When using a Doppler microwave sensor to measure the flow of granular fertilizer, the vibration generated by the operation of the fertilizer applicator and various external disturbances can cause the collected signal to be distorted. This article first explores the optimal parameters for wavelet analysis and Kalman filtering algorithms. By comparing the denoising effects of the two algorithms, a denoising algorithm based on the combination of empirical mode decomposition and sample entropy combined with wavelet is proposed. Taking Stanley 15-15-15 granular fertilizer as the experimental object, the detection system such as Doppler microwave sensor is deployed on the fertilizer applicator to collect the mass flow signal of granular fertilizer for algorithm effect experimental verification.The results indicate that, compared to the original signal, the average signal-to-noise ratio of the Kalman filtering algorithm improved by 3.548 dB after optimizing the gain coefficient. After optimizing the wavelet denoising parameters, the average SNR of the wavelet analysis algorithm increased by 7.184 dB. When combining the optimized wavelet analysis with the denoising algorithm of integrated empirical mode decomposition and sample entropy, the average SNR of the denoised signal increased by 7.899 dB, while the average root mean square error decreased by 0.184, this algorithm demonstrates significant advantages in denoising the mass flow rate signals of granular fertilizers.