Activity-Integrated Hidden Markov Model to Predict Calving Time.

Abstract

Simple SummaryDairy cows are known to become more active during the time calving approaches. Dairy farms provide individual calving pens to monitor the behavior of pregnant cows. Frequent posture changes such as alternating between lying and standing are good indicators that calving is imminent. In this paper, we aimed to determine how using these behavior changes or activities could help predict calving time. The activity monitoring video cameras in this study were located at a top corner of the calving pens so that the whole pens are visible. By processing the collected video sequences, the activities of pregnant cows three days before the calving were modeled in a Hidden Markov Model to predict the time when the calving event occurs. The experimental results show that the proposed method has promise.Accurately predicting when calving will occur can provide great value in managing a dairy farm since it provides personnel with the ability to determine whether assistance is necessary. Not providing such assistance when necessary could prolong the calving process, negatively affecting the health of both mother cow and calf. Such prolongation could lead to multiple illnesses. Calving is one of the most critical situations for cows during the production cycle. A precise video-monitoring system for cows can provide early detection of difficulties or health problems, and facilitates timely and appropriate human intervention. In this paper, we propose an integrated approach for predicting when calving will occur by combining behavioral activities extracted from recorded video sequences with a Hidden Markov Model. Specifically, two sub-systems comprise our proposed system: (i) Behaviors extraction such as lying, standing, number of changing positions between lying down and standing up, and other significant activities, such as holding up the tail, and turning the head to the side; and, (ii) using an integrated Hidden Markov Model to predict when calving will occur. The experiments using our proposed system were conducted at a large dairy farm in Oita Prefecture in Japan. Experimental results show that the proposed method has promise in practical applications. In particular, we found that the high frequency of posture changes has played a central role in accurately predicting the time of calving.