# Prediction of the Moisture Content in Corn Straw Compost Based on Their Dielectric Properties
###### Abstract  
This study proposes a novel method for the rapid detection of compost moisture content. The effects of the test frequency (1 to 100 kHz), compost moisture content (5% to 35%), temperature (25 to 65 \({}^{\circ}\)C), and bulk density (665.6 to 874.3 kg/m\({}^{3}\)) on the dielectric properties (the dielectric constant \(\ell^{\prime}\) and the loss factor \(\varepsilon^{\prime\prime}\)) in the compost consisting of fresh sheep and manure corn were investigated. The mechanism for the change in dielectric properties was analyzed. The feature variables of dielectric parameters (\(\ell^{\prime}\), \(\varepsilon^{\prime\prime}\), and the combination of \(\ell^{\prime}\) and \(\ell^{\prime\prime}\)) were selected using principal component analysis (PCA), and the selected characteristic variables and the full-frequency variables were used to perform support vector machine regression (SVR) modeling. The results revealed that the increase in both temperature and bulk density in the frequency band from 1 to 100 kHz increased \(\ell^{\prime}\) and \(\varepsilon^{\prime\prime}\). The PCA-SVR model with both \(\ell^{\prime}\) and \(\varepsilon^{\prime\prime}\) combined variables achieved the best results, with a prediction set coefficient of determination of 0.9877 and a root mean square error of 0.0026. In conclusion, the method of predicting the moisture content based on the dielectric properties of compost is feasible. dielectric properties; moisture content; the straw compost; principal component analysis (PCA); machine learning +
Footnote †: journal:  
The dielectric technique establishes an intrinsic link between the electromagnetic and physicochemical properties of the object to be measured by directly measuring the information inside the sample. This technique is more reliable than the spectroscopic technique [11; 12]. Dielectric properties are the response characteristic of a substance under the action of an electric field and are typically expressed by the complex permittivity \(\varepsilon^{\prime}\), which describes the interaction of the sample with the applied electric field [13]. Here, \(\varepsilon^{\prime}\) is the dielectric constant and \(\varepsilon^{\prime\prime}\) is the loss factor. As presented in Equation (1), the dielectric constant (\(\varepsilon^{\prime}\)) and the loss factor (\(\varepsilon^{\prime\prime}\)) correspond to the real and imaginary parts of complex permittivity (\(\varepsilon^{\prime}\)), respectively. \[\varepsilon*=\varepsilon^{\prime}-j\varepsilon^{\prime\prime} \tag{1}\]  
Since the first publication of data on grain dielectric parameters in 1953 [14], numerous studies have been conducted on the dielectric properties of agricultural products. Scholars have applied this technology for measuring the moisture content in many fields, such as building materials and pelleted biomass, and verified its effectiveness. [PERSON] et al. [15] fused the information of \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) and used a nonlinear modeling method combining the continuous projection algorithm (SPA) and SVR to demonstrate the feasibility of nondestructive testing of the corn leaf moisture content based on dielectric properties. [PERSON] et al. [16] developed a ternary mathematical model describing the straw capacitance with the moisture content, temperature, and bulk density and verified the reliability of the technique. [PERSON] et al. [17] used a modified ISO (MISO) model combined with the dielectric constant of the asphalt mixture to estimate its moisture content. The results revealed that the MISO model predicted the moisture content with a high R2 value of more than 0.74. [PERSON] et al. [18] investigated the dielectric properties of peanut shells and pine wood chip pellets at three flow rates. They created a mathematical model to forecast the moisture content of peanut shells and pine wood chip particles under dynamic conditions with a standard error of calibration between 0.48% and 0.56%.