Cattle Behavior Recognition and Modeling: A Review of Sensing Modalities, Intelligent Methods, and Engineering Applications

Abstract

Cattle behavior recognition is a key component of precision livestock farming, enabling continuous monitoring of animal health, welfare, and management conditions. This review provides a structured overview of recent advances in cattle behavior recognition from an engineering perspective. Existing studies are systematically summarized according to three main sensing paradigms: contact-based sensing using wearable devices, non-contact sensing based on vision and other remote techniques, and multimodal fusion approaches. Wearable sensing methods are reviewed with respect to sensor types, data acquisition, feature representation, and temporal modeling, demonstrating strong capability for fine-grained behavior recognition. Non-contact approaches, particularly vision-based methods, are discussed in terms of detection, tracking, pose estimation, and spatiotemporal analysis under realistic farm environments. In addition, multimodal fusion and behavior modeling methods are highlighted for their ability to improve robustness and support long-term monitoring, including digital twin–oriented behavior perception. Finally, engineering system design and practical deployment issues, such as energy efficiency, communication, edge computing, and long-term maintainability, are discussed, together with key challenges and future research directions. This review aims to provide a concise reference for developing reliable and scalable cattle behavior monitoring systems.