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

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%.