Department of Agriculture, Food and Environment (Di3A)-Building and Land Engineering Section, University of Catania, Via Santa Sofia n° 100, 95123 Catania, Italy.
Natural Resources Institute Finland, Luke Latokartanonkaari 9, 00790 Helsinki, Finland.
Sensors (Basel). 2023 May 18;23(10):4873. doi: 10.3390/s23104873.
In the field of precision livestock farming, many systems have been developed to identify the position of each cow of the herd individually in a specific environment. Challenges still exist in assessing the adequacy of the available systems to monitor individual animals in specific environments, and in the design of new systems. The main purpose of this research was to evaluate the performance of the SEWIO ultrawide-band (UWB) real time location system for the identification and localisation of cows during their activity in the barn through preliminary analyses in laboratory conditions. The objectives included the quantification of the errors performed by the system in laboratory conditions, and the assessment of the suitability of the system for real time monitoring of cows in dairy barns. The position of static and dynamic points was monitored in different experimental set-ups in the laboratory by the use of six anchors. Then, the errors related to a specific movement of the points were computed and statistical analyses were carried out. In detail, the one-way analysis of variance (ANOVA) was applied in order to assess the equality of the errors for each group of points in relation to their positions or typology, i.e., static or dynamic. In the post-hoc analysis, the errors were separated by Tukey's honestly significant difference at > 0.05. The results of the research quantify the errors related to a specific movement (i.e., static and dynamic points) and the position of the points (i.e., central area, perimeter of the investigated area). Based on the results, specific information is provided for the installation of the SEWIO in dairy barns as well as the monitoring of the animal behaviour in the resting area and the feeding area of the breeding environment. The SEWIO system could be a valuable support for farmers in herd management and for researchers in the analysis of animal behavioural activities.
在精准畜牧领域，已经开发出许多系统来识别特定环境中每头奶牛的位置。评估现有系统在特定环境中监测个体动物的充分性以及设计新系统仍然存在挑战。本研究的主要目的是通过实验室条件下的初步分析，评估 SEWIO 超宽带 (UWB) 实时定位系统在牛群活动期间识别和定位奶牛的性能。目标包括量化系统在实验室条件下的误差，并评估该系统是否适合实时监测奶牛在奶牛舍中的活动。通过使用六个锚点，在实验室的不同实验设置中监测静态和动态点的位置。然后，计算与点的特定运动相关的误差，并进行统计分析。具体而言，应用单因素方差分析 (ANOVA) 来评估与点的位置或类型（静态或动态）相关的每组点的误差是否相等。在事后分析中，使用 Tukey Honestly Significant Difference 进行误差分离， > 0.05。研究结果量化了与特定运动（即静态和动态点）和点位置（即研究区域的中心区域、周边区域）相关的误差。基于这些结果，为在奶牛舍中安装 SEWIO 以及监测繁殖环境的休息区和喂食区的动物行为提供了具体信息。SEWIO 系统可以为农民的畜牧业管理和动物行为活动分析研究人员提供有价值的支持。
