ADQ-PACR: A Deployable One-Stage Framework for Weed Growth-Point Localization in Cotton Fields

Precision weeding in cotton fields requires joint plant detection and growth-point localization despite canopy overlap and limited embedded computing resources. We propose ADQ-PACR, a one-stage framework that combines quality-aware localization with auxiliary coordinate regularization. ADQ jointly predicts growth-point coordinates, keypoint validity, and scale-normalized localization quality. PACR maintains independent supervision of the primary candidate and supervises the auxiliary candidate with the lowest coordinate error. The auxiliary branches are removed before deployment, leaving a fixed primary path. Experiments used 3527 smartphone images collected in Shaya and Changji, Xinjiang, from April to July 2022, spanning cotton seedling to squaring stages and including weeds such as Amaranthus retroflexus, Chenopodium album, and Portulaca oleracea. Evaluation used 343 fixed test images and three training seeds, with a common evaluator for adapted YOLO pose baselines initialized from detection weights and two-stage systems using predicted boxes. The full ADQ-PACR framework increased mean keypoint mAP50–95 over the corresponding YOLOv8n, YOLO11n, and YOLO26n baselines by 3.29, 2.56, and 2.17 percentage points, respectively. The YOLO11n implementation achieved box mAP50–95 of 60.68 ± 0.11% and keypoint mAP50–95 of 43.50 ± 1.55%. TensorRT FP16 deployment on Jetson AGX Orin in MAXN mode yielded end-to-end latencies of 20.85–22.22 ms across the three backbones.