DSS-YOLO: A Lightweight Flower and Stamen Detection Model for Greenhouse Tomato Pollination Assistance

The accurate detection of tomato flowers and stamens in greenhouse environments is a key technology for achieving automated pollination and intelligent crop management. However, during the detection process, tomato flowers and stamens often face challenges such as overlapping, leaf occlusion, complex backgrounds, and difficulties in small-scale feature extraction, which severely impact detection accuracy. Therefore, this paper proposes a lightweight detection model, termed DSS-YOLO. First, the backbone network of YOLOv11n was replaced with HGNetv2, and depthwise separable convolution (DWConv) was incorporated to construct a multiscale lightweight feature extraction network, named DWHGNetv2. This design enhances the feature extraction capability for tomato flowers while reducing computational cost and overall model complexity. Second, traditional convolution-based downsampling layers were replaced with the SCDown module to improve computational efficiency and feature representation. Furthermore, the SIoU loss function was introduced to optimize the localization accuracy of angle-sensitive targets, such as stamens. Experimental results demonstrate that DSS-YOLO consistently outperforms the baseline YOLOv11n. Compared with YOLOv11n, the model size, parameter count, and computational cost are reduced by 34%, 36%, and 35%, respectively, while precision, recall, and mean Average Precision at an IoU threshold of 0.5 ([email protected]) are improved by 1.1%, 1.0%, and 0.7%, respectively. These results indicate that DSS-YOLO provides a robust and efficient solution for high-precision tomato flower and stamen detection, meeting the practical requirements of pollination robots in greenhouse environments.