A Context-Aware Method Based Cattle Vocal Classification for Livestock Monitoring in Smart Farm

This paper focuses on livestock monitoring in smart farm to improve animal well-being and production. The great potential for increased automation and technological innovation in agriculture help livestock farmers in monitoring the welfare of their animals for precision livestock farming. A new acoustical method exploiting contextual information is introduced for cattle vocal classification. The proposed scheme considers the raw recordings which contain cattle sounds. Then a set of contextual acoustic features is constructed as input to the MSVM classifier to track the types of cattle vocalizations. Categorized noisy cattle calls are finally classified into four types of calls (i.e. cattle food anticipating call, animal estrus call, cough sound, and normal call) with an overall classification accuracy of 89.80% outperforming the results obtained using conventional MFCC features. We have used an open access dataset consists of 270 cattle classification records acquired using multiple sound sensors. Promising results are obtained by the proposed method for livestock monitoring enabling farm owners to determine the status of their cattle.