399 Research paper – Taneja – 2020-Machine learning based fog computing assisted data driven approach for early lameness detection in dairy cattle

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399 Research paper – Taneja – 2020-Machine learning based fog computing assisted data driven approach for early lameness detection in dairy cattle

In Significant Impact Groups:
Precision Livestock Farming & Early detection \ Sensor technology
Species targeted: Dairy;
Age: Not stated;
Summary:
Where to find the original material:
Country: IE
Timely lameness detection is one of the major and costliest health problems in dairy cattle. This poses a concern with increasing herd sizes, as prolonged or undetected lameness severely compromises cows’ health and welfare, and ultimately affects the milk productivity of the farm. To tackle this, an end-to-end IoT application that leverages advanced machine learning and data analytics techniques to monitor the cattle in real-time and identify lame cattle at an early stage has been developed. The proposed approach has been validated on a real world smart dairy farm setup consisting of a dairy herd of 150 cows in Waterford, Ireland. The detected lameness anomalies are further sent to farmer’s mobile device. The results indicate that lameness can be detected 3 days before it can be visually captured by the farmer with an overall accuracy of 87%. This means that the animal can either be isolated or treated immediately.