DINOv3 Meets YOLO26 for Weed Detection in Vegetable Crops

Researchers developed a foundational crop-weed detection model combining DINOv3 vision transformer with YOLO26 architecture, achieving significant improvements in precision agriculture applications. The model showed up to 14% better performance on cross-domain datasets while maintaining real-time processing at 28.5 fps despite increased computational requirements.