AI Audio-Based Poultry Behavior Monitoring Using Vocal Sound Analysis

The aim is to develop a simple and efficient AI audio-based approach to recognize chickens’ key behaviors, such as eating, greeting, foraging, hunting, and tidbitting, to improve poultry farming. First, the proposed study performs cepstral and entropy analyses on the chickens’ vocalizations to extract new vocal features. Second, a simple deep unsupervised clustering method is proposed to recognize the behaviors of the chickens. Alternations in recognized behaviors can be indicators of lameness in chickens. Here, we used an open access chicken language dataset consisting of a total of 74 distinct chicken calls with their probable meanings as based on careful observations. Promising results are obtained by the proposed scheme for chicken behavior monitoring, enabling poultry personnel to accurately determine the health and well-being of chickens.