The dielectric properties prediction of the vegetation depending on the moisture content using the deep neural network model

Abstract

In this paper, dielectric properties of citrus leaves are predicted with long short-term memory (LSTM) which is one of the well-known deep neural network (DNN) models and real-time measurements for any moisture content (MC) values in the range of 4.90 to 7.05 GHz at a fixed temperature of 24°C for microwave applications, as a novelty. Firstly, S-parameters of samples are measured with WR-159 waveguide and Waveguide Transmission Line Method. In addition, the MCs of samples depending on their weights are calculated. Thus, the dataset depending on various MC and frequency is obtained with the measurement results to both training and testing the DNN model. Secondly, a total of 4000 datasets are obtained, 80% of which is used for training, and 20% for testing. The proposed DNN model consists of four inputs (f, MC, S11, and S21) and two outputs (ε′ and ε″). Finally, the dielectric parameters for the desired MC and f are displayed with the graphical user interface in real-time. Success criteria for the prediction such as mean absolute error, root mean squared error, mean absolute percentage error, and R-squared are calculated. The results indicated that there is good agreement between the measured and predicted ones. R-squared are calculated as 0.962 and 0.968 for ε′ and ε″, respectively.