BR-YOLOv9: a multi-scale fusion framework for robust weed recognition in rice paddy fields.

To address the low accuracy and limited generalization of intelligent weed recognition under complex paddy-field conditions involving illumination variation, vegetation occlusion, multi-scale targets, and complex background interference, this study proposes an improved YOLOv9-based weed detection model, termed BR-YOLOv9. First, a weighted bidirectional feature pyramid network (BiFPN) was introduced into the neck network of YOLOv9 to strengthen cross-scale feature fusion and improve the representation of small and multi-scale weed targets. Subsequently, the original RepNCSPELAN4 module was reconstructed, and a novel RepFEL module was designed by integrating feature enhancement and a large selective kernel mechanism to improve fine-grained feature extraction and suppress background interference. Experimental results showed that BR-YOLOv9 achieved mAP@0.5 and mAP@0.5:0.95 values of 98.21% and 95.97%, respectively, representing improvements of 2.03 and 3.71 percentage points over the original YOLOv9 model. In the generalization experiments, BR-YOLOv9 was further evaluated under multiple representative field scenarios, including close-up, long-distance, high-illumination, low-illumination, and multi-target conditions. The results showed that BR-YOLOv9 maintained stable detection performance across different imaging distances, illumination conditions, and target densities, demonstrating stronger robustness and generalization capability in complex agricultural environments. The proposed model provides a robust visual perception method for paddy-field weed detection and offers technical support for precision weed management and intelligent weeding equipment.