Smart Resource Management and Energy-Efficient Regimes for Greenhouse Vegetable Production

Alla Dudnyk (), Natalia Pasichnyk, Inna Yakymenko, Taras Lendiel, Kamil Witaszek (), Karol Durczak and Wojciech Czekała
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Alla Dudnyk: Department of Automatics and Robotic Systems, National University of Life and Environmental Sciences of Ukraine, 03-041 Kyiv, Ukraine
Natalia Pasichnyk: Department of Automatics and Robotic Systems, National University of Life and Environmental Sciences of Ukraine, 03-041 Kyiv, Ukraine
Inna Yakymenko: Department of Automatics and Robotic Systems, National University of Life and Environmental Sciences of Ukraine, 03-041 Kyiv, Ukraine
Taras Lendiel: Department of Automatics and Robotic Systems, National University of Life and Environmental Sciences of Ukraine, 03-041 Kyiv, Ukraine
Kamil Witaszek: Department of Biosystems Engineering, Poznań University of Life Sciences, 60-633 Poznań, Poland
Karol Durczak: Department of Biosystems Engineering, Poznań University of Life Sciences, 60-633 Poznań, Poland
Wojciech Czekała: Department of Biosystems Engineering, Poznań University of Life Sciences, 60-633 Poznań, Poland

Energies, 2025, vol. 18, issue 17, 1-18

Abstract: Greenhouse vegetable production faces significant challenges due to the non-stationary and nonlinear dynamics of the cultivation environment, which demand adaptive and intelligent control strategies. This study presents an intelligent control system for greenhouse complexes based on artificial neural networks and fuzzy logic, optimized using genetic algorithms. The proposed system dynamically adjusts PI controller parameters to maintain optimal microclimatic conditions, including temperature and humidity, enhancing resource efficiency. Comparative analyses demonstrate that the genetic algorithm-based tuning outperforms traditional and fuzzy adaptation methods, achieving superior transient response with reduced overshoot and settling time. Implementation of the intelligent control system results in energy savings of 10–12% compared to conventional stabilization algorithms, while improving decision-making efficiency for electrotechnical subsystems such as heating and ventilation. These findings support the development of resource-efficient cultivation regimes that reduce energy consumption, stabilize agrotechnical parameters, and increase profitability in greenhouse vegetable production. The approach offers a scalable and adaptable solution for modern greenhouse automation under varying environmental conditions.

Keywords: greenhouse automation; adaptive control; neural networks; fuzzy logic; genetic algorithm; PI controller tuning; energy efficiency; microclimate regulation; resource-efficient production; intelligent control systems (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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