AI APPLICATIONS IN LIVESTOCK BEHAVIOR MONITORING AND WELFARE

Abstract

Watching how cattle lead their lives through the use of artificial intelligence (AI) is a huge leap towards a more accurate control over animal welfare.  This research concerned how effectively AI-enabled tools, such as computer vision algorithms, wearable sensors, and predictive analytics could identify behavioral shifts, stress indicators, and potential health concerns in livestock herds.  The findings revealed that Al models were very good in locating things particularly in the context of detecting early indicators of stress, visualizing anomalies, and catagorizing welfare situations. All these far surpassed the traditional manual system of monitoring.  Data gathered by multiple sensors in the machine learning pipelines allowed identifying minor changes in behaviour preceding the clinical manifestation.  This reduced the incidences of welfare and enhanced herd management plans through quick actions made possible by the addition of real-time monitoring features.  The statistical study revealed an outstanding correlation between predicted welfare measures using AI and real health outcomes which confirmed that the system was a good predictor.  These findings indicate that AI can assist ethical farming, as well as facilitate the decision-making process in the animal livestock management process and provide a smoother running of operations.  The work reveals that monitoring animals through the use of AI generates a scalable, accurate, and welfare-enhancing method of monitoring animal behaviour. It is a giant leap towards developing sustainable and compassionate livestock production system.