CLBD-YOLO: A Lightweight Tea Bud Detection Algorithm

Abstract

Tea bud detection is a key technology for enabling automated and intelligent tea harvesting. However, accurate detection remains challenging because tea buds are generally small, densely distributed, partially occluded, and visually similar to the surrounding background. In addition, resource-constrained devices impose strict requirements on model size and computational complexity. To address these challenges, a lightweight tea bud detection model, termed CLBD-YOLO, is proposed for resource-constrained tea bud detection applications. Based on YOLOv11n, a C3K2_GD module is designed for the backbone and neck to combine ghost-style lightweight feature generation with input-conditioned dynamic feature modeling. A three-level weighted BiFPN is employed for bidirectional multi-scale feature fusion, while LAE, DAT, and MPDIoU are incorporated for downsampling feature extraction, adaptive spatial representation, and bounding box regression, respectively. Experiments on the predefined TeaBud test subset containing 625 images show that CLBD-YOLO achieves 74.31% precision, 77.07% recall, 85.67% mAP50, and 62.69% mAP50--95. Compared with YOLOv11n, the parameter count is reduced from 2.582M to 1.426M and the computational cost from 6.3G to 5.6G FLOPs, corresponding to reductions of approximately 44.8% and 11.1%, respectively. For the pre-declared seed-0 run, mAP50 and mAP50--95 decrease by 0.22 and 1.40 percentage points, respectively; when averaged over the three matched random seeds, the corresponding decreases are 0.77 and 1.89 percentage points. Because near-duplicate screening revealed cross-subset augmented siblings in the predefined TeaBud split, these results are interpreted as within-distribution reference values rather than as strictly leakage-free generalization estimates. Direct evaluation on the 591-image external Xinyang Maojian tea bud validation subset using the fixed TeaBud-trained weights without additional fine-tuning or threshold tuning yields 81.2% mAP50 and 62.5% mAP50--95, respectively, for CLBD-YOLO, compared with 80.4% and 61.2% for YOLOv11n. These results show that the compact model remains competitive under this external data distribution. Overall, CLBD-YOLO substantially reduces model complexity while maintaining competitive tea bud detection performance, demonstrating a favorable accuracy–complexity trade-off for resource-constrained applications.