A Two-Stage Embedded Detect-to-Cut Vision Pipeline for Date Palm Branch Analysis on Jetson Orin NX

Date palm harvesting requires reliable localization of anatomically appropriate cutting sites under cluttered orchard conditions, variable illumination, and frequent occlusion. This study presents an embedded two-stage detect-to-cut vision pipeline for date palm branch analysis on NVIDIA Jetson Orin NX. In the first stage, a YOLOv8-based detector localizes candidate fruit-bearing branches in the full image. In the second stage, a YOLOv8-Pose model operates on cropped regions of interest to estimate four harvest-relevant anatomical keypoints, namely the peduncle base, cut point, rachis base, and rachis tip. Under the reported evaluation setting, the detection stage achieved a precision of 0.893, a recall of 0.881, an mAP@0.5 of 0.942, and an mAP@0.5:0.95 of 0.669. For keypoint localization, the pose stage achieved a Pose mAP@0.5 of 0.952 and a Pose mAP@0.5:0.95 of 0.840, while cut-point accuracy, assessed using peduncle-normalized PCK, reached 0.605, 0.801, and 0.871 at α=0.1, 0.2, and 0.5, respectively. On-device evaluation further showed a mean end-to-end latency of 380.34 ms per frame (4.00 FPS) across multi-ROI scenes and 135.01 ms per frame (7.79 FPS) in a continuous single-bunch video scenario. Taken together, these results support the feasibility of the proposed pipeline as a leakage-controlled within-domain embedded perception prototype for cut-point localization in the studied Deglet Nour orchard setting, rather than as a broadly validated field-ready harvesting solution.