Automated detection of oestrus and mastitis in dairy cows

Detection models for oestrus and mastitis in dairy cows were developed, based on sensors for milk yield, milk temperature, electrical conductivity of milk, cow's activity and concentrate intake, and on combined processing of the sensor data. The detection model generated alerts for cows, that need the farmer's attention, because of a possible case of oestrus or mastitis. A first detection model for cows, milked twice a day, was based on time series models for the sensor variables, where the parameters were fitted on-line for each cow after each milking by a Kalman filter. This model was tested during two years on two experimental farms, and under field conditions on four farms during several years. A second detection model, for cows milked in an automatic milking system (AMS), was based on a generalisation of the first model. Two data sets (one small, one large) were used for testing. The results of both models for oestrus detection were good, for mastitis varying. Fuzzy logic was used for the classification of mastitis and oestrus alerts with both detection models, to reduce the number of false positive alerts. Input for the fuzzy logic model were alerts from the detection models and additional information. The number of false positive alerts decreased considerably, while keeping the number of detected cases at the same level. The models make automated detection possible in practice.