ANIMAL HEALTH AND SAFETY MONITORING

Computer-implemented methods, systems and computer program products leveraging cognitive learning, and machine learning algorithms to predictively identify and monitor animals within a monitoring zone in real-time and identify an occurrence of unwanted or unsafe behaviors being performed by the animals. Surveillance systems and sensors within a monitoring zone or affixed to the animals provide audio/visual data and sensor data describing activity and animals within the monitoring zone. Machine learning models are trained using audio-visual, sensor and historical data to learn to predict the identities of registered animals based on the sight or sound of the animals. Behaviors of animals that are unsafe and should be corrected can be remediated, minimized or altered using IoT devices positioned within the monitoring zone perform pre-determined actions initiated automatically in response to identification of unsafe behaviors or upon verification by users of the occurrence of the unsafe events or conditions within the monitoring zone.