Precise weed identification and differentiated laser weeding strategies for Salvia miltiorrhiza fields based on an enhanced object detection network

Effective weed control is crucial for Salvia miltiorrhiza cultivation, yet traditional methods are often inefficient, costly, or polluting. To address this, this study developed a laser weeding robot based on an improved object detection model capable of identifying weeds and implementing targeted strategies. First, a self-propelled laser weeding robot was constructed for Salvia miltiorrhiza fields to meet operational requirements. Second, a real-world field dataset was established for Salvia miltiorrhiza and five weed families. The detection model, optimized from the You Only Look Once (YOLO) architecture, integrates attention-based feature interaction, dynamic spatial attention, and small object feature enhancement modules. These improvements enhanced the features of small objects, improved occluded target localization, and strengthened similar object discrimination. Third, drawing on weed biological characteristics, a multi-level, differentiated laser weeding strategy was developed to precisely target growth points while ensuring crop safety. Finally, the model and strategy were deployed on the robot to perform real-time detection and intelligent laser weeding. Test results demonstrate the superior performance of the proposed model: the precision of object detection reached 78.09% (2.54% over baseline) and that of keypoint detection stood at 80.69% (8.22% over baseline). The mean average precision ( mAP50 ) metrics improved to 78.14% and 80.56%, representing increases of 2.34% and 2.88% respectively. Field tests achieved a 90.2% weed control rate alongside a low 1.9% damage rate to Salvia miltiorrhiza . These results validate the system's effectiveness and practicality, providing crucial technical support for intelligent weed management in Salvia miltiorrhiza and other high-value medicinal crops. • A wheeled laser weeding robot designed and constructed for Salvia miltiorrhiza fields. • WEED-YOLO model for precise, real-time Salvia miltiorrhiza and weeds detection. • Differentiated laser weeding strategy based on weed biological traits established. • Field tests showed 90.2% weed control and only 1.9% Salvia miltiorrhiza damage.