PD-YOLO: a novel weed detection method based on multi-scale feature fusion

Experimental results on two public weed datasets demonstrate the superior performance of PD-YOLO over state-the-art models, with a modest increase in computational cost. PD-YOLO improves the mean average precision (mAP) by 1.7% and 1.8% on the CottonWeedDet12 dataset at thresholds of 0.5 and 0.5-0.95, respectively. This research not only presents an efficient and accurate weed detection method but also offers new insights and technological advances for automated weed detection in agriculture.