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

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 APS from 36.0% to 32.4%, whereas adding ECA to the dual-scale architecture increased the APS to 37.0%. On an independent test set, YOLO11-ECA achieved 82.2% precision, an 84.8% mAP@0.5, and an F1 score of 81.0%. The model also reached 86.28 FPS with end-to-end latency of 11.59±0.75 ms under a unified GPU evaluation protocol. These results indicate a favorable balance between tea bud detection performance and computational efficiency.