The paper discusses creation of a digital twin (DT) of plant for an intelligent cyber-physical system for managing precision farming. A new approach to formalization of DT knowledge is proposed to form expert knowledge within the subject area based on the ontological specification of stages of plant growth and development and multi-agent technology for creating stage agents and coordinated dynamic recalculation of stage duration and yield forecast based on events in the environment. The paper proposes a method for calculating the forecast for duration of plant development stages and yield based on expert knowledge. A “tube” model of the range of changes in parameters of plant development for each stage has been developed. The paper also introduces a method for calculating the yield forecast, as well as the dates of beginning and end for each plant development stage within the “tube” during their normal development and in case of critical situations, for example, frost or drought. Ontology of plant development is constructed for implementation of the “tube” model of environmental parameters, which is converted into a digital form within the ontology editor, available for use by agents. The paper describes the structure and functions of a smart plant DT, built on the basis of a knowledge base and a module for multi-agent planning of plant development stages (for example, wheat), integrated with external weather forecast and fact services. A brief description of the created prototype of the intelligent plant DT system in Java is given. Using the system, agronomists can create their own knowledge bases and DTs of the cultivated plants for each field or even field section. The system will be useful in modern crop production for precision farming, not only “place-wise” but also “time-wise”, i.e. in terms of the best time for performing agrotechnical operations.

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The paper is devoted to an overview of multi-agent principles, methods, and technologies intended to adaptive real-time data clustering. The proposed methods provide new principles of self-organization of records and clusters, represented by software agents, making it possible to increase the adaptability of different clustering processes significantly. The paper also presents a comparative review of the methods and results recently developed in this area and their industrial applications. An ability of self-organization of items and clusters suggests a new perspective to form groups in a bottom-up online fashion together with continuous adaption previously obtained decisions. Multi-agent technology allows implementing this methodology in a parallel and asynchronous multi-thread manner, providing highly flexible, scalable, and reliable solutions. Industrial applications of the intended for solving too complex engineering problems are discussed together with several practical examples of data clustering in manufacturing applications, such as the pre-analysis of customer datasets in the sales process, pattern discovery, and ongoing forecasting and consolidation of orders and resources in logistics, clustering semantic networks in insurance document processing. Future research is outlined in the areas such as capturing the semantics of problem domains and guided self-organization on the virtual market.

The modern problem of real-time resource management to increase enterprise efficiency is considered.A new look at the dynamic self-organizing processes based on multi-agent technologies in building and revising schedules by events in real time is suggested. Schedule is considered as a flexible network of operations of demand and resource agents. This schedule is formed during the interactions of basic agent classes that set and break the dynamic links between each other, depending on the events and changing situation in the real world.A thermodynamic model of demand-resource network (DRN) dynamics is introduced. There is a similarity to Ilya Prigogine's non-linear thermodynamics theory which allows us to explain the phenomenon of unstable equilibrium emergence, order and chaos, catastrophes, bifurcations and other non-linear events that are significant to the self-organizing processes control in multi-agent systems (MASs).