Research On Intelligent Monitoring System Of Greenhouse Environment Based On Internet Of Things Technology

Agriculture,as the pillar industry of our country,is taken as the cornerstone of economic development.To ensure the stable and sustainable development of this basic industry,accuracy and high efficiency is of great significance.The paper is based on the agricultural greenhouse,where the null value problem,probably caused by failure of the sensor in the extreme environment,is coped with multiple interpolations.The Skewness Analysis and batch estimation are used to preprocess the sensor data of different side sites,the PCA dimension reduction is applied,and factor analysis algorithm,gray correlation analysis,multiple linear regression and other methods are used to further analyze the data.Under the current environment,the way to control the hardware,and then to control the environmental parameters in the greenhouse is figured out.The restriction of environmental factors to the growth of crops is eliminated;the goal of precision operation and the increase of yield per mu are achieved.First,the research background of agricultural internet of things technology is described,its significance and research status at home and abroad are highlighted,meanwhile,the difficulties,contents,achievements and overall structure are summarizedSecond,algorithm use is taken as the focus of the study,which mainly involves two aspects.One step is to preprocess the data collected by the sensor to ensure the accuracy of the initial data.For example,when the sensor doesn't work properly or goes wrong in the complex environment of greenhouse,multiple interpolation method can be used to complement the null value.And in order to further improve the accuracy of the data,Skewness Analysis method and batch estimation can be used to analyze the same data collected by multi-sensor,and the results are fused to obtain the maximum data accuracy to the utmost.The other step is to analyze the data collected by the sensor,which includes the strong coupling factors such as light,temperature,humidity,wind speed,soil p H,carbon dioxide concentration etc,they all act together on the crop growth factors.And PCA dimension reduction processing is used to reduce the multi-dimensional data and the key dimension data is extracted as the feature component.Factor analysis is used to analyze the multi-dimensional data so as to analyze the influence of single or double elements on the growth and results of crops,and rank the influence factors.The grey relation analysis is used to analyze the influence degree of each factor on crop growth and result,and the correlation degree of each factor is obtained according to the development trendof each factor.Multiple linear regression method is used to verify the consistency of the results in the above algorithm.Finally,based on the sensor position design and its implementation in the greenhouse,taking the data collected by the sensor as the object,this paper designs the hardware and software structure of the system centered on the way to fully automate the agricultural production.And based on the Intelligent Agricultural Planting Base of Jilin Agricultural Science and Technology Institute,the hardware environment of the greenhouse and greenhouse environment based on intelligent monitoring platform of B / S-based structure are established according to the design results.The platform mainly includes sensor data acquisition module,system analysis and processing module,hardware control module and web system module.Through the interaction of these modules,the automatic planting of crops in the greenhouse can be realized.