TeaPickingNet: Towards Robust Recognition of Fine-Grained Picking Actions in Tea Gardens Using an Attention-Enhanced Framework

With the emergence of smart agriculture, precise behavior recognition in tea gardens has become increasingly important for operational monitoring and intelligent management. However, existing behavior detection systems face limitations when deployed in real-world agricultural environments, particularly due to dense vegetation, variable lighting, and diverse human–machine interactions. This paper proposes a novel deep learning framework for picking behavior recognition tailored to complex tea plantation environments. We first construct a large-scale, annotated dataset comprising 12,195 images across 7 behavior categories, collected from both field and web sources, capturing a diverse range of geographic, temporal, and environmental conditions. To address occlusion and multi-scale detection challenges, we enhance YOLOv5 by integrating an Exponential Moving Average (EMA) attention mechanism, Complete Intersection over Union (CIoU) loss, and Atrous Spatial Pyramid Pooling (ASPP), achieving a 73.6% [email protected], representing an 11.6% relative improvement over the baseline model, which indicates a notable enhancement in detection accuracy under complex tea garden conditions. Furthermore, we propose an SE-Faster R-CNN model by embedding Squeeze-and-Excitation (SE) channel attention modules and anchor box optimization strategies, which significantly boosts performance in complex scenarios. A lightweight visual interface for real-time image and video-based detection is also developed to enhance the practical deployability of the system. Experimental results demonstrate the effectiveness, robustness, and real-time potential of the proposed system in recognizing tea garden behaviors under real field conditions.