Climate-Smart Livestock Management Using Reinforcement Learning and Environmental Sensor Networks

Abstract

Climate change poses significant challenges to livestock production by increasing heat stress, disease prevalence, resource scarcity, and environmental impacts. This review examines recent advances in Climate-Smart Livestock Management through the integration of Environmental Sensor Networks, Artificial Intelligence, and Reinforcement Learning. The study synthesizes current research on intelligent sensing, Internet of Things connectivity, machine learning, deep learning, computer vision, explainable AI, and adaptive decision-making for monitoring environmental conditions, animal health, welfare, and resource utilization. Environmental sensor networks provide continuous acquisition of temperature, humidity, air quality, gas concentration, and physiological data, enabling timely detection of heat stress and disease risks. Reinforcement Learning enables autonomous optimization of ventilation, cooling, feeding, watering, and energy management by continuously interacting with dynamic farm environments. The review further identifies research gaps related to limited integration of sensing infrastructure, explainability, standardized datasets, scalability, and real-time implementation. A comprehensive framework integrating environmental sensing, intelligent analytics, and adaptive learning is presented to support climate-resilient livestock farming. The findings indicate that AI-driven management strategies improve animal comfort, productivity, resource efficiency, and sustainability while reducing greenhouse gas emissions and operational costs. Future research should emphasize federated learning, edge computing, digital twins, trustworthy AI, multimodal data fusion, and autonomous farm management for sustainable.