Design And Implementation Of Software Platform For Intelligence Agriculture Based On Java EE

Our country is agricultural country since ancient times.Agriculture,as a knowledgeintensive industry,has gradually developed from a single industry to one that involves a integration of various kinds of industry.All kinds of information and knowledge of agriculture and science have played a vital role in agricultural production.With the progress and further development of science and technology,the traditional industrial agricultural production model has been unable to meet the needs of current social life,while "Internet +",combining the traditional industry and the Internet to realize the informatization and intelligence of the industry,provides a new way.Typical Internet technologies include the Internet of things(Io T),cloud computing,big data and artificial intelligence(AI).By combining science and technology including the focus of this article-"intelligent agriculture",the traditional industry's problems due to reasons such as industry scale and inefficiency have gradually been resolved.This paper designs and implements a set of intelligent agricultural software platform based on Java EE and MVC design pattern.After analyzing the system requirements of the software platform,the whole system is designed and its functional structure and overall architecture are obtained.The software platform is divided into three software subsystems: enterprise information subsystem,agricultural product traceability subsystem and Internet of things monitoring subsystem.Later,the design of the software platform includes obtaining the data tables according to the requirements,with carrying out conceptual design,logical design and physical design of the database based on the standard process.The business application layer in the system is designed,including the design of persistence class and model layer.Last the interaction process and system configuration of the back end are explained.In the process of realizing the software platform,multiple subsystems are implemented respectively.Finally,the intelligent decision-making algorithm in the system is designed and implemented with BP neural network algorithm.After testing the system,the conclusion can be listed as follows: three subsystems' functions are all ready and good to cover the system requirements.The predicted results of the decision-making algorithm is given and the actual results relative errors are less than 10%,which has reached the expected goal.