Skip to main content
ARS Home » Midwest Area » Madison, Wisconsin » U.S. Dairy Forage Research Center » Cell Wall Biology and Utilization Research » Research » Publications at this Location » Publication #269623

Title: Evaluation of a microwave resonator for predicting grain moisture independent of bulk density

item Digman, Matthew
item CONLEY, SHAWN - University Of Wisconsin
item LAUER, JOSEPH - University Of Wisconsin

Submitted to: Applied Engineering in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/7/2012
Publication Date: 7/24/2012
Citation: Digman, M.F., Conley, S.P., Lauer, J. 2012. Evaluation of a microwave resonator for predicting grain moisture independent of bulk density. Applied Engineering in Agriculture. 28(4):611-617.

Interpretive Summary: Currently, capacitive technology (an electrical method) is used to measure moisture in commodities such as corn and soybeans so that they are harvested at an appropriate time and dried down, if necessary. To gain precise moisture values, the grain sample must be presented to the sensor in a precise way (namely a constant density), usually by way of a by-pass chamber. However, more recent laboratory work has shown that microwave-based techniques have evolved to predict moisture independent of density; this method would be more practical for measuring moisture in the field during harvest as it would have no by-pass chamber and as such no moving parts. In this study, a commercially available microwave resonator was evaluated to determine its ability to predict moisture in corn and soybeans independent of density. Results showed that the technique accurately predicted moisture without influence of density. This information is useful to manufacturers of agricultural equipment who would like to implement microwave technology in their harvester designs.

Technical Abstract: This work evaluated the ability of a planar whispering mode resonator to predict moisture considering moisture and densities expected in an on-harvester application. A calibration model was developed to accurately predict moisture over the moisture, density and temperature ranges evaluated. This model, comprising of temperature and the amplitude shift at a resonance peak centered at 2.38 GHz, predicted the oven moisture reference data with an r^2 of 0.89 and a root mean squared error (RMSE) of 1.65. It was also determined that density was not statistically significant in predicting changes in amplitude shift.