AdaBoost-SVR Model-based Transmitted Microwave Sensing in Wheat Moisture Prediction

The moisture content (MC) is a critical factor influencing the quality of wheat. Presently, existing MC sensing methods do not provide a swift and precise determination of the wheat’s MC. This study develops a non-contact microwave sensing approach for the accurate prediction of wheat’s MC. To be more specific, the system employs microwave signals for transmitting wheat samples, enabling the measurement and calculation of the dielectric constant ε′ and dielectric loss ε″. Additionally, it takes into account the natural packing density of the sample, thereby enhancing detection accuracy. Meanwhile, the system receives the signal attenuation difference ΔA and phase shift value Δφ corresponding to each frequency component, MC levels, and bulk density. Experimental results demonstrate that the wheat’s dielectric characteristic parameters monotonically increase with the MC level in the selected frequency range. According to the data feature, this study proposes an Adaptive Boosting for Support Vector Regression (AdaBoost-SVR) moisture prediction model to consider dielectric characteristic parameters and bulk density comprehensively. These results are also compared with Support Vector Regression (SVR) regression and the linear regression predictions using only one dielectric parameter. The comparison denotes that the best prediction performance is obtained using the three feature inputs of dielectric characteristic indicators and the bulk density (ε′ & ε″,ρ), achieving an root mean square error ( RMSE ) of 0.2201 and an coefficient of determination ( R ² ) of 0.991. This study strongly suggests that a combination of multiple indicators is inevitable when predicting MC in stockpiled wheat or future silos.