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

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:
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.

Where to find the original material: https://www.researchgate.net/publication/339914936_Machine_learning_based_fog_computing_assisted_data-driven_approach_for_early_lameness_detection_in_dairy_cattle; https://doi.org/10.1016/j.compag.2020.105286
Country: IE