Green asparagus has the characteristic of growing in clusters, making it inevitable for harvest targets to overlap with weeds and immature asparagus in the field. Extracting stem details in complex spatial positions information presents a significant challenge in identifying suitable harvest targets and high-precision cutting-points. This paper explored YS3AM (Yolo-SAM-3D-Adaptive-Modeling) method for green asparagus detection and 3D adaptive-section modeling using a depth camera, which could furnish harvesting path planning for the selective harvesting robots. Firstly, the model was developed and deployed to extract bounding boxes for individual asparagus stems within clusters. Secondly, the green asparagus stems within these bounding boxes were segment and generate binary mask images. Thirdly, high-quality depth images were obtained using pixel block completion. Finally, based on the cylinder, an adaptive-section 3D reconstruction method fusion with mask and depth was proposed, with a novel evaluation method applied to assess modeling accuracy. The experimental detection results of 1,095 test images demonstrated that the Precision was 98.75%, the Recall was 95.46%, the F1 score was 0.97, and the mAP was 97.16%. The modeling accuracy of 103 asparagus stems under sunny (54) and cloudy (49) conditions was estimated. The average RMSEs of length and bottom depth were 0.74 and 1.105. The detection and modeling for each stem approximately demanded 22 ms. The results of this paper indicated that the 3D model effectively represented the spatial distribution of green asparagus, and further accurately identification of suitable harvest targets and stem cutting-points. This model provided essential spatial pathways for end-effector path planning, thereby fulfilling the operational requirements for efficient green asparagus harvesting robot.