RGC-YOLO: a lightweight YOLOv8n-based detector for edge-deployable weed detection in Xinjiang cotton fields.

The cotton field weed detection model must balance detection accuracy, model size, and inference efficiency when deployed at the edge. This study constructed a dataset comprising four types of weeds based on field images collected from cotton fields in Xinjiang and proposed a lightweight detection model, RGC-YOLO, based on YOLOv8n. The model incorporates R-UIB modules into the deep layers of the backbone network to reduce the number of parameters and computational load during the high-channel feature extraction stage. A G-C2f module is introduced at the P5 feature fusion point in the neck network to improve the efficiency of deep semantic feature fusion. Additionally, a DIoU loss function is employed to strengthen the constraints on bounding box localization. The results show that, compared to YOLOv8n, RGC-YOLO achieves a 2.65% improvement in the mAP@0.75, while reducing the number of parameters and GFLOPs by 35.22% and 15.37%, respectively. This indicates that the model effectively reduces complexity while enhancing high-IoU localization performance. Eigen-CAM analysis further demonstrates that RGC-YOLO produces more concentrated feature responses in weed target regions and reduces irrelevant background activations. On the Jetson AGX Orin platform, the FP16 TensorRT engine for RGC-YOLO achieves high throughput and a compact footprint, demonstrating that its lightweight design translates into benefits for edge deployment. External validation on a sesame-field binary crop-weed dataset and the 2SeasonWeedDet8 multi-class weed dataset further confirms that RGC-YOLO maintains comparable detection performance while preserving its lightweight advantage across different agricultural scenarios.