Dynamic weed control using selective laser application with object tracking and target scheduling

Selective laser application for weed control is emerging as one of the most sustainable practices for various crops. The system targets weeds using a laser beam with specific time and intensity settings to eliminate undesired plants through thermal damage. However, this process — commonly known as static weed laser treatment — reduces machinery efficiency, as the platform must remain stationary until all visible weeds are treated. To address this limitation, the current work proposes a dynamic laser weeding approach that predicts weed positions while the platform is in motion, thereby improving operational efficiency. Several deep learning architectures (e.g., YOLO series for weed detection and DeepSORT for weed tracking) are evaluated to identify the most effective models for detecting and tracking multiple weeds in RGB images. In addition, a time-constrained scheduling strategy is implemented to determine the order in which weeds are treated, minimizing the number of missed targets. We find that receding horizon control offers the best performance, particularly under strict time and energy constraints. Field deployment results show that YOLOv7 achieves the highest precision, recall, and mean Average Precision (mAP) for weed detection. The dynamic laser weeding system significantly outperforms the static counterpart, enabling up to 2.8 times faster movement while successfully treating 90% of detected weeds. This work presents a proof of concept for dynamic weeding, laying the foundation for future developments in intelligent, autonomous crop protection systems. • A dynamic laser weeding system enables real-time weed detection. • The dynamic system is up to 2 . 8 × faster than a comparable static solution. • YOLOv7 outperformed other architectures in precision, recall, and mAP for weed detection. • Receding horizon scheduling reduces energy use with high success rate.