Non-destructive detection of wheat moisture content with frequency modulated continuous wave system under L and S bands

•Developing wheat moisture content prediction FMCW systems using multi- and full- frequency signals in L and S bands.•Compared the performances of wheat moisture prediction models under various experimental conditions.•Single frequency data is not as good as that of multi- and full- frequency for wheat moisture content prediction. Wheat moisture content is a critical indicator for evaluating quality. The microwave free space measurement method can achieve nondestructive and efficient measurement of wheat moisture. Regarding microwave detection technology for wheat moisture content, further validation is needed for establishing a prediction model using multi-frequency and full-frequency data within a specific band. Due to the excellent penetration capability of microwaves in the L and S bands, this study explores the potential of utilizing multi-frequency and full-frequency signals in these bands to develop a prediction system for wheat water content. The paper analyzes the relationship between different microwave frequencies, temperatures, moisture contents, and bulk densities on dielectric properties. Temperature, bulk density, and dielectric properties serve as characteristic parameters for the regression model, and a moisture prediction model incorporating single frequency, multi-frequency, and full-frequency data is established. The moisture content detection model integrates three regression methods: Partial Least Squares (PLS), Support Vector Regression (SVR), and Extreme Learning Machine (ELM). Results show that among the nine different prediction models, the SVR model under full-frequency conditions performs the best. The correlation coefficient, root mean square error, and residual prediction bias for moisture prediction on the validation set are 0.9838, 0.3511%, and 6.3245, respectively. To enable online detection of wheat moisture content, a low-cost frequency modulated continuous wave (FMCW) detection system was designed based on the optimal prediction model. Experiments have confirmed that within the moisture content range of 11.35% to 17.79%, the average determination coefficient between the moisture content obtained through drying methods and the measurement results from the FMCW system can reach 0.9493. These endeavors have the potential to provide reliable and cost-effective solutions for precision agriculture applications.