Non-contact wheat moisture detection based on mmWave radar ToF

The grain moisture content detection is crucial for ensuring food security. The existing grain moisture detection schemes based on radio frequency (RF) signals have the defects of low flexibility or dependence on a large amount of training data. Thus, this paper proposes a wheat moisture detection system based on millimeter-wave (mmWave) radar time of flight (ToF), termed mm-Smc, which is an efficient and easy-to-deploy intelligent agricultural system based on a mathematical model. The linear fitting function between the multi-antenna average relative ToF (MAAR-ToF) and the moisture content is used as a moisture detection model. Moreover, an adaptive threshold wavelet denoising method for suppressing random noise in radar-received signals is developed. The Zoom Fast Fourier Transform (ZoomFFT) is used to enhance system accuracy. We implement a prototype system using existing commercial mmWave radar and measure six different moisture wheat samples within the range of 10%-15.1%. The results show that the overall detection error of the system is less than 4%.