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

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cris.virtual.author-orcid0000-0002-8897-6459
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cris.virtual.author-orcid0000-0003-4811-005X
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cris.virtual.author-orcid0000-0002-7750-9265
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dc.abstract.enGreenhouse 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.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Inżynierii Biosystemów
dc.contributor.authorDudnyk, Alla
dc.contributor.authorPasichnyk, Natalia
dc.contributor.authorYakymenko, Inna
dc.contributor.authorLendiel, Taras
dc.contributor.authorWitaszek, Kamil
dc.contributor.authorDurczak, Karol
dc.contributor.authorCzekała, Wojciech
dc.date.access2025-09-15
dc.date.accessioned2025-09-15T06:10:30Z
dc.date.available2025-09-15T06:10:30Z
dc.date.copyright2025-09-04
dc.date.issued2025
dc.description.abstract<jats:p>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.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if3,2
dc.description.number17
dc.description.points140
dc.description.versionfinal_published
dc.description.volume18
dc.identifier.doi10.3390/en18174690
dc.identifier.issn1996-1073
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/4763
dc.identifier.weblinkhttps://www.mdpi.com/1996-1073/18/17/4690
dc.languageen
dc.pbn.affiliationmechanical engineering
dc.relation.ispartofEnergies
dc.relation.pagesart. 4690
dc.rightsCC-BY
dc.sciencecloudsend
dc.share.typeOPEN_JOURNAL
dc.subject.engreenhouse automation
dc.subject.enadaptive control
dc.subject.enneural networks
dc.subject.enfuzzy logic
dc.subject.engenetic algorithm
dc.subject.enPI controller tuning
dc.subject.enenergy efficiency
dc.subject.enmicroclimate regulation
dc.subject.enresource-efficient production
dc.subject.enintelligent control systems
dc.titleSmart Resource Management and Energy-Efficient Regimes for Greenhouse Vegetable Production
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue17
oaire.citation.volume18