On Predicting Milk Yield and Detection of Ill Cows

The fully automated milking system VMS has different functions which complements the actual milking of cows. This master thesis presents a method to improve the calculation of milk yield in dairy cows for the VMS. This report also investigates if it is possible to improve the algorithm for finding cows with mastitis (udder inflammation). The correctness of the prediction of milk yield is important for a couple of actions in the VMS. For example, valuable time can be saved if teatcups are attached first to high yielding teats. Only cows with an attained minimum level of predicted yield should be allowed to enter the VMS and get milked. Milking has traditionally been an event to monitor the condition of the cows. Therefore methods that determine the condition are demanded for any automatic milking systems. Mastitis is a costly illness and a working test for ill cows should be implemented in the VMS in order to know which cows that are ill. The goal of this thesis work is to develop two new algorithms for the VMS. First, an improved algorithm for the prediction of secretion rate is presented. The improved algorithm uses a Kalman-filter to update the secretion-rate. The improved method has a lower total prediction in most cases. The Kalman-filter was tested and developed for five farms and was verified on one farm. Second, this report investigates if a cusum test can be used to detect ill cows. The method turns out to be slightly better than the current algorithm. A test for cows which are milked on three or two teats is evaluated. In this test the number of milkings with high conductivity and low secretion rate are weighted together. This algorithm is slightly better than the current algorithm used for detection of ill cows.