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| Behavior monitoring of dairy cattle using partitioningaround medoids and random forest model（PAM-RF） |
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| Abstract: |
| 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%. |
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