Deep learning for visual animal monitoring (detection, tracking, pose estimation, and behavior classification): A comprehensive review

• Advances in deep learning improve detection, tracking, and behavior analysis. • YOLO, DeepSORT/ByteTrack, HRNet, and CNN-LSTM show strong performance. • Current challenges include limited datasets, high computation, and complex scenarios. • Future directions focus on diverse datasets, efficient models, and multimodal methods. The automation of animal monitoring is important for precision livestock farming as a means to enhance animal welfare, ensure biosecurity, and optimize productivity. This review provides an in-depth analysis of recent advancements in deep learning applications for four key tasks associated with automatic animal monitoring: detection, tracking, pose estimation, and behavior classification. Leading models such as YOLO, R-CNN, DeepSORT, ByteTrack, HRNet, DeepLabCut, and CNN-LSTM are examined in detail. Their performance is evaluated in terms of accuracy, computational efficiency, and adaptability across various species and farm environments. Key performance metrics, including mAP, MOTA, PCKh, and the F1-score, are reported, while issues such as limited cross-species generalization, data annotation bottlenecks, and the lack of animal monitoring during transportation are discussed. A novel taxonomic framework is proposed to guide model selection, providing a structured approach that aligns specific deep learning methods with distinct use cases and operational needs. Emphasis is placed on the integration of high-quality datasets and strategic annotation to improve the reliability and real-world applicability of these models. Collectively, this review aims to bridge scientific knowledge and real-world applications, offering researchers and practitioners actionable insights for the establishment of robust, scalable, and welfare-oriented monitoring systems.