The current corn cleaning loss monitoring sensors are affected by various problems such as the variety of cleaning products and the complex environmental noise, and the monitoring accuracy is difficult to meet the actual needs. In order to solve this problem, a clearing loss monitoring sensor based on PVDF piezoelectric sensitive element was designed to separate the vibration, industrial noise and stray signals in the collected signal. A minimum energy criterion based on DSP electronic signal processing was proposed. The EMD denoising method used the decomposition order corresponding to the minimum energy point of the IMF component as the signal-to-noise boundary point. The amplitude discrimination circuit identified the impact signal and calculated the loss rate. In order to verify the feasibility of this method, the signal with Gaussian white noise was simulated for denoising. Compared with wavelet denoising, low-pass filtering and moving average, the Matlab simulation results showed that the EMD denoising method based on the minimum energy criterion had the smallest root mean square error (RMSE), the highest signal-to-noise ratio (SNR), and the processed signal was the closest to the original signal. Changing the signal-to-noise ratio of the original simulation signal further verified that the results obtained by this method were always optimal. In order to verify the accuracy of the method, the corn kernels and miscellaneous mixtures with loss rates of 0, 5%, 10%, 15% and 20% were used as impact samples. Compared with the experimental data obtained by three denoising methods, wavelet denoising, low-pass filtering and moving average method, the average error of the minimum energy criterion EMD denoising method was reduced by 2.12 percentage points, 4.40 percentage points and 6.52 percentage points, respectively. The research result was of great significance for improving the detection accuracy of corn cleaning loss rate, especially the research on denoising methods in the process of signal processing.