The Body Condition Score (BCS) for cows indicates their energy reserves, the scoring for which ranges from very thin to overweight. These measurements are especially useful during calving, as well as early lactation. Achieving a correct BCS helps avoid calving difficulties, losses and other health problems. Although BCS can be rated by experts, it is time-consuming and often inconsistent when performed by different experts. Therefore, the aim of our system is to develop a computerized system to reduce inconsistencies and to provide a time-saving solution. In our proposed system, the automatic body condition scoring system is introduced by using a 3D camera, image processing techniques and regression models. The experimental data were collected on a rotary parlor milking station on a large-scale dairy farm in Japan. The system includes an application platform for automatic image selection as a primary step, which was developed for smart monitoring of individual cows on large-scale farms. Moreover, two analytical models are proposed in two regions of interest (ROI) by extracting 3D surface roughness parameters. By applying the extracted parameters in mathematical equations, the BCS is automatically evaluated based on measurements of model accuracy, with one of the two models achieving a mean absolute percentage error (MAPE) of 3.9%, and a mean absolute error (MAE) of 0.13.

中文翻译：

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基于使用3D相机进行回归分析的身体状况得分估算。

母牛的身体状况评分（BCS）表示他们的能量储备，其得分范围从非常瘦到超重。这些测量在产犊以及早期泌乳期间特别有用。实现正确的BCS有助于避免产犊困难，损失和其他健康问题。尽管BCS可以由专家评估，但它耗时且在由不同专家执行时常常不一致。因此，我们系统的目的是开发一种计算机化的系统，以减少不一致之处并提供节省时间的解决方案。在我们提出的系统中，通过使用3D相机，图像处理技术和回归模型引入了自动人体状况评分系统。实验数据是在日本大型奶牛场的旋转客厅挤奶站上收集的。该系统包括一个用于自动选择图像的主要应用程序平台，该平台是为大型农场中的个体母牛的智能监控而开发的。此外，通过提取3D表面粗糙度参数，在两个感兴趣的区域（ROI）中提出了两个分析模型。通过将提取的参数应用于数学方程式，可基于模型准确性的测量结果自动评估BCS，两个模型之一的平均绝对误差（MAPE）为3.9％，平均绝对误差（MAE）为0.13 。