Early Lameness detection through machine learning

Early lameness is a considerable problem in the dairy industry. It causes pain and discomfort for the cow, while lowering fertility and milk yield for the farmer. Since current solutions come with high-initial costs and complex equipment, this use case utilises leg mounted sensors - measuring step count, lying time and swaps per hour - in combination with machine learning algorithms to identify lame cattle at an early stage. These data are analysed in the cloud and anomalies are sent to farmers’ mobile device to treat affected animals immediately and avoid further effects. As opposed to a general approach, this use case customises the data models to dynamically adjust as weather and farm conditions change. By detecting early lameness before it can be visually captured, treatment costs are decreased while animal welfare is improved.

87%

detection accuracy

-15%

required treatment time

-7%

milk yield loss

Specific goals

  • Validate the solution on multiple vendor platforms and different environments;
  • Integrate lame detection as a service into IoF2020 reference architecture and incorporate data from the dairy use case Herdsman;
  • Scale the current solution to multiple sites across four countries with 1900 cattle;
  • Develop machine learning algorithms for monitoring and early detection of anomalies;
  • Combine state of the art technology with unique benefits to enhance the detection process, along with the cows’ welfare status;
  • Ensure that the data collected is accessible only by the specific end-user to whom it is related.

Expected results

  • Milk yield loss due to early lameness -7%
  • Required treatment time -15%
  • Reduced animal mortality
  • Detection accuracy 87%
  • Days before visual detection -3
  • Increased productivity
  • Improved animal welfare and reproduction efficiency
  • Reduced usage of antibiotics

Additional material

  • Use case poster

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