Dairy cow management depends on repeated observations of behavior and physical condition to support health, welfare, and operational decisions, but these observations remain labor-intensive. Deep learning (DL)-based computer vision can automate parts of this work, although deployment requirements differ among management operations. This operation-centered structured review retained 97 distinct publications. Five operations were compared: lameness detection, behavior recognition, individual identification and tracking, body condition score estimation, and body weight prediction. The comparison addresses management tasks, inputs, outputs, label requirements, validation designs, and deployment constraints. It identifies shared concerns involving reference-standard reliability, data-capture protocols, multi-animal scenes, identity continuity, cross-farm generalization, and data access. A qualitative three-part research agenda is proposed: candidate foundations in reference standards and identity continuity; transfer and data infrastructure; and conditional extensions involving multimodal and longitudinal evaluation.
Zhang et al. (Sun,) studied this question.