AI-enabled electromagnetic sensor architecture for precision welfare monitoring in small ruminant production systems

Climate change and increasing heat stress present growing challenges for the welfare and productivity of small ruminants raised in arid and hot environments. In many production systems, animal health and welfare are still assessed primarily through visual observation, which may delay the detection of subtle physiological or behavioural changes associated with emerging welfare risks. Recent developments in precision livestock farming offer new possibilities for continuous and objective monitoring of animal conditions through advanced sensing technologies. This review explores recent progress in electromagnetic sensing technologies and their integration with artificial intelligence (AI) for monitoring animal welfare in small ruminant production systems. A literature search was conducted using the Web of Science, Scopus, and Google Scholar databases, focusing mainly on studies published over the past decade related to sensor-based livestock monitoring, electromagnetic sensing approaches, and AI-supported welfare assessment. Studies examining physiological, behavioural, or environmental indicators relevant to livestock welfare were considered. Recent studies indicate that electromagnetic sensing technologies—including microwave antennas, radar-based monitoring systems, metamaterial sensors, and dielectric sensing devices—can detect a range of welfare-related indicators such as respiration patterns, locomotor activity, and hydration status. When combined with machine-learning algorithms, these systems enable the interpretation of complex data streams and support more proactive approaches to welfare management. Based on the reviewed literature, this paper proposes a conceptual framework that integrates three key monitoring modules thermal comfort, locomotor stability, and hydration balance to illustrate how multi-sensor data can be analyzed through AI-driven approaches in small-ruminant production systems under arid climatic conditions. While these technologies show considerable promise, practical challenges remain, including sensor robustness in farm environments, economic feasibility, and the need for appropriate farmer training. Further research is therefore required to evaluate integrated sensor–AI systems under commercial farming conditions and to develop scalable decision-support tools for precision livestock management. To the best of our knowledge, this is among the first reviews to specifically focus on the integration of electromagnetic sensing technologies and artificial intelligence for welfare monitoring in small ruminants under arid climatic conditions.