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• A UNIVERSAL MODEL OF ADAPTIVE MANAGEMENT OF CLOSED AGRICULTURAL PRODUCTION USING AI TECHNOLOGIES

  А.А. Kochkarov , А.К. Kulikov , V.М. Matsakova

  2026-04-29

  Abstract ▼

  The relevance of this research stems from the contradiction between the need to increase food production in an urbanized environment and the fragmentation of existing high-tech solutions (hydroponics, aeroponics, IoT), which are being implemented in isolation, without a unified management methodology. The lack of unified approaches to data collection and adaptive control of environmental parameters limits the scalability of vertical farms. The goal of this research is to develop and theoretically substantiate the architecture of a universal adaptive management model for closed-loop agricultural production systems, integrating various cultivation methods based on machine learning algorithms. The methodology is based on a systematic analysis of scientific publications and experimental data on the use of embedded devices and machine learning algorithms in hydroponic, aeroponic, and soil-based vertical greenhouses. Based on this data synthesis, parametric matrices were constructed to standardize technological processes. The main results include the development of a structural diagram of a universal model that enables the integration of disparate systems into a single platform with the ability to continuously monitor and perform predictive analytics. The minimum required sensor set is substantiated: pH, EC, temperature, humidity, CO₂, PAR, pressure, and nutrient solution flow. The proposed architecture enables dynamic switching between hydroponic, aeroponic, and indoor modes within a single phytotron. The conclusions and significance of this work lie in creating a foundation for designing scalable vertical farms with predictable profitability and resource efficiency indicators, while enabling continuous further training of AI algorithms for predictive microclimate management and early plant disease detection in urban environments

• APPROACH TO TRAFFIC MANAGEMENT BASED ON THE IEC 61499 STANDARD

  D. M. Elkin , V. V. Vyatkin

  2021-01-19

  Abstract ▼

  The number of vehicles on public roads is constantly increasing, and the development of road
  infrastructure is proceeding at a slow pace, and not high-quality transport management entails an
  increase in transportation costs, an increase in accidents, noise levels, and environmental pollution.
  As a consequence, there is a need to apply advanced algorithms and approaches to transport management
  in order to maximize the use of the existing road network and increase road capacity. In the
  course of recent studies, it has been revealed that adaptive approaches to traffic management are
  most effective on sections of the road network with high traffic intensity and variability. The essence
  of the approaches to adaptive management used today is that they are based on the analysis of traffic
  congestion and change the phases of traffic light operation depending on the received data in real
  time .. Adaptive traffic management shows much better results compared to tight control , significantly
  reduces transport delays, travel time and emissions of harmful substances into the atmosphere,
  therefore, modern researchers are developing new and improving existing approaches and algorithms
  for adaptive transport control. For example, traffic management approaches based on the
  concept of IoT and the use of cloud computing are actively developing. The concepts of applying the
  agent-based approach to adaptive control are also being developed. The paper proposes a method
  for managing traffic flows and automating road infrastructure using an agent-based approach. The
  proposed approach includes distributed management of various elements of the road network and
  their direct interconnection with each other. To implement this concept, the open standard of distributed
  control and automation systems IEC 61499 was used, and to test the feasibility of implementation,
  several models of traffic intersections were used, one of which was created on the basis of real
  data and SUMO - a microscopic and continuous traffic simulation package.