AI & ML for Livestock Welfare: Analysing Behaviour and Stress Patterns for Better Care

Monitoring animal health, analysing behaviour, and preventing disease are all being revolutionized by the application of artificial intelligence (AI) and machine learning (ML) in livestock welfare. This study investigates how AI-driven models might improve cattle care by identifying patterns in a variety of data sources, including biometric tracking, video analysis, and sensor-based monitoring. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs) are examples of deep learning architectures that are highlighted in the study for their superiority over traditional methods in identifying stress, forecasting illnesses, and improving farm management. Numerous fields, including automated feeding, genetic improvement, environmental monitoring, and precision livestock husbandry, use AI and ML to improve the welfare of animals. This study also addresses the obstacles and potential paths for AI adoption in livestock care, with a focus on data privacy, ethical issues, and the requirement for flexible AI solutions catered to various animal species. Keywords— Artificial Intelligence, Machine Learning, Livestock Welfare, Precision Livestock Farming, Deep Learning, Animal Behavior Analysis, Stress Detection.