PDF(Pubmed)
Abstract：
Global Positioning Systems (GPSs) can collect tracking data to remotely monitor livestock well-being and pasture use. Supervised machine learning requires behavioral observations of monitored animals to identify changes in behavior, which is labor-intensive. Our goal was to identify animal behaviors automatically without using human observations. We designed a novel framework using unsupervised learning techniques. The framework contains two steps. The first step segments cattle tracking data using state-of-the-art time series segmentation algorithms, and the second step groups segments into clusters and then labels the clusters. To evaluate the applicability of our proposed framework, we utilized GPS tracking data collected from five cows in a 1096 ha rangeland pasture. Cow movement pathways were grouped into six behavior clusters based on velocity (m/min) and distance from water. Again, using velocity, these six clusters were classified into walking, grazing, and resting behaviors. The mean velocity for predicted walking and grazing and resting behavior was 44, 13 and 2 min/min, respectively, which is similar to other research. Predicted diurnal behavior patterns showed two primary grazing bouts during early morning and evening, like in other studies. Our study demonstrates that the proposed two-step framework can use unlabeled GPS tracking data to predict cattle behavior without human observations.
摘要：
全球定位系统（GPS）可以收集跟踪数据，以远程监控牲畜福祉和牧场使用情况。有监督的机器学习需要对被监控的动物进行行为观察，以识别行为的变化。这是劳动密集型的。我们的目标是在不使用人类观察的情况下自动识别动物行为。我们使用无监督学习技术设计了一个新颖的框架。该框架包含两个步骤。第一步使用最先进的时间序列分割算法对牛跟踪数据进行分割，第二步将段分组为集群，然后标记集群。为了评估我们提出的框架的适用性，我们利用了从1096公顷牧场中的五头母牛收集的GPS跟踪数据。根据速度（m/min）和与水的距离，将牛的运动路径分为六个行为簇。再一次,使用速度，这六个集群被分类为步行，放牧,和休息行为。预测的步行，放牧和休息行为的平均速度为44、13和2分钟/分钟，分别,这与其他研究类似。预测的昼夜行为模式显示了清晨和傍晚的两次主要放牧，就像其他研究一样。我们的研究表明，所提出的两步框架可以使用未标记的GPS跟踪数据来预测牛的行为，而无需人类观察。
