Estrus Detection in Dairy Cows from Location and Acceleration Data using Machine Learning

Abstract

Accurate and timely detection of estrus, in cows, in dairy farms is very important for reproduction, health and milk production. Traditional estrus detection methods like manual observation and chin head chalking are outdated and not suitable for the dairy farm because of large number of animals. A lot of automated estrus detection methods have been proposed like milk yield fluctuation, milk progesterone detection etc., but they are either too complex to implement or have low detection rate. Whereas, the proposed estrus detection method can be easily implemented, cheaply and accurately. This method uses features extracted from 3D acceleration data, obtained using accelerometer attached to cow’s neck. The data is then clustered using k-means into 3 clusters. Categories are assigned based on data variance. As a result, the three clusters are categorized as: low activity, medium activity, or high activity. Based on this information, activity index is calculated and then it is used for the estrus detection. Machine learning classifiers including SVM, and D-trees are used for the activity recognition. SVM and D-tree demonstrate an accuracy of 96% and 86% respectively.