An automated on-line clinical mastitis detection system using measurement of electrical parameters and milk production efficiency

This study aims to assess a novel method for automatic on-line detection of clinical mastitis in an automatic milking system using the measurement of electrical parameters, data of milk production efficiency and neural network from the novel mastitis detection sensor. The sensors were used to measure following 9 parameters: the quarter-level milk yield (MY; kg), average electrical conductivity in milking session (AEC; mS/cm), pH of milk (pH), temperatures of milk (TP; °C), milk production efficiency (MPE; kg/h) between successive milking sessions, milking time(MT; min), Milking efficiency (ME; kg/min), Milk production time(MPT; kg/h), cow number. The 9 measurements were inputted into a neural network to calculate the mastitis detection index. The network was trained with 44 healthy and 6 clinical mastitic cows. 42 of 44 healthy and 5 of 6 mastitic cows were classified correctly after training. The trained neural network predicted 164 of 176 healthy quarters correctly in different evaluation data sets. These results were better than the results obtained with the model usually used on the farm.