Neural network based data fusion in food transportation system

Considering latest improvements, there are different applications for data fusion techniques. In food transportation systems, measuring environmental conditions like temperature and humidity is necessary for monitoring and controlling quality of products. Application of data fusion on measured data increases reliability of food transportation system. This paper introduces application of data fusion on the measurement results from a trading food company in purpose of data approximation and classification. For this purpose, neural network is used for temperature approximation and approximated temperature is being processed for data fusion. Then according to defined fault/failure classes, the temperature records are classified. This leads to increasing reliability of food monitoring system.