A review of cattle behavior recognition methods based on computer vision and deep learning

Cattle behavior recognition is vital for precision livestock farming, supporting welfare assessment, health monitoring, and intelligent management. With advances in computer vision and deep learning, automated behavior analysis has evolved rapidly. This review comprehensively summarizes recent progress in cattle behavior recognition, focusing on datasets, model architectures, algorithmic strategies, and practical challenges. Methods based on convolutional networks, temporal and spatiotemporal modeling, and Transformer architectures are analyzed, highlighting their contributions to recognition accuracy and robustness. The review further discusses limitations such as weak generalization across complex environments, lack of standardized benchmarks, and deployment constraints in applications. Future directions include multimodal data fusion, lightweight edge models, and interpreted learning frameworks. To address current limitations regarding weak generalization across complex environments and deployment constraints, future research must prioritize multimodal data fusion, lightweight edge models, and interpreted learning frameworks to facilitate the transition from experimental models to practical on-farm applications.