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Smart Operation of Climatic Systems in a Greenhouse

Author

Listed:
  • Aurora González-Vidal

    (Department of Information and Communication Engineering, University of Murcia, 30100 Murcia, Spain
    Information Technologies Institute, The Centre for Research & Technology, Hellas (ITI-CERTH), 57001 Thessaloniki, Greece)

  • José Mendoza-Bernal

    (Department of Information and Communication Engineering, University of Murcia, 30100 Murcia, Spain)

  • Alfonso P. Ramallo

    (Department of Information and Communication Engineering, University of Murcia, 30100 Murcia, Spain)

  • Miguel Ángel Zamora

    (Department of Information and Communication Engineering, University of Murcia, 30100 Murcia, Spain)

  • Vicente Martínez

    (Department of Vegetal Nutrition, Centro de Edafología y Biología Aplicada del Segura del Consejo Superior de Investigaciones Científicas (CEBAS-CSIC), 30100 Murcia, Spain)

  • Antonio F. Skarmeta

    (Department of Information and Communication Engineering, University of Murcia, 30100 Murcia, Spain)

Abstract

The purpose of our work is to leverage the use of artificial intelligence for the emergence of smart greenhouses. Greenhouse agriculture is a sustainable solution for food crises and therefore data-based decision-support mechanisms are needed to optimally use them. Our study anticipates how the combination of climatic systems will affect the temperature and humidity of the greenhouse. More specifically, our methodology anticipates if a set-point will be reached in a given time by a combination of climatic systems and estimates the humidity at that time. We performed exhaustive data analytics processing that includes the interpolation of missing values and data augmentation, and tested several classification and regression algorithms. Our method can predict with a 90% accuracy if, under current conditions, a combination of climatic systems will reach a fixed temperature set-point, and it is also able to estimate the humidity with a 2.83% CVRMSE. We integrated our methodology on a three-layer holistic IoT platform that is able to collect, fuse and analyze real data in a seamless way.

Suggested Citation

  • Aurora González-Vidal & José Mendoza-Bernal & Alfonso P. Ramallo & Miguel Ángel Zamora & Vicente Martínez & Antonio F. Skarmeta, 2022. "Smart Operation of Climatic Systems in a Greenhouse," Agriculture, MDPI, vol. 12(10), pages 1-18, October.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1729-:d:947472
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    References listed on IDEAS

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    1. Chiara Bersani & Marco Fossa & Antonella Priarone & Roberto Sacile & Enrico Zero, 2021. "Model Predictive Control versus Traditional Relay Control in a High Energy Efficiency Greenhouse," Energies, MDPI, vol. 14(11), pages 1-21, June.
    2. Iddio, E. & Wang, L. & Thomas, Y. & McMorrow, G. & Denzer, A., 2020. "Energy efficient operation and modeling for greenhouses: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
    3. Showkat Ahmad Bhat & Nen-Fu Huang & Ishfaq Bashir Sofi & Muhammad Sultan, 2021. "Agriculture-Food Supply Chain Management Based on Blockchain and IoT: A Narrative on Enterprise Blockchain Interoperability," Agriculture, MDPI, vol. 12(1), pages 1-25, December.
    Full references (including those not matched with items on IDEAS)

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