Cow Mastitis Detection Based on Electrical Parameters and Neural Networks

Abstract:

The temperatures, electrical conductivities, capacitances and somatic cell counts of cow fresh milk were measured accurately, and a four-layer BP neural networks regression model was established. The temperatures, electrical conductivities and capacitances were used as the model input data, and somatic cell was counted as output data. The model results were compared with those of the model without the capacitances parameters. It showed that the detection accuracy had been significantly improved with the capacitances parameters, the correct cow mastitis detection rate for validation sample set was 100%.