Accurate Detection of Large-Leaf Tea Buds in Mountainous Tea Plantations Based on an Improved YOLO Framework

Tea buds are the key raw material for high-quality tea production, and their accurate perception is essential for intelligent harvesting and quality-oriented management. However, tea bud detection in mountainous large-leaf tea plantations remains challenging because small, densely distributed targets are embedded in complex field environments, significantly limiting the stability and accuracy of existing detection methods. To address these challenges, this study proposes an improved tea bud detection model, termed YOLO-LAR, for mountainous large-leaf tea plantations in Yunnan Province, China, which is developed as an enhanced framework based on the YOLOv11 baseline. YOLO-LAR improves feature representation through multi-scale feature fusion, enabling more effective detection of densely distributed small tea buds. In addition, an optimized downsampling strategy is employed to preserve critical spatial information, and a context-enhanced feature aggregation mechanism is introduced to strengthen robustness under complex backgrounds and illumination variations. The results demonstrate that YOLO-LAR achieves precision, recall, [email protected], and [email protected]:0.95 of 0.959, 0.908, 0.961, and 0.814, respectively, outperforming mainstream YOLO-based models, including YOLOv11n, YOLOv10n, and YOLOv8n. These results indicate that YOLO-LAR provides an effective and practical solution for accurate tea bud detection, offering strong technical support for intelligent harvesting and precision management in mountainous tea plantation environments.