Dairy farms are quietly becoming some of the most data-intensive environments in agriculture.
Welfare assessment on most dairy farms still depends on periodic human observation and a short list of visible indicators.
Current evidence is strongest for task-level environmental and behavioral monitoring and selected anomaly detection; it remains less mature for validated latent welfare-state inference, trajectory forecasting and closed-loop control.
Subject of Research: AI and IoT architectures for continuous dairy cattle welfare assessment and digital twin decision support Article Title: AI-IoT architectures for dairy welfare assessment: multimodal monitoring, deployment constraints, and the emerging role of digital twins Article References: Zhang, Y., Bai, D., Li, Y., Han, G., Xie, X., Gotoh, T., Zhao, K., & Ma, Y.
AI-IoT architectures for dairy welfare assessment: multimodal monitoring, deployment constraints, and the emerging role of digital twins.