Establishing a Prediction Model for Tea Leaf Moisture Content Using the Free-Space Method’s Measured Scattering Coefficient

Abstract

The rapid and nondestructive detection of tea leaf moisture content (MC) is of great significance to processing tea with an automatic assembly line. This study proposes an MC detection method based on microwave scattering parameters (SPs). Through the established free-space electromagnetic measurement device, 901 different frequency points are taken between 2.45 and 6 GHz using a vector network analyzer (VNA). The SPs of tea leaves with different moisture contents (5.72–55.26%) at different bulk density and different sample thicknesses were measured. The relationship between frequency, S21 amplitude and moisture content, thickness, and bulk density of tea was analyzed using correlation coefficients, significance analysis, and model construction. Back propagation (BP) neural network, decision tree (DT), and random forest (RF) MC prediction models were established with the frequency, amplitude, and phase of the SPs, thickness, and bulk density of the samples as inputs. The results showed that the RF-based model had the best performance, with determination coefficient (R2) = 0.998, mean absolute error (MAE) = 0.242, and root mean square error (RMSE) = 0.614. Compared to other nondestructive testing processes for tea, this method is simpler and more accurate. This study provides a new method for the detection of tea MC, which may have potential applications in tea processing.