Resumen
The detection of the moisture content of wheat is an important index used to measure the quality and preservation of wheat. In order to rapidly and non-destructively detect the moisture content of wheat, in this study, we designed a stripline detection device that measures 151 frequency points in the 50?200 MHz frequency range with a vector network analyzer. Random forest (RF), extreme learning machine (ELM), and BP neural network prediction models were established, using the frequency, temperature, volume density and dielectric constant as input and the water content as output. It was shown that, in the frequency range 50?200 MHz, the permittivity of wheat decreases as the frequency increases, and that this is negatively correlated. The dielectric constant of wheat increases as the moisture content, temperature, and bulk density increase, and these are positively correlated. The random forest (RF) prediction model, which uses the frequency, temperature, effective dielectric constant εeff" role="presentation">????????eeff
e
e
f
f
. and volume density as inputs and the wheat moisture content as the output, demonstrates the best performance. The determination coefficient (R2) = 0.99977, the mean absolute error (MAE) = 0.044368, the mean square error (MAE) = 0.0053011, and the root mean square error (RMSE) = 0.072809. This study provides a new device and method for the detection of the moisture content of wheat. The device is small and is not easily disturbed by the external environment. It can be measured in a variety of conditions and is important for the development of low-cost, high-precision, and portable devices for the detection of the moisture content of wheat.