoSchool of Mechanical and Electronic Engineering, Jingdezhen Ceramic University, Jingdezhen 333403, China.

Yi JiangSchool of Mechanical and Electronic Engineering, Jingdezhen Ceramic University, Jingdezhen 333403, China.

Hao YouSchool of Mechanical and Electronic Engineering, Jingdezhen Ceramic University, Jingdezhen 333403, China.

Wenzhi LiaoDepartment of Telecommunications and Information Processing, Ghent University, B-9000 Ghent, Belgium.ORCID 0000-0002-2183-0324

Yang LiTea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China.

Funding

Education Department of Jiangxi Province GJJ2502301

6 · The paper itself

Abstract

Accurate tea bud detection in natural plantation environments is challenged by variable illumination, foliage occlusion, target-background similarity, and dense target distribution. This study proposes YOLO11-ECA, a lightweight detector derived from YOLO11n, to improve the detection performance while reducing the computational demands. The P3/8 prediction branch and its associated high-resolution feature fusion path are removed, while the P4/16 and P5/32 detection scales are retained. Standard adaptive efficient channel attention (ECA) modules are then integrated into the retained feature paths for lightweight channel recalibration. On the validation set, YOLO11-ECA achieved 79.8% precision, 85.1% recall, an 89.2% mAP@0.5, a 52.8% mAP@0.5:0.95, and an F1 score of 82.0%. Relative to YOLO11n, these metrics increased by 1.9, 2.6, 3.1, 0.4, and 2.0 percentage points, respectively, while the parameters and GFLOPs decreased by 7.4% and 28.8%. Scale-specific evaluation showed that P3 removal reduced the AP

Indexed as

Image Processing, Computer-AssistedTeaAlgorithmsDetection AlgorithmsPlant LeavesTeaefficient channel attentionlightweight object detectionmachine visiontea bud detectionYOLO11

Identifiers

PMID42740018

DOI10.3390/s26175398

PMCPMC13568150

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Textmetadata

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.