MASTITIS DETECTION OF ONLINE QUARTER-MILK CONDUCTIVITY FOR DAIRY COWS BY USING THE ARTIFICIAL NEURAL NETWORK

摘要

By using an online electrical conductivity (EC) measurement system for mastitis inspection of dairy cow, the variance of quarter-milk conductivity (QMC), ECR ratio among the quarter-milk conductivity and milk temperature can be measured during the milking. The somatic cell counts (SCC) of the foremilk from each quarter of dairy cows were measured at the day before and after of the field tests as the criterion to identify the healthiness of the udder. Therefore, it can be classified whether a quarter of dairy cow infected with mastitis or not according to the QMC and ECR indices which were used a back-propagation artificial neural network (ANN). All data of QMC and ECR were acquired from the field test by the online EC measurement system for mastitis would be classified three different ratios of healthy quarters to mastitis quarters (H/M ratio) in the training data sets, and be classified four different H/M quarter ratios in the testing data sets which would be executed to valid. The analysis results show ed that lower H/M quarter ratio in the training data set had a better predictive probability of the ANN for mastitis quarters, and higher H/M ratio in the training data had a better predictive probability of the ANN for healthy quarters. In addition, as the H/M quarter ratio in the testing data increased, the predictive probability of true-positive response, P(PTP), decreased significantly, while the predictive probability of true-negative response, P(PTN), increased significantly. However, the probability of total correct response, P(TCR), of the ANN to identify the mastitis quarters was 88.2%. It is feasible to identify the mastitis cows according to online QMC and ECR of dairy cows using a back-propagation ANN.