Automatic recognition of ingestive-related behaviors of dairy cows based on triaxial acceleration

Abstract

Ingestive-related behaviors including feeding and ruminating are important indexes to measure the health and welfare of dairy cows. The purpose of this study is to develop a method based on triaxial acceleration to automatically recognize feeding and ruminating of dairy cows. During the experiment, five diary cows raised in a barn were used as experimental subjects. A triaxial acceleration sensor was used as the device to collect jaw-movement data of dairy cows, and the behaviors of dairy cows were classified into three categories: feeding, ruminating and other behavior. The features of time-domain and frequency-domain were extracted from the raw acceleration data. Three machine learning algorithms including k-nearest neighbor, support vector machine and probabilistic neural network were used for the classification and the results based on four different data segment lengths were compared. The results show that the three algorithms can be used for recognition of feeding and ruminating with high accuracy. Under the condition that the sampling frequency of the sensor is 5 Hz, the combination of data segment length of 256 and k-nearest neighbor algorithm is the best scheme for recognition of feeding and ruminating in this study. The precision and recall of recognition for feeding were 92.8% and 95.6% respectively, and those of recognition for ruminating were 93.7% and 94.3% respectively. The specificity and AUC of recognition for feeding were 96.1% and 0.959 respectively, and those of recognition for ruminating were 97.5% and 0.959 respectively. This will provide an effective method for real-time monitoring of ingestive-related behaviors of dairy cows and lay a foundation for prediction of dairy cows’ health status and welfare to further achieve the purpose of disease prediction and adjusting feeding and management methods.