Multi-modal AI Framework for Livestock Health Monitoring
AI–Driven Multimodal Sensing for Early Detection of Health Disorders in Dairy Cows
This study pioneers a multimodal AI framework for real-time health monitoring in dairy cows, integrating physiological, production, and thermal imaging data. Targeting veterinarian-confirmed udder, leg, and hoof infections, the system processes data from daily milking events to generate cow-day level predictions. It demonstrates improved accuracy and AUC compared to single-modality models, enhancing early detection of health deviations and supporting proactive herd management.
This research demonstrates a significant leap in precision livestock farming, offering the potential to revolutionize how health and welfare are managed in dairy operations. By proactively identifying health issues, farms can mitigate economic losses, improve animal well-being, and optimize resource allocation.
Key Performance Metrics & Potential Gains
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Overall Accuracy
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Area Under ROC Curve (AUC)
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Sick Cows F1 Score
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Improvement over Single Modality
Deep Analysis & Enterprise Applications
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The proposed multimodal AI framework integrates data from automated milking systems, intra-ruminal bolus sensors, and thermal imaging to provide a comprehensive and contactless health monitoring solution for dairy cows. This approach targets early detection of udder, leg, and hoof infections, crucial for animal welfare and farm productivity.
91.62%
Overall Accuracy for Health Disorder Detection
Enterprise Process Flow
Data Acquisition (Milking, Bolus, Thermal)
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Preprocessing & Aggregation
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Deep Learning (Numerical & Image)
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Multimodal Fusion & Prediction
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Early Disorder Identification
| Architecture | AUC Performance | Key Features |
|---|---|---|
| Proposed Hybrid Model | 0.94 |
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| U-Net | 0.85 |
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| O-Net | 0.89 |
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| ResNet50 | 0.81 |
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Case Study: Early Detection of Mastitis
In a real-world farm deployment involving 88 lactating cows, the multimodal AI system successfully identified early signs of mastitis, often before overt clinical symptoms appeared. The integration of milk yield changes, rumination behavior, internal body temperature, and thermal patterns around the udder provided a comprehensive view, leading to a 95% F1-score for sick cow detection. This allowed for targeted interventions, reducing the severity of infection and optimizing antibiotic use.
Compared to traditional methods, the AI framework demonstrated superior sensitivity in identifying subtle physiological deviations that precede a full-blown infection, significantly impacting herd health management and productivity. The system's ability to operate contactlessly minimised animal stress and staff involvement.
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