Design Of Crop Growth Environment Monitoring System And Research On Data Fusion Algorithm

The Internet of Things technology has played an important role in China’s transition from traditional agriculture to modern agriculture.The traditional agricultural management method requires manually collecting various environmental parameters of crops,and then grasping their growth situation based on experience and feeling.This extensive manual collection lacks accuracy and real-time in the acquisition and processing of information on the one hand;Agricultural production management is inefficient and labor costs are expensive.Therefore,this paper designs and implements a set of crop growth environment monitoring system based on the Internet of Things,which can real-time and accurately monitor the growth environment parameters of crops such as temperature and humidity,light intensity,soil p H,soil conductivity and static images,which is convenient for user management and make corresponding decisions.First of all,the system needs analysis and feasibility study,on this basis,the overall design plan is given.The hardware includes wireless communication module,regional gateway module and sensor module.The hardware design is carried out separately,and the overall architecture of the hardware system is built.In terms of software,a wireless sensor network based on ZigBee is constructed for data communication and transmission between sensor nodes;various information display interfaces are designed with the help of the Qt grap Hics framework,and the sensor measurement data is visually displayed on the touch screen equipped with the regional gateway At the same time,the data is stored in the local area gateway and uploaded to the remote server by means of the SQLite database.Aiming at the problem that the measurement data of the sensor is easily interfered by the environment and other factors,resulting in inaccurate measurement results,this paper proposes a multi-sensor data fusion algorithm based on trust and improved genetics: first,the original measurement data is pre-expanded Processing,eliminating abnormal data and noisy data;then datalevel fusion of smooth data,the establishment of a fusion model based on exponential trust,and the use of improved genetic algorithm to optimize the fusion estimate,further improve the fusion accuracy.The simulation results show that the algorithm proposed in this paper can significantly improve the measurement accuracy of the system compared with common fusion algorithms such as arithmetic averaging and adaptive weighting.Finally,the system is functionally tested,and the actual operating results show that the sensor’s measurement data can be correctly displayed on the touch screen and the system is running stably.