Automatic Detection of Cow’s Oestrus in Audio Surveillance System

Abstract

Early detection of anomalies is an important issue in the management of group-housed livestock. In particular, failure to detect oestrus in a timely and accurate way can become a limiting factor in achieving efficient reproductive performance. Although a rich variety of methods has been introduced for the detection of oestrus, a more accurate and practical method is still required. In this paper, we propose an efficient data mining solution for the detection of oestrus, using the sound data of Korean native cows (Bos taurus coreanea). In this method, we extracted the mel frequency cepstrum coefficients from sound data with a feature dimension reduction, and use the support vector data description as an early anomaly detector. Our experimental results show that this method can be used to detect oestrus both economically (even a cheap microphone) and accurately (over 94% accuracy), either as a standalone solution or to complement known methods.

Invited review: The evolution of cattle bioacoustics and application for advanced dairy systems

“…Therefore, with further research, the analysis of vocalisations could assist in the management of cattle during feeding, improve detection rates during oestrus, and determine stress responses during calf separation, social isolation and painful husbandry procedures, all of which are situations where cattle are commonly vocal (Watts and Stookey, 2000). There has been recent interest in utilising sound analysis as a potential welfare monitor in dairy cows (Meen et al, 2015), and some studies have already incorporated human-derived algorithms to analyse and then recognise specific cattle vocalisations (Jahns, 2008;Chung et al, 2013). For future application in advanced dairy systems, a greater understanding of the cattle vocal repertoire is first necessary to determine the information that the calls encode, why particular animals are vocal and whether changes to vocal parameters are consistent across ages, different breeds and situations.…”

A Cost-Effective Pigsty Monitoring System Based on a Video Sensor

“…Chung et al [23] proposed a data mining solution for the detection of oestrus using the sound data of cows. It first extracted the mel frequency cepstrum coefficients with a feature dimension reduction and then used the support vector data description as an early anomaly detector.…”

The Innovative Techniques in Animal Husbandry

“…Chung et al [86] reported that voice identification processing can be used to detect estrus both economically (simple microphone) and accurately (over 94% accuracy), either as a stand alone solution. The Mount Count manual version of the Heat Watch system is also available in the markets at more low price which is not required a computer or software to process and display the data.…”