Smart livestock management using sensing technologies: applications, digital integration, and future perspectives

Smart livestock management is increasingly enabled by advances in sensing and biosensing technologies that support continuous, data-driven monitoring of animal health, welfare, reproduction, productivity, and environmental conditions. Recent digital innovations have further accelerated this transition, creating opportunities for more predictive and sustainable livestock management. This review synthesizes current knowledge on conventional sensors, emerging biosensing technologies, and their integration within smart livestock systems. It critically examines technology readiness, wearable, implantable, and environmental sensing platforms. It evaluates their applications in disease surveillance, animal welfare assessment, reproductive management, milk production optimization, and growth monitoring. The review further explores how the convergence of sensors and biosensors with the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), cloud and edge computing, and digital decision-support systems (DSS) enables continuous data integration, predictive analytics, and evidence-based management. Current barriers to large-scale implementation, including technical reliability, interoperability, data management, infrastructure requirements, economic feasibility, and regulatory and ethical considerations, are also discussed. The available evidence indicates that integrating sensing, biosensing, and digital technologies provides a more comprehensive understanding of animal physiological, behavioral, and environmental status than individual technologies alone, supporting earlier interventions and improved production efficiency. Continued advances in integrated monitoring platforms, intelligent analytics, and next-generation biosensors are expected to accelerate the transition toward predictive, resilient, and sustainable livestock production.