REB-Tea: An Intelligent Detection Model for Tea Buds with Clarity and Multi-Scale Feature Enhancement

Tea bud detection is a fundamental prerequisite for accurate tea yield estimation and intelligent mechanical harvesting. However, existing detection methods face several critical challenges, including ineffective extraction of multi-scale features, weak feature saliency for small tea bud targets, and the prevalent imaging issue in which the central regions of tea images are in focus while peripheral areas suffer from defocus blur. These factors collectively result in a high rate of missed detections, severely limiting detection accuracy and subsequent application performance. To overcome these technical bottlenecks, this paper proposes a novel tea bud detection framework, termed REB-Tea, which integrates image clarity optimization with multi-scale feature enhancement. First, the Restormer image restoration network is employed to improve overall image clarity and enhance the discriminative representation of tea bud features. Subsequently, a bidirectional feature pyramid network (BiFPN) structure and an efficient multi-scale attention (EMA) mechanism are incorporated into the neck of the YOLOv5 model to strengthen multi-scale feature fusion and guide the network to focus on fine-grained tea bud features across different scales, thereby improving detection performance for small and densely distributed targets. Experimental results based on 10-fold cross-validation demonstrate that the proposed REB-Tea model achieves an average mAP50 of 95.5% on the Longjing 43 tea test set, representing a 9.9 percentage point improvement over the baseline YOLOv5 model, and Welch’s independent two-sample t-test verifies that this accuracy increment is highly statistically significant. Moreover, the model exhibits reliable detection performance across different tea varieties, including Cuifeng and Fuding White Tea. Specifically, the mAP50 reaches 88.3% on Cuifeng, which shares similar appearance characteristics with Longjing, and 78.1% on Fuding White Tea, which has noticeably different appearance characteristics from Longjing. These results confirm the effectiveness of the REB-Tea framework in addressing challenges such as out-of-focus blurring, weak feature saliency, and multi-scale feature extraction. Overall, the proposed approach significantly enhances tea bud detection accuracy in natural environments and provides robust technical support for intelligent tea harvesting applications.