A Novel Method for Estimating the Body Weight and Size of Sows Using 3D Point Cloud

Body weight and size are critical indicators of sow health and reproductive performance. Traditional manual measurement methods are not only time-consuming and labor-intensive but also induce stress in sows. To address these limitations, we propose an innovative method for estimating sow body weight and size using 3D point cloud data. Our method began by obtaining point cloud data from depth images captured by an Intel^® RealSense™ D455 camera. First, we used a KPConv segmentation model with a deformable kernel to extract the sow's back. The resulting back point cloud then served as the input to a novel dual-branch, multi-output regression model named DbmoNet, which integrates features from both location and feature spaces. We evaluated the method on 2400 samples from three breeds during non-pregnant periods. The KPConv model demonstrated excellent performance, achieving an overall segmentation accuracy (OA) of 99.54%. The proposed DbmoNet model outperformed existing benchmarks, achieving mean absolute percentage errors (MAPEs) of 3.74% for body weight (BW), 3.97% for chest width (CW), 3.33% for hip width (HW), 3.82% for body length (BL), 1.94% for chest height (CH), and 2.43% for hip height (HH). Therefore, this method provides an accurate and efficient tool for non-contact body condition monitoring in intensive sow production.