Abstract
Cattle behaviour patterns provide significant information about cattle health. Therefore, early behaviour recognition may help breeders be aware of cattle health problems promptly to have appropriate treatment to reduce negative impact. In this paper, an approach to cow behaviour recognition based on accelerated data will be proposed. The behaviour recognition model is built using random forest algorithm. This study focuses on four popular behaviours, i.e. walking, standing, eating (grazing), and lying. The model is validated using a real cow activity datatset. The overall classification result of the model is about 95% of accuracy. The comparison on the classification result with other recent approaches is provided. It is shown that the proposed approach in this paper is promising, and it can be used for developing cow behavior recognition application.
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