Improving the efficiency of greenhouse control by using a Markov decision-making process model

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.338565

Keywords:

greenhouse microclimate, two-level optimization, stochastic Markov decision-making process, precision agriculture, control problem

Abstract

The object of this paper is the process of greenhouse control. The study solves the task of rational greenhouse control based on the Markov decision-making process taking into account two-level optimization. A random Markov decision-making process has been defined for the problem of greenhouse operation improvement.

A greenhouse control model was built, which makes it possible to determine rational microclimate parameters to grow agricultural crops. To validate the greenhouse control model, real data from an experiment on growing strawberries in a greenhouse complex were used.

Observations lasted from May 17 to June 8, 2025. Monitoring of microclimate parameters was carried out around the clock with an interval of 1 minute, which ensured high accuracy of the analysis. The experimental scenario included three irrigation circuits, a heating system, LED lighting, ventilation, and CO2 monitoring.

The proposed approach to greenhouse management based on the Markov decision-making process model demonstrates high practical value, especially in the context of growing sensitive crops such as strawberries. The simulation shows that the implementation of two-level optimization in autonomous greenhouse control systems could provide an increase in yield by 10.15%. At the same time, due to the significant volume of the greenhouse and the high thermal inertia of the structures, the actual values of the microclimate parameters deviate from the rational ones by 10–15%, as a result of which the calculated yield increase for the model built is about 7%.

Author Biographies

Andrii Biloshchytskyi, Astana IT University; Kyiv National University of Construction and Architecture

Doctor of Technical Sciences, Professor, Vice-Rector of the Science and Innovation

Department of Information Technology

Yurii Andrashko, Uzhhorod National University

PhD, Associate Professor

Department of System Analysis and Optimization Theory

Oleksandr Kuchanskyi, Astana IT University; Uzhhorod National University

Doctor of Technical Sciences, Professor

Department of Computational and Data Science

Department of Informative and Operating Systems and Technologies

Alexandr Neftissov, Astana IT University; Academy of Physical Education and Mass Sports

PhD, Associate Professor

Research and Innovation Center "Industry 4.0"

Myroslava Gladka, Taras Shevchenko National University of Kyiv; National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"

PhD, Associate Professor

Department of Information Systems and Technologies

Department of Biomedical Cybernetics

Volodymyr Vatskel, Kyiv National University of Construction and Architecture

Senior Lecturer

Department of Information Technology

Sofiia Berdei, Refugee & Immigrant Services Northwest

Senior Specialist

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Improving the efficiency of greenhouse control by using a Markov decision-making process model

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Published

2025-10-31

How to Cite

Biloshchytskyi, A., Andrashko, Y., Kuchanskyi, O., Neftissov, A., Gladka, M., Vatskel, V., & Berdei, S. (2025). Improving the efficiency of greenhouse control by using a Markov decision-making process model. Eastern-European Journal of Enterprise Technologies, 5(2 (137), 122–135. https://doi.org/10.15587/1729-4061.2025.338565

Issue

Vol. 5 No. 2 (137) (2025): Information technology. Industry control systems

Section

Industry control systems

License

Copyright (c) 2025 Andrii Biloshchytskyi, Yurii Andrashko, Oleksandr Kuchanskyi, Alexandr Neftissov, Myroslava Gladka, Volodymyr Vatskel, Sofiia Berdei

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