Noise reduction method for agricultural monitoring system signals based on Adaptive Kalman Filtering

The smart agricultural monitoring system is playing a crucial role in modern agriculture; it provides accurate and detailed information about fields and crops. However, relatively high noise outside or inside the system affects data analysis and signal transmission, reducing the system’s overall precision. Existing research that addresses noise reduction in the field of agricultural systems is limited. Therefore, a noise reduction method for agricultural monitoring system based on Adaptive Kalman Filtering is proposed. This method achieves precise noise reduction for changing parameters such as soil temperature and humidity, and it achieves moisture monitoring by real-time estimation of the process noise variance ( Q) and observation noise variance (R) of agricultural monitoring signals as well as in the method of dynamic adjustment of Kalman gain. In performance tests, compared with traditional Kalman Filtering and SMA, the RMSE of Adaptive Kalman Filtering is 0.56–1.12%; the rate of smoothness of data is 0.41%, with relatively fastest response time at about 10 seconds. According to experimental results, Adaptive Kalman Filtering has excellent smoothness of data, and quick response effect on mutation occurrence. And Adaptive Kalman Filtering can effectively adapt to the time-varying interference in agricultural environment compared with traditional Kalman Filtering, which can be flexibly applied to agricultural monitoring systems.