Results of a multivariate approach to automated oestrus and mastitis detection

Abstract

In modern dairy farming sensors can be used to measure on-line milk yield, milk temperature, electrical conductivity of quarter milk, concentrate intake and the cow's activity. Together with information from the management information system (MIS), the sensor data can be used for the automated detection of oestrus and diseases. A model has been developed to process the measured variables in a multivariate way. This model is based on time series analysis combined with a Kalman filter. Sensor data, MIS information and reference data of two experimental farms (approx. 90 cows for two years) were available to test the model. The test results were expressed in sensitivity, the percentage of True Positive alerts, and specificity, the percentage of True Negative alerts. For oestrus, it gave in a sensitivity ranging from 94% to 83% (with the level of significance ranging from 95% to 99.9%), coupled with a specificity from 95% to 98%. For clinical mastitis a sensitivity ranging from 96% to 65% was found, for subclinical mastitis it was ranging from 100% to 57%; the coupled specificity for mastitis (clinical and subclinical) was ranging from 95.3% to 99.4%. For other diseases, a sensitivity ranging from 99.6% to 76.8% with a specificity from 86% to 97% was found. Some possibilities to improve these results are discussed.