When using Doppler microwave sensors 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 signals to be distorted. Based on the analysis of the denoising effects of wavelet analysis and Kalman filtering, this paper proposes a denoising algorithm based on the integration of empirical mode decomposition (EEMD) and sample entropy (SpEn) combined with wavelet analysis(EEMD-SpEn-WL). Using Stanley 15-15-15 granular fertilizer as the experimental subject, the detection system including the Doppler microwave sensor is deployed on the fertilizer applicator to collect signals related to the mass flow rate of granular fertilizer. The experimental results indicate that, compared to the original signal, the average signal-to-noise ratio (SNR) 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 (RMSE) decreased by 0.184, this algorithm demonstrates significant advantages in denoising the mass flow rate signals of granular fertilizers.