Abstract:

[Objective] Laser weeding, as an emerging non‑chemical and environmentally friendly solution, offers high precision and immediate action without causing soil disturbance or herbicide residues. However, existing laser systems based on galvanometer scanners have limited scanning ranges; covering wide working widths requires multiple parallel laser units, which substantially increases costs. In addition, variations in weed height and uneven terrain further compromise targeting accuracy. To overcome these challenges and achieve precise and efficient weed control in open‑field vegetable production, a novel dynamic laser targeting system integrating an improved deep‑learning detector, binocular vision, and a parallel robotic arm was proposed. [Methods] Three common weed species-Amaranthus retroflexus, Eleusine indica, and Chenopodium album-which are prevalent in open‑field vegetable fields in the Beijing-Tianjin-Hebei region were selected as targets. A weed image dataset was constructed from 4 200 field‑collected images captured under diverse weather and lighting conditions and was augmented to 16 800 images via rotation, brightness adjustment, blurring, and contrast enhancement; all images were manually annotated in YOLO format. An improved HAD‑YOLO model was developed for real‑time weed detection, and trained on an NVIDIA RTX 3090 GPU using stochastic gradient descent. A hardware platform was established comprising an Intel RealSense D455 binocular camera, an OY‑D‑S‑350 parallel robotic arm, a blue‑laser emitter, an NVIDIA AGX Orin edge computing device, and a robotic arm controller. Custom host‑computer software was developed for model weight selection, confidence and IoU threshold adjustment, and TCP communication with the arm. Coordinate transformation from pixel to robot base coordinates was achieved using camera intrinsic/extrinsic parameters, while depth information was actively acquired through the binocular camera's infrared structured‑light system. Kalman filtering and the Hungarian algorithm were employed for multi‑frame target tracking, and a virtual line was set within the camera's field of view to trigger coordinate transmission when weeds crossed that line. A FIFO‑based path‑planning strategy was implemented to schedule laser tracking sequences, and real‑time encoder pulse counts were used to estimate target positions after they left the camera's field of view, enabling dynamic targeting on the moving conveyor belt. Static positioning accuracy experiments and dynamic laser weeding trials were conducted, with weed mortality assessed at 5 and 7 days post‑treatment based on meristem necrosis and absence of new shoots. [Results and Discussions] Static positioning experiments showed that HAD‑YOLO achieved a mean average precision (mAP) of 94.2% across the three species, outperforming the baseline YOLOv5 model. The average relative error in the X and Y directions was 1.73%, meeting the precision requirement for targeted laser weeding. In dynamic weeding trials, the system achieved a detection rate of 97% and an overall laser hit rate of 90% across all weeds. Post‑hit mortality rates were 86.67% for Amaranthus retroflexus, 89.13% for Chenopodium album, and 81.82% for Eleusine indica. Overall weed elimination rates (hit and subsequently dying) were 78%, 82%, and 72% for the three species, respectively, yielding a combined elimination rate of 77% for all weeds. Observations over seven days showed progressive necrosis, tissue carbonization, and loss of meristem activity in targeted weeds, indicating effective physiological damage. [Conclusions] The proposed dynamic laser targeting system, combining HAD‑YOLO detection, binocular vision localization, and parallel robotic arm control, showed reliable performance with high detection accuracy, acceptable positioning precision, and effective weed elimination. This study provides a feasible technological pathway for intelligent, chemical‑free weed control and may help reduce herbicide dependence and environmental pollution. The findings provide a technical reference for laser‑targeted weeding.

Key words: weed identification, HAD-YOLO, weed localization, laser weeding, precision agriculture, weed control, Kalman filtering, Hungarian algorithm