Department of Information Technology, Ghent University/imec, iGent-Technologiepark 126, 9052 Ghent, Belgium; Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Scheldeweg 68, 9090 Melle, Belgium.
Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Scheldeweg 68, 9090 Melle, Belgium; Department of Veterinary and Biosciences, Faculty of Veterinary Medicine, Heidestraat 19, B-9820 Merelbeke, Belgium.
Animal. 2023 Apr;17(4):100730. doi: 10.1016/j.animal.2023.100730. Epub 2023 Feb 11.
Cattle behaviour is fundamentally linked to the cows' health, (re)production, and welfare. The aim of this study was to present an efficient method to incorporate Ultra-Wideband (UWB) indoor location and accelerometer data for improved cattle behaviour monitoring systems. In total, 30 dairy cows were fitted with UWB Pozyx wearable tracking tags (Pozyx, Ghent, Belgium) on the upper (dorsal) side of the cow's neck. In addition to the location data, the Pozyx tag reports accelerometer data as well. The combination of both sensor data was performed in two steps. In the first step, the actual time spent in the different barn areas was calculated using location data. In the second step, accelerometer data were used to classify cow behaviour using the location information of step 1 (e.g., a cow located in the cubicles cannot be classified as feeding, or drinking). A total of 156 hours of video recordings were used for the validation. For each hour of data, the total time each cow spent in each area and performing which behaviours (feeding, drinking, ruminating, resting, and eating concentrates) were computed using the sensors and compared against annotated video recordings. Bland-Altman plots for the correlation and difference between the sensors and the video recording were then computed for the performance analysis. The overall performance of locating the animals into the correct functional areas was very high. The R was 0.99 (P < 0.001), and the root-mean-square error (RMSE) was 1.4 min (7.5% of the total time). The best performance was obtained for the feeding and lying areas (R = 0.99, P < 0.001). Performance was lower in the drinking area (R = 0.90, P < 0.01) and the concentrate feeder (R = 0.85, P < 0.05). For the combined location + accelerometer data, high overall performance (all behaviours) was obtained with an R of 0.99 (P < 0.001) and a RMSE of 1.6 min (12% of the total time). The combination of location and accelerometer data improved the RMSE of the feeding time and ruminating time compared to the accelerometer data alone (2.6-1.4 min). Moreover, the combination of location and accelerometer enabled accurate classification of additional behaviours that are difficult to detect using the accelerometer alone, such as eating concentrates and drinking (R = 0.85 and 0.90, respectively). This study demonstrates the potential of combining accelerometer and UWB location data for the design of a robust monitoring system for dairy cattle.
牛的行为与牛的健康、（再）繁殖和福利密切相关。本研究的目的是提出一种有效的方法，将超宽带（UWB）室内定位和加速度计数据结合起来，以改进牛的行为监测系统。总共 30 头奶牛在牛的颈部上方（背部）佩戴了 UWB Pozyx 可穿戴跟踪标签（Pozyx，根特，比利时）。除了位置数据外，Pozyx 标签还报告加速度计数据。传感器数据的组合分两步完成。在第一步中，使用位置数据计算牛在不同畜栏区域的实际停留时间。在第二步中，使用加速度计数据使用步骤 1 的位置信息对牛的行为进行分类（例如，位于牛舍中的牛不能归类为进食或饮水）。总共使用了 156 小时的视频记录进行验证。对于每小时的数据，使用传感器计算每头奶牛在每个区域花费的总时间以及执行的行为（进食、饮水、反刍、休息和吃浓缩物），并与注释的视频记录进行比较。然后为性能分析计算了传感器和视频记录之间的相关性和差异的 Bland-Altman 图。将动物定位到正确功能区域的整体性能非常高。R 为 0.99（P<0.001），均方根误差（RMSE）为 1.4 分钟（总时间的 7.5%）。在进食和躺卧区域的性能最好（R=0.99，P<0.001）。在饮水区（R=0.90，P<0.01）和浓缩物喂食器（R=0.85，P<0.05）的性能较低。对于组合的位置+加速度计数据，高整体性能（所有行为）为 0.99（P<0.001）和 RMSE 为 1.6 分钟（总时间的 12%）。与单独使用加速度计数据相比，位置和加速度计数据的组合提高了进食时间和反刍时间的 RMSE（2.6-1.4 分钟）。此外，位置和加速度计的组合能够准确地分类其他难以使用加速度计单独检测的行为，例如吃浓缩物和饮水（R=0.85 和 0.90）。本研究表明，将加速度计和 UWB 位置数据结合起来，可为奶牛的稳健监测系统设计提供潜力。
