Cattle Community Extraction Using the Interactions Based on Synchronous Behavior

Abstract

In management of beef cattle, it is important to grasp cattle's unusual conditions such as estrus and disease as soon as possible. The methods to detect the condition of estrus based on information such as the amount of activity from pedometer have been proposed so far. In this paper, we propose an innovative method to grasp cattle's status by focusing on unique changes of communities in time series. Our method has a possibility that it can discover and deal with new cases which were not found on the amount of activity in previous method. To extract cattle's communities, the nature of cattle's behaviors that synchronize in the community is used. The cattle's walking speed is calculated by position information obtained from GPS collar, and their behaviors are classified. We quantify the duration of cattle's behaviors being synchronized, and create a graph to observe the relationship between cattle. Then, we extract communities from the graph, and analyze changes of communities in time series. In the proposed method, we focused on the size of community and discovered cases that the cattle's condition, especially estrus, changed accordingly due to the dynamic changes of communities.