Dielectric behavior of soil as a function of frequency, temperature, moisture content and soil texture: a deep neural networks based regression model

Abstract Dielectric behavior of soil has utmost applications in microwave remote sensing and soil treatment. In the present study, the soil's dielectric properties (Ɛ' and Ɛ") were measured using the vector network analyzer and an open-ended coaxial probe (85070E, Agilent Technologies) in the region of 0.2 to 14 GHz. The observed results showed that Ɛ' and Ɛ" strongly depend on frequency, texture, moisture content and temperature. A deep neural network (DNN) based multivariable regression model has been developed to model their behavior, using experimentally observed data to learn its parameters automatically. It shows a five-fold cross-validation root mean square errors (RMSE) of 0.0258 and 0.0336, and R2-scores of 1.0000 and 0.9998, between actual recorded and predicted values of Ɛ' and Ɛ", respectively. The results of the proposed DNN-based model have been compared with the response surface method (RSM) based model; among these, the DNN-based model shows significantly better results. Further, the DNN-based estimates of Ɛ' and Ɛ" for loam texture at a moisture content of 18% (i.e. in between observed experiments of 15% and 20%) are made and plotted with actual observed values at 15% and 20% to verify the predictive ability of the proposed DNN-based model. It shows an acceptable estimate of dielectric properties and the effectiveness of the fast and innovative DNN-based approach for predicting soil's dielectric properties depending upon multiple factors.