Microwave power adjusting during potato slice drying process using machine vision

In this study, machine vision was used for measuring area shrinkage of potato slices during thin layer drying process and then an artificial neural network (ANN) and linear models were investigated to predict moisture content (MC) of potato slices based on area shrinkage. Then an algorithm for adjusting the microwave power with respect to the predicted MC during the drying process was developed. A drying setup including imaging unit, lightning unit, infrared temperature sensor, image processing algorithm and microwave power adjusting program based on MC was developed. The experiments with two microwave power modes (variable and constant) have been done. The developed image processing had ability to separate connected potato samples and measured the shrinkage of center sample. The consequences expressed that the ANN with 1-3-1 structure had better results than linear model and could predict the MC based on shrinkage with 0.0966 RMSE and 96.87 R values on test data set. Also evaluating the developed ANN model with new experiments data set revealed that it could predicted the MC with 0.094 RMSE and 96 R values and resulted it has great accuracy and reliability. The real-time evaluating the image processing algorithm and ANN model with another new experiments indicated that the developed method has good promising ability for adjusting the microwave power and preventing the increased of microwave power density during the potato chips drying process.