Artificial Intelligence for Early Detection of Heat Stress and Environmental Hazards in Dairy Animals

Abstract

Heat stress and environmental hazards significantly affect dairy animal health, welfare, milk productivity, and farm sustainability, while conventional monitoring methods often fail to provide timely and accurate early warnings. This review presents a comprehensive analysis of artificial intelligence (AI)-based approaches for the early detection of heat stress and environmental risks in dairy animals. The study examines recent advancements in machine learning, deep learning, computer vision, wearable sensing, Internet of Things (IoT), and multimodal data fusion for continuous livestock monitoring. A unified AI-based framework integrating environmental sensing, physiological measurements, behavioral analysis, and predictive analytics is proposed to enable proactive health management and real-time decision support. Furthermore, mathematical formulations for feature extraction, Heat Stress Index estimation, risk prediction, and model optimization are discussed to provide a systematic understanding of intelligent monitoring systems. The review compares existing AI techniques, identifies their strengths and limitations, and highlights challenges related to sensor reliability, data quality, model generalization, computational efficiency, and practical deployment in commercial dairy farms. Finally, emerging research directions, including edge AI, federated learning, explainable AI, and digital twin technologies, are outlined to improve prediction accuracy, scalability, and sustainability. The review offers researchers and practitioners a structured roadmap for developing intelligent livestock health monitoring systems that enhance animal welfare, productivity, and climate resilience.