IoT-Based Multi-Sensor Fusion Framework for Livestock Health Monitoring, Prediction, and Decision-Making Operations

Abstract

The growing challenges of livestock management increase the demand for robust and efficient livestock health monitoring systems in terms of outbreaks of diseases and productivity losses. Traditional approaches depend mostly on manual observation and standalone sensor systems, which are characterized by a high error rate, delayed anomaly detection, and poor integration of diverse data sources. This leads to inefficient interventions and increased operational costs. The proposed IoT-based multi-sensor data fusion framework provides holistic monitoring of animal health and supports proactive decision making. Advanced methods of data acquisition are combined with system methods for the detection of anomaly, predictive analytics, and decision- making in real time. Output includes multi-sensor fusion which is achieved with the help of Kalman filters for dynamic estimation of states that handle uncertainty Dempster-Shafer Theory. Thus, it yields 30% sensor noise reduction and a 25% improvement in accuracy in health metric computation. Anomaly detection uses deep autoencoder networks that can find anomalies in high-dimensional time-series data with a 96% accuracy and 4% false positives. Predictive health analytics uses LSTM networks that use an attention mechanism for disease prediction such as mastitis with 93% accuracy, allowing the detection of diseases two days before their visibility. A fuzzy-logic-based interpretation of health risk and environmental parameter, combined with real-time decision support, supports an accuracy as high as 95% for various scenarios at the same time reducing unnecessary vet visits by 20%. Seamless connectivity to cloud IoT platforms enables near-real-time visualizations and insights into actionable form via intuitive dashboard displays. It showed 40% death in livestock, 30% saving, and 20% productivity, thereby displaying its capability for replication and scaling up in vast numbers to be applied to any farming system.