Machine learning-assisted self-powered ear tag for animal welfare

fluctuations in interstitial fluid. By implementing a machine learning pipeline to decode the coordinated dynamics of these multi-ionic markers, the platform effectively distinguishes among five distinct welfare-related states with an average classification accuracy over 95%. This performance is rigorously validated via leave-one-animal-out cross-validation across 5399 sampling windows from three independent animals. By bridging energy-autonomous operation with intelligent metabolic profiling capabilities, the self-powered ear tag establishes a scalable technological paradigm for next-generation precision livestock health monitoring and welfare management.