Behavior monitoring of dairy cattle using partitioning around medoids and random forest model（PAM-RF）

Intelligent monitoring of cows’ physiological status is an important tool to achieve the modernization of large-scale dairy farming. According to the problems of monitoring cows’ behavior，this paper proposed an behavior monitoring model of dairy cattle using wireless sensor networks technology based on the combination of PAM algorithm and random forest algorithm. It used a three-axis accelerometer monitoring as a means to digitally capture the behavior of cows，unsupervised classification algorithm PAM acts as a training set of samples，which combined with supervised random forest-based algorithm acts as a mathematical model of the cow activity classification. And it built a time series of cows’ behavior index，based on the classification of cows’ behavior accurately，to monitor abnormal situations of dairy cattle. Experimental results showed that the model could effectively distinguish the high，medium and low intensity of three different cows’ behaviors. The average classification accuracy rate of the model was above 91%，and both high intensity and low intensity classification accuracy rate reached 95%. And the time series of cows’ activity intensity index could effectively monitor the occurrence of estrus situations and the accuracy rate of monitoring estrus of dairy cattle reached 91.67%.