Intelligent support system for agro-technological decisions for sowing fields

O. Fedusenko, Natalia Shkurpela, Iryna Domanetska, Anatoliy Fedusenko
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Abstract

The are crop planning problems exist in a modern agriculture of Ukraine. With the help of the intelligent support system for agro-technological decisions proposed by the authors, it is possible to simplify the planning process by using the concept of precision farming. Modern fields monitoring methods were analyzed and methods that will be used in the intelligent system are identified. The k-means method is one of them and will be applied to field clustering. The authors analyzed modern research and publications related to the concept of precision farming and the problem of implementing modern innovative information systems in agriculture of Ukraine. The decomposition of the intelligent system was carried out. Six main subsystems were identified, functional requirements were developed for each of them. Modern methods of fields monitoring are analyzed and methods that will be used in the intelligent system are identified, one of which is the k-means method, which will be applied to field clustering. Based on the already developed requirements, the authors have developed the general architecture of the system. The notation TOGAF was applied for the graphical display of the architecture. Based on the proposed architecture, intelligent system software was created. As a result of testing the soft-ware of the intelligent system, it is possible to draw a conclusion about its efficiency and readiness for implementation. The designed and developed system allows to carry out intellectual analysis of historical data of crops, to display results in the form of tables and graphs, to carry out planning of crops, agrotechnological operations and fertilizer application. The introduction of this system will improve the quality of management decisions and productivity of agricultural activities.
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播种领域农业技术决策智能支持系统
乌克兰现代农业存在的主要问题是作物规划问题。在作者提出的农业技术决策智能支持系统的帮助下,可以通过使用精准农业的概念来简化规划过程。分析了现代现场监测方法,确定了智能系统将采用的方法。k-means方法就是其中的一种,将被应用于场聚类。作者分析了与精确农业概念有关的现代研究和出版物以及在乌克兰农业中实施现代创新信息系统的问题。对智能系统进行了分解。确定了六个主要子系统,并为每个子系统开发了功能需求。分析了现场监测的现代方法,并确定了智能系统中将使用的方法,其中之一是k-means方法,该方法将应用于现场聚类。根据已经开发的需求,作者开发了系统的总体架构。TOGAF符号用于体系结构的图形显示。基于所提出的体系结构,构建了智能系统软件。通过对智能系统软件的测试,可以得出一个关于其效率和实施准备的结论。设计开发的系统可以对作物历史数据进行智能分析,以表格和图形的形式显示结果,进行作物规划,农业技术操作和肥料施用。引进这一系统将提高管理决策的质量和农业活动的生产力。
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