Multi-Sensor Integration and Machine Learning for High-Resolution Classification of Herbivore Foraging Behavior

The study classified cows' foraging behaviors using machine learning (ML) models evaluated through Random Test-Split (RTS) and Cross-Validation (CV). Models in-cluded Perceptron, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Random Forest (RF), and XGBoost (XGB). These models classified activity states (Active vs. Static), foraging behaviors (Grazing (GR), Resting (RE), Walking (W), Ruminating (RU)), posture states (Standing up (SU) vs. Lying down (LD)), and activity-by-posture combinations (RU_SU, RU_LD, RE_SU, RE_LD). XGB achieved the highest accuracy for state classification (74.5% RTS, 74.2% CV) and foraging behavior (69.4% CV). RF out-performed XGB in other classifications, including GR, RE, and RU (62.9% CV vs. 56.4% RTS), posture (83.9% CV vs. 79.4% RTS), and activity-by-posture (58.8% CV vs. 56.4% RTS). Key predictors varied: Speed and Actindex were crucial for GR and W when in-creasing and for RE and RU when decreasing. X low values were linked to RE_SU and RU_SU, while X and Z influenced RE_LD more. RTS showed higher accuracy in be-havioral state and general foraging classification. These results emphasize CV in RF's reliability in managing complex behavioral patterns and the importance of continuous recording devices and movement metrics to monitor cattle behavior accurately.