On-line detection of mastitis in dairy herds using artificial neural networks

The data include milking data collected over a four month time period by robots on four farms and monthly test-day milk data collected by veterinarian for determination of the incidence of clinical mastitis (i.e. healthy cows and sick cows). A major part of work involved data pre-processing that plays an important role in this model development. Since the milking machine operated continually and it failed from time to time, an approach for normalizing the variables using running means for herd and individual cows over their own history of milkings was used. The effect of biological differences on milk yield between cows was dealt with using relative differences among quarters instead of absolute values. The final selected variables were normalized peak electrical conductivity (EcMax), normalized quarter yield fraction (QYF) and maximum relative deviation of EcMax values among four quarters for a cow (EcDV).