A noninvasive method for estimating the body weight (BW) of a pig considering its posture using a low-cost depth camera (Kinect v2) was proposed. A total of 150 pigs were used, and 738 depth images (point clouds) were obtained for them. The pig “volume” was calculated from the pig point cloud, and it was found to have a very high correlation to BW. To evaluate the posture of a pig quantitatively, seven posture angles were calculated based on the “spine” extracted from a pig point cloud. We found the posture angles representing the height of the head position correlated with the accuracy of BW estimation using the “volume.” Based on this finding, we proposed an “adjusted volume,” which was adjusted based on the relationship between the posture angles and the estimation error. The BW of pigs was estimated using the simple regression model with the “adjusted volume,” and the MAPE and RMSPE were 4.87% and 6.13%, respectively. The accuracy of the suggested model was similar to that of the volume-based estimation models of other studies that used only data with an appropriate pig posture for BW estimation.

中文翻译：

---

使用深度相机通过姿势分析估算猪的体重

提出了一种使用低成本深度相机 (Kinect v2) 考虑其姿势来估计猪体重 (BW) 的无创方法。总共使用了 150 头猪，并为它们获得了 738 个深度图像（点云）。猪的“体积”是根据猪的点云计算出来的，发现它与 BW 有很高的相关性。为了定量评估猪的姿势，根据从猪点云中提取的“脊柱”计算了七个姿势角。我们发现表示头部位置高度的姿势角与使用“体积”的 BW 估计精度相关。基于这一发现，我们提出了一种“调整体积”，它是根据姿势角和估计误差之间的关系进行调整的。使用具有“调整体积”的简单回归模型估算猪的 BW，MAPE 和 RMSPE 分别为 4.87% 和 6.13%。建议模型的准确性类似于其他研究中基于体积的估计模型的准确性，这些模型仅使用具有适当猪姿势的数据来估计 BW。