Beef Cattle Body Weight Estimation Based on Dual-View RGB Images

Non-contact body weight (BW) estimation provides a low-stress and low-cost approach for precision beef cattle management, but single-view RGB images may not fully capture body-shape information. This study proposed a practical dual-view RGB framework for cattle BW estimation. A total of 3210 paired top-view and side-view RGB images were collected from 107 Simmental beef cattle with BW ranging from 169 to 980 kg. An EMA-enhanced YOLO11n-seg model was adopted to improve cattle foreground extraction, and a two-stream CBAM-ResNet50-SE network was constructed to learn dorsal and lateral morphological features for BW regression. The EMA-YOLO11n-seg model achieved mAP@0.5 values of 99.18% and 98.35% for top-view and side-view images, respectively. On the test set, the proposed BW estimation model achieved an MAE of 14.96 kg, an RMSE of 17.86 kg, and an R² of 0.85. The model also showed stable performance across different growth stages and posture conditions. Adaptation experiments using a Sanhe cattle dataset further demonstrated the adaptability of the proposed framework. These results suggest that the practical dual-view RGB framework developed in this study provides an effective solution for non-contact beef cattle BW estimation under fixed image-acquisition conditions.