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 °C), and bulk density (665.6 to 874.3 kg/m3) on the dielectric properties (the dielectric constant ε′ and the loss factor ε″) 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 (ε′, ε″, and the combination of ε′ and ε″) 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 ε′ and ε″. The PCA–SVR model with both ε′ and ε″ 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.

Keywords

• dielectric properties
• moisture content
• the straw compost
• principal component analysis (PCA)
• machine learning