Emerging technologies including sensor networks, computer vision, and machine learning are transforming our ability to monitor farm animal welfare at scale with greater precision than human observation alone.
Technology-based welfare monitoring offers transformative potential by making continuous, objective assessment possible at scales that human observation cannot achieve. Traditional welfare assessment relies on periodic visits by trained assessors, creating temporal gaps where problems may develop unseen. Sensor systems provide real-time data streams that enable early intervention before welfare problems become severe. Computer vision lameness scoring in dairy cows has been shown to identify lame cows earlier than farmer observation, enabling faster treatment. However, technology cannot replace human care and stockperson skill; it complements and augments rather than substitutes. Regulatory frameworks need to evolve to incorporate technology-based welfare evidence and create incentives for adoption across the sector.