HighlightsRadio frequency sensing technology was used to estimate clean grain mass based on grain moisture content and grain properties.Multiple variable regression analysis was used to develop grain mass estimation model.A grain mass estimation model with high R2 was developed by introducing dielectric properties and phase angle.Parameter of dielectric constant e' indicated the domination of moisture content in grain mass estimation model.Abstract. Grain mass estimation is critical in many precision agriculture applications, especially in yield monitoring during harvest procedures. A new clean grain mass estimation method using Radio Frequency (RF) sensing technology is discussed in this paper. RF sensing technology is sensitive to moisture content and grain properties. In this study, a vector network analyzer (VNA) and a pair of horn antennas were used to collect phase shift and attenuation data from 1 to 18 GHz of grain samples (soybean, canola, and corn) on a static testbed in an anechoic chamber. Using multiple variable linear regression analysis, a comprehensive clean grain mass estimation model was developed based on the dielectric properties of the grain samples derived from the S-Parameters at 13 GHz. Dielectric (e') constant/properties and phase shift were introduced into the regression models and generated a grain mass estimation result with R2 values of 0.976, 0.977, and 0.989 for soybean, canola, and corn samples, respectively. The results indicate that RF sensing technology can reveal how grain attributes interact with electromagnetic fields at a certain frequency and has the potential to provide more accurate sensing methods for estimating grain mass in multiple precision agricultural applications. Keywords: Keywords., Dielectric properties, Grain mass estimation, Microwave frequency, Phase shifts, Radio frequency sensing.