Soil moisture sensor for agricultural applications inspired from state of art study of surfaces scattering models & semi-empirical soil moisture models

Abstract

In this research work, models for the scattering from rough surfaces and semi-empirical soil moisture models are presented. Models using the “Bidirectional reflectance distribution function (BRDF)” are focussed in this study. Therefore the effect of the “surface roughness” on the moisture content estimation can be analyzed. “Kirchhoff Approximation Model,” “Small Perturbation Model,” “Integral Equation Model,” and “Small Slope Approximation Method” are mathematically investigated in this research work. Integral Equation Model and Small Slope Approximation Method are having the most extensive range of validity. The main disadvantage of these methods is that they follow a complex methodology and are pretty tricky to implement. Two semi-empirical soil moisture models, popularly known as the “Dubois model” and “Oh model,” are also presented and analyzed in this research work. These models assess the moisture content present in the soil depending upon several factors, i.e., incidence angle (θ), wavelength (λ), frequency (ν), scattering coefficients, etc. Scattering coefficients provide information in terms of polarization in “horizontal-horizontal (HH),” “vertical–vertical (VV),” “horizontal-vertical (HV),” or “vertical-horizontal (VH)” directions. Finally, a novel IoT-based resistive soil moisture sensor is developed and presented in this research work which provides voltage values corresponding to different moistured soil surfaces. Thus in this work, complex mathematics behind the scattering from rough surfaces is presented. Popular semi-empirical soil moisture models for moisture content estimation are presented. Finally, a prototype of the soil moisture sensor is developed to predict the moisture conditions for the different soil surfaces.