Simulation–based detection of bovine health anomalies using machine learning and synthetic IoT sensor data

Abstract

The objective of this research was to evaluate a machine learning– based methodology for the early detection of health abnormalities in cattle by analyzing physiological and feeding behavior variables obtained from IoT sensors in real–world settings. For this research, a synthetic dataset of body temperature, heart rate, and feeding activity records from forty cattle was generated, incorporating normal physiological variations and controlled abnormalities. Preprocessing included the removing records (rows) containing missing values and normalizing of variables to ensure comparability. Three machine learning models were implemented: Random Forest as a supervised classifier to differentiate between normal and abnormal physiological states, K–Means as an unsupervised algorithm to identify feeding behavior patterns, and the Isolation Forest algorithm for the individual detection of abnormalities without requiring predefined clinical thresholds. The Random Forest model demonstrated adequate performance in classifying physiological states. The K–Means algorithm allowed the cattle to be grouped into two distinct clusters based on average feeding rate, revealing patterns of low and high feeding activity. For its part, Isolation Forest identified individuals with atypical values for physiological and behavioral variables, isolating unusual profiles. It is concluded that this approach provides a methodological basis for the development of intelligent monitoring systems in precision livestock farming, with potential application in real–world scenarios through the incorporation of IoT sensor data, thus contributing to improved animal welfare and optimized bovine productivity.