Research And Application Of Information Intelligent System Based On Agricultural Internet Of Things

China is a big agricultural country with a vast agricultural planting area.In the process of agricultural modernization and intelligent development,water resources are undoubtedly one of the important factors of crop production.Although the total amount of water resources in China is abundant,the per capita amount is relatively low.With the rapid growth of economic construction,the consumption of water resources is increasing day by day.In terms of agricultural water use,people’s own experience and unscientific water use strategies not only lead to the waste of a large amount of water resources,but also further aggravate the contradiction between agricultural land and water supply.With the development of Internet of Things technology,wireless transmission technology and artificial intelligence technology,these modern technologies also have a large number of applications and research prospects in the field of agricultural development,such as intelligent agriculture and forestry,pest and disease detection,agricultural yield prediction,etc.In addition to soil composition and the properties of crops themselves,the main factor affecting the water demand of crops is the environmental conditions for their growth.Therefore,obtaining the data of crop growth environment is the premise for achieving high-quality and high-yield crop production and reducing agricultural water resource waste.Based on the acquisition of crop growth environment data,the water demand of crops in different growth stages can be reasonably predicted and accurate irrigation can be realized through data analysis and machine learning algorithm,which is of great practical significance to the construction of modern agriculture.The purpose of this study is to build an intelligent agricultural information system based on the Internet of Things.We take greenhouse potato as an example to analyze and predict its water demand.In order to collect the environmental data of greenhouse crops in real time,a variety of sensors are used to collect the data,including soil temperature and humidity sensors,light temperature and humidity sensors,CO2 concentration sensors,etc.In terms of data transmission,this paper adopts GPRS technology and Lo Ra technology,and uses location algorithm to optimize nodes to improve the transmission efficiency and stability of data transmission process.Then,a variety of machine learning algorithms were used to predict the amount of water required by greenhouse potatoes.By comparison,the XGBoost algorithm was able to predict the amount of water needed accurately.The main work of this paper mainly includes the following aspects:(1)According to this research need,set up the intelligent agriculture information system based on Internet of things,including the selection of crop growth environment factors,the communication technology,localization algorithm optimization and data analysis method,etc.,and the three-tier architecture has carried on the detailed introduction and explanation,namely agricultural perception layer,network layer,application layer in the Internet of things.On this basis,the hardware system construction and equipment selection,including sensors,data acquisition module,microprocessor,communication module,circuit power supply,etc.Software,through the program design,the data acquisition module and the center base station program flow design.(2)Wireless location algorithm can not only provide more accurate node location information,but also ensure the stable transmission of data.Therefore,this paper analyzes the current mainstream positioning algorithms,and proposes a signal strength RSSI correction algorithm based on Lo Ra gateway.In addition,in order to better optimize the positioning algorithm,a BP neural network model based on particle swarm optimization algorithm is proposed.Finally,on the hardware and software systems,the positioning algorithm is tested.By comparing the experimental results,the ranging accuracy of the optimized algorithm is less than1 m compared with the actual measurement results,which realizes the high-precision positioning and provides a guarantee for the safe and stable transmission of data.In order to maintain the stability of data transmission and improve transmission efficiency,communication experiment and positioning experiment are designed in this paper.The experimental results show that the agricultural Io T system in this paper has stable operation,safe and reliable data transmission,high node positioning accuracy,and effectively improves the benefit of agricultural environmental data collection.(3)According to the real-time data collected by the sensors,a variety of machine learning algorithms(XGBoost,Random Forest,KNN)were used to analyze and predict greenhouse potato water demand.The results show that XGBoost algorithm has high accuracy and can better predict greenhouse potato water demand.Through this paper research and design,the Internet of things of agriculture system can better realize the greenhouse potato growth environment of information acquisition and data transmission,and through the machine learning algorithm to predict the water demand,so as to help solve the problem of low efficiency of agricultural water and waste serious,it has important practical significance for agricultural modernization and economic value.