The transition to "intellectual" agriculture is the main vector of modernization of the agricultural sector of the economy. It is based on integrated automation and robotization of production, the use of automated decision-making systems. This is inevitably accompanied by a significant increase in data flow from sensors, monitoring systems, meteorological stations, drones, satellites and other external systems. Farm management has the opportunity to use various online applications for accurate recommendations and making various kinds of management decisions. In this regard, the most effective use of cloud information technologies, allowing implementing the most complex information and technical level of automation systems for management of agricultural technologies. The purpose of this work is to test the approach to creating expert management decision support systems (DSS) through the knowledge base (KB), formed in the cloud information system. For this, we consider an example of constructing a DSS for choosing the optimal date for preparing forage from perennial grasses. A complete theoretical and algorithmic database of the analytical DSS implemented in the data processing center of the cloud information system is given. On its basis, a KB is formed for a variety of different decision-making conditions. This knowledge base is transmitted to the local DSS. To make decisions about the optimal dates for the preparation of the local DSS, two variants of algorithms are used. The first option is based on management models, and the second uses the pattern recognition method. The approbation of the algorithms was carried out according to the BZ from 50 cases. According to the results of testing, the method of pattern recognition proved to be more accurate, which provides a more flexible adjustment of the situation on the local DSS to a similar situation in the KB. The considered technique can be extended to other crops.
The agricultural industry is one of the most important areas of digitalization of the economy. At the same time, the content basis of digitalization is the technology of precision farming (TZ), which implements the tasks of agrotechnology management. These tasks are divided into two main groups according to the type of executive control system. The first group includes organizational management tasks, embodied in control decision-making systems (DSS) and implemented by management at various levels. The second group includes the tasks of managing field agricultural technologies, embodied in automated control systems (ASUAT). These tasks are implemented by automated and robotic technological machines. The effectiveness of management systems depends on the degree of human participation in the management process, i.e. on the level of his intellectualization. The high level of intellectualization depends on how widely the achievements of modern management science are involved in the creation of control systems. Such achievements are most fully used in analytical systems DSS and ASUAT. However, their actual use is faced with the lack of the required qualifications of rural producers. This problem can be solved by moving to expert control systems that do not require complex multi-step calculations. At the same time, the breakthrough level of such systems can be provided by cloud-based information systems, when knowledge bases (BRs) in expert systems will be formed in information processing centers and transmitted through the public cloud to local DSS and MISS. In order to make optimal decisions on KBs in local DSS and ASUAT, pattern recognition algorithms or special decision-making models can be used, the parameters of which are estimated by the KB, considered as a training sample.
Evaluating the modern market of the Internet of things, one should consider equipment connected into a single network, solutions, applications along the entire chain of product creation, including the end user. In this paper, such a binding is based on cloud information technologies. Through these technologies, the intellectualization of agrotechnology management is implemented through the creation of expert management decision support systems (DSS). The aim of the work is to consider the methodology for constructing DSS of strategic management in precision farming systems, where this type of management has not been implemented to date. To this kind of management, the task of forming strategies for the application of mineral fertilizers and ameliorants of prolonged action for all the years of various types of crop rotations is included. To solve the problem, an algorithm for the formation of optimal strategies for the application of mineral fertilizers and ameliorants, which is implemented in an analytical automated control system of agricultural technologies, through which a knowledge base (KB) is transmitted from the cloud system to local DSS, is substantiated. To select the best option from the knowledge base, the pattern recognition method is used. The technique was tested on arbitrary initial conditions of local systems, including extreme combinations of initial conditions. Based on the analysis of the loss of optimality associated with the discrepancy of the initial conditions on the local DSS and the KB, a method for controlling the formation of the BR, aimed at reducing these losses, is substantiated.
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