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

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.