A Two-Stage Embedded Detect-to-Cut Vision Pipeline for Date Palm Branch Analysis on Jetson Orin NX
Year: 2026
Type: article
Abstract: 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 [email protected] of 0.942, and an [email protected]:0.95 of 0.669. For keypoint localization, the pose stage achieved a Pose [email protected] of 0.952 and a Pose [email protected]: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.
Source: Computers
Institutions: Tunis El Manar University, University of Southern Denmark
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W7211879244