Concept drift for CRD prediction in broiler farms

Chronic Respiratory Disease (CRD) is a serious problem in broiler farms and food production industry. This disease cannot be observed easily during the broiler raised process. The model for predicting CRD rate is not exactly identified, because of the variation in farm environment and the development of breeding. Therefore the embedded of concept drift to the normal predictor is the possible way to build the system that can detect the change in data stream and build the suitable predictor for the current information. In this work we applied the method of concept drift to the basic Naïve Bayes classifier to predict the CRD rate by using the data collected from many contract farms during the entire raising period. The experimental result shows that, with the concept drift embedded method, the accuracy of prediction is improved by about 30%.