KRUSHIDHAN: IoT-Enabled Cattle Health Monitoring and Disease Risk Detection Dataset

This dataset is associated with the KRUSHIDHAN project, an Internet of Things (IoT)-enabled cattle health monitoring and disease risk detection system. The dataset contains multi-parameter time-series sensor data collected using a wearable smart collar designed for continuous monitoring of cattle health.The dataset includes physiological, behavioral, and environmental parameters such as body temperature (°C), heart rate (bpm), ambient humidity (%), motion data from accelerometer and gyroscope sensors (Ax, Ay, Az, Gx, Gy, Gz), and timestamp information. Each record represents real-time sensor readings captured at regular intervals.Health condition labels are included to indicate normal and early-risk states of cattle, based on threshold-based physiological observations and expert validation. The dataset is stored in structured format using comma-separated values (CSV), making it suitable for data analysis and machine learning applications.Basic preprocessing such as noise filtering and normalization was applied during runtime to ensure data reliability. The dataset is primarily maintained in raw structured form, supporting real-time analytics and classification.This dataset can be used for research in precision livestock farming, IoT-based monitoring systems, and predictive health analytics. It enables the development and evaluation of machine learning models for early disease detection and decision-support systems in smart agriculture.