Applied Electrical Method for Maize Moisture Detection

ABSTRACT Moisture content of maize is a key indicator in maize production, processing and storage. In order to realize the rapid and accurate detection of maize grain moisture content, the original output signals such as input/output voltage and phase angle at 26 frequency points within the range of 10 kHz to 1.25 MHz were measured by using an oscilloscope, a signal generator and a self‐made parallel plate capacitor. The six types of electrical parameters of maize under different moisture content conditions, namely capacitance, resistance, dielectric constant, dielectric loss factor, dissipation factor and quality factor, were calculated. Feature frequencies were selected using the Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) algorithm, and modeling was performed using the Support Vector Regression (SVR) and Random Forest Regression (RF). The experimental results showed that the coefficient of determination R 2 of the constructed SPA‐SVR model reaches 0.989, and the root mean square error (RMSE) is 0.93. This method broke through the limitations of the existing detection technology that underutilizes the range of the frequency domain, and by establishing a nonlinear relationship between the electrical characteristics of the wide‐frequency domain and the moisture content, it provided an effective means of rapid and accurate detection of the maize grain moisture content.