College of Agriculture, Ibaraki University, Ami, Ibaraki, Japan.
United Graduate School of Agricultural Science, Tokyo University of Agriculture and Technology, Fuchu, Japan.
Anim Sci J. 2021 Jan-Dec;92(1):e13626. doi: 10.1111/asj.13626.
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.
提出了一种使用低成本深度相机(Kinect v2)考虑猪姿势来估算猪体重(BW)的非侵入性方法。共使用了 150 头猪,为它们获得了 738 张深度图像(点云)。从猪点云中计算出了猪的“体积”,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”计算了七个姿势角度。我们发现代表头部位置高度的姿势角度与使用“体积”进行 BW 估计的准确性相关。基于这一发现,我们提出了一种“调整后的体积”,该体积是根据姿势角度与估计误差之间的关系进行调整的。使用“调整后的体积”的简单回归模型来估算猪的 BW,MAPE 和 RMSPE 分别为 4.87%和 6.13%。该模型的准确性与其他仅使用适合 BW 估计的猪姿势数据的基于体积的估计模型相似。