Multi-Sensor Fusion Fails to Generalize for Cattle Posture Classification

Automated systems for classifying cattle posture (lying vs. standing) often report very high accuracy, but their reliability in real-world, long-term deployments has been largely unexamined. This research specifically investigated whether combining data from multiple sensors—like collar accelerometers, rumen-bolus sensors, and environmental measurements—actually improves a model's ability to generalize, or if it leads to reliance on context-specific signals that fail over time. The study evaluated posture classification models using data collected from a beef cattle herd over two consecutive years. While multimodal models achieved strong performance within a single year (macro-F1 of 0.94), their performance drastically declined when evaluated across years (macro-F1 of 0.49). This significant drop occurred under a "cross-year evaluation" protocol, which tested the models on an entirely new cohort of animals recorded a year later. Analysis showed that the models persistently relied on rumen-bolus activity and environmental variables, even when these features became less predictive due to distribution shifts between the recording years. The findings underscore that standard evaluation methods can greatly overestimate a system's real-world readiness. It also suggests that, contrary to intuition, multimodal sensor fusion might sometimes decrease robustness when faced with temporal distribution shifts, emphasizing the need for more rigorous, robustness-focused evaluation in livestock monitoring.