Computer vision-enabled smart farms for cattle herd management in open-pasture

AbstractVirtual fencing is a promising alternative to conventional physical barriers for managing free-ranging livestock, offering flexibility, lower infrastructure costs, and potential livestock-welfare outcomes. Yet, there are significant gaps in the practical deployment of virtual fencing in open pastures. While all existing scientific and commercial systems primarily rely on GPS-equipped collars, these solutions remain limited by challenges in animal localization, behavior recognition, and adaptive boundary enforcement. Therefore, the article introduces a computer-vision (CV) framework for virtual fencing of cattle. The solution combines detection and tracking models with risk-based zone generation logic to deliver early warnings and CV-enabled boundaries for breach alerts in real-time. Both fixed cameras/UAVs are considered as potential data sources without GPS collars. Experimental evaluations in structured pasture settings using stationary cameras (with imitations related to spatial coverage, viewpoint dependence and occlusions, sensitivity to camera placement, environmental variability, and infrastructure and maintenance requirements) demonstrate reliable herd-level monitoring and accurate virtual-fence enforcement, quantified using detection precision/recall and tracking-relevant metrics including tracking consistency, count stability, crossing-event accuracy, and zone dwell time. Within the limits of controlled tests across three scenarios, the evaluation of eight quantitative and two qualitative Key Performance Indicators (KPIs) showed that as a proof-of-concept, The proposed system enables key digital-twin capabilities for open pasture environments through real-time computer vision, enabling live-stream monitoring, virtual boundary enforcement, and extensions to vegetation assessment and emergency management applications such as wildfire situational awareness and environmental crisis management.