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

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This framework demonstrates improved accuracy in livestock health monitoring, enabling proactive interventions.

The framework yields 30% sensor noise reduction and a 25% enhancement in health metric accuracy.
Anomaly detection employs deep autoencoder networks, achieving 96% accuracy with just 4% false positives.
Predictive health analytics utilizing LSTM networks can detect diseases up to two days before symptoms appear.
This system reduces unnecessary vet visits by 20% while improving overall livestock productivity.

Janga et al. (2025) studied this question.

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