近年来，随着我国的互联网和物联网技术的快速发展，农业信息化的建设进入新的阶段。农耕土壤作为农业可持续发展的重要基础要素，也一直被各国所重视。但目前在国内无论是个体农户还是一般农场，都没有与农业信息化对称的土壤重要参数跟踪分析及在此基础上有效的测土配方及分析系统的建立和实施。本课题在此背景下以西安市科技局农业科研项目为依托，设计并完成了测土配方和土壤多参数分析系统。 通过课题组实际的调研和需求分析，该系统的用户为个体农户和普通农场及相关工作人员。以基于物联网技术的土壤多参数采集及传输子系统的数据为基础，设计完成了测土配方和土壤多参数分析系统。针对普通农户完成了土壤温湿度、PH值及养分数据的存储；建立了测土配方施肥数学模型并可以快速根据农户土壤和作物情况给出综合测土配方结果；同时也可以收集存储不同地域农户的土壤和作物信息，为后期的研究提供真实的数据基础。 针对普通农场，以基于物联网技术的土壤多参数采集及传输子系统的数据为基础，设计完成了农场的用户基本信息管理、土壤的墒情管理、养分数据管理及相应的可视化和土壤测土配方；针对这些基础数据作者尝试使用机器学习的方法进行土壤肥力评价，设计了土壤肥力评价的算法模型，并在Matlab上进行仿真实验。实验表明作者使用的方法可以进行较为有效的土壤肥力评价。 本课题是以现场农户或农场的真实土壤养分数据，并结合土壤的温湿度和PH值等重要的物理化学环境数据，构建了较为合理和实时性更强的综合测土配方及土壤肥力评价软件系统。经过系统测试，各项软件功能均正常运行。

In recent years, with the rapid development of China's Internet and Internet of Things technologies, the construction of agricultural informatization has entered a new stage. As an important basic element for the sustainable development of agriculture, farming soil has always been valued by all countries. However, at present, no matter whether it is an individual farmer or a general farm in China, there is no tracing and analysis of the important parameters of soil symmetry with agricultural informatization, on this basis, there is no effective soil testing formulation and analysis system establishment and implementation. This project is based on the agricultural scientific research project of the Science and Technology Bureau of Xi’an City. It has designed and completed a soil testing formulation and a multi-parameter soil analysis system. Through the actual research and demanding analysis of the subject, the users of the system are individual farmers and ordinary farms and related staff. Based on the data of the multi-parameter acquisition and transmission subsystem of soil, facing individual farmer, a soil testing formulation and a soil multi-parameter analysis system were designed. The soil temperature, humidity, PH value and nutrient data were stored at server database; a mathematic model for soil testing and formula fertilization was established and a comprehensive soil testing formulation result could be quickly given based on the soil and crop conditions of the farmer; the farmers in different areas could also be collected and stored. The soil and crop information provide a true data base for later research. Based on the data of the multi-parameter collection and transmission subsystem of soil, the basic farms were designed to complete the user basic information management, soil moisture management, nutrient data management, and corresponding visualization and soil testing formulas. For these basic data, the author tried to use the method of machine learning to evaluate the soil fertility, designed the algorithm model of soil fertility assessment, and conducted simulation experiments on Matlab. Experiments show that the method used by the author can perform more effective evaluation of soil fertility. This research is based on actual soil nutrient data of on-site farmers or farms, combined with important physical and chemical environmental data such as soil temperature and humidity and PH value, to construct a more reasonable and real-time comprehensive soil testing formula and soil fertility evaluation software. system. After system testing, all software functions are running normally.