Sensor Data Fusion Algorithm and its Application Effect Evaluation in Greenhouse Environment

In today's rapidly developing modern agriculture, agricultural intelligence has gradually become an important way to improve the yield and quality of agricultural products. To solve this problem, this article intends to adopt a multi-sensor information fusion method to achieve real-time monitoring of the indoor environment. This article uses a combination of Kalman filtering and Bayesian estimation to weight the information from multiple sensors, in order to reduce the error of a single sensor and obtain more accurate environmental parameters. Based on this, experiments are conducted in real greenhouse environments to comprehensively evaluate the application effect of the algorithm. Within a week, the temperature remained relatively stable, with an average temperature ranging from 23 °C to 25 °C. This slight change indicates that this method can ensure the continuity and stability of the data. The research results will help improve the accuracy and reliability of greenhouse environmental monitoring, and provide scientific basis for agricultural production management.