Fruit-stem structural visual perception for picking point localization in strawberry harvesting robots

In ridge-cultivated strawberry environments, significant scale variation between fruits and stems, severe occlusion, and high missed-detection rates for small or slender targets pose major challenges to accurate picking point localization. This study proposes a fruit-stem oriented detection and picking point localization method based on an improved YOLOv11n-OBB model. Specifically, an enhanced model termed FSGE-OBB is developed. A Feature Fusion Lite Module (FFLM) is introduced to strengthen cross-layer interaction between shallow spatial details and deep semantic features, thereby improving multi-scale feature representation. A Stem Direction Enhancement Module (SDEM) is designed to enhance the directional perception of slender stems. In addition, Grouped Spatial Excitation Convolution (GSEConv) is adopted to reduce model complexity while maintaining feature representation capability, and an Efficient Upsampling Convolution Block (EUCB) is incorporated into high-resolution feature maps to improve detail recovery. Based on the oriented detection results, a geometry-constrained fruit-stem association method is established by modeling the spatial relationship between fruit and stem oriented bounding boxes (OBBs), and the corresponding picking points are localized accordingly. Experimental results show that the FSGE-OBB model achieves mAP@0.5 and mAP@0.5:0.95 of 78.85% and 68.27%, respectively, outperforming the YOLOv11n-OBB baseline by 2.59 and 1.90 percentage points. The proposed fruit-stem association method achieves a success rate of 97.99%. In preliminary three dimensional localization experiments, the mean absolute errors of picking points are 2.2 mm, 2.6 mm, and 2.3 mm in the x-, y-, and z-directions, respectively. Furthermore, simulated harvesting experiments achieve a success rate of 93.3%, with an average harvesting time of 9.2 s per fruit, validating the feasibility and effectiveness of the proposed method in practical harvesting tasks. These results demonstrate that the proposed FSGE-OBB-based framework enables unified modeling of fruit-stem detection, association, and picking point localization, providing a feasible visual perception solution for strawberry harvesting robots.