Graduate School of Engineering, University of Miyazaki, 1 Chome-1 Gakuenkibanadainishi, Miyazaki 889-2192, Japan.
Center for Animal Disease Control, University of Miyazaki, 1 Chome-1 Gakuenkibanadainishi, Miyazaki 889-2192, Japan.
Sensors (Basel). 2020 Jul 2;20(13):3705. doi: 10.3390/s20133705.
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 () of 3.9%, and a mean absolute error () of 0.13.
奶牛的身体状况评分 (BCS) 表明了它们的能量储备，评分范围从非常瘦到超重。这些测量在分娩和泌乳早期特别有用。实现正确的 BCS 有助于避免分娩困难、损失和其他健康问题。虽然 BCS 可以由专家进行评估，但由不同专家进行评估既费时又常常不一致。因此，我们系统的目标是开发一种计算机化系统，以减少不一致性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 摄像机、图像处理技术和回归模型引入了自动身体状况评分系统。实验数据是在日本一个大型奶牛场的旋转式挤奶站收集的。该系统包括一个自动图像选择应用平台，作为一个主要步骤，该平台是为大型农场的个体奶牛的智能监控而开发的。此外，通过提取 3D 表面粗糙度参数，在两个感兴趣区域 (ROI) 中提出了两个分析模型。通过在数学方程中应用提取的参数，根据模型准确性的测量值自动评估 BCS，其中一个模型的平均绝对百分比误差 (MAPE) 为 3.9%，平均绝对误差 (MAE) 为 0.13。
