Design of Temperature and Humidity Monitoring System for Locust Greenhouse Based on Kalman Filter Fusion Algorithm

In this study, a new method based on Kalman filter fusion algorithm is proposed for the key problem of temperature and humidity monitoring in locust breeding greenhouses. The traditional method has many limitations in processing temperature and humidity data, and is susceptible to sensor failure, environmental interference and other factors, resulting in monitoring results deviating from the actual situation. Especially in the locust farming environment, locust activities may have a large impact on the monitoring system. To solve the above problems, Kalman filter fusion algorithm is used in this study. Kalman filtering is a recursive state estimation algorithm, which continuously updates the state estimates by predicting the current state and correcting the observed values, so as to realize the accurate estimation of the system state. In this study, we utilize the Kalman filter algorithm to fuse the temperature and humidity data collected by multiple sensors, and at the same time, we consider the system dynamic model to dynamically adjust the degree of data fusion, which improves the accuracy and stability of the monitoring data. Finally, this study compares the Kalman filter fusion algorithm with the simple averaging method commonly used in ordinary agricultural greenhouses, and the results show that the Kalman filter fusion algorithm can obtain more accurate and smooth data.