Smart Operation of Climatic Systems in a Greenhouse

Aurora González-Vidal, José Mendoza-Bernal, Alfonso P. Ramallo (), Miguel Ángel Zamora, Vicente Martínez and Antonio F. Skarmeta
Additional contact information
Aurora González-Vidal: Department of Information and Communication Engineering, University of Murcia, 30100 Murcia, Spain
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

Agriculture, 2022, vol. 12, issue 10, 1-18

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.

Keywords: smart agriculture; greenhouse technologies; artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/12/10/1729/pdf (application/pdf)
https://www.mdpi.com/2077-0472/12/10/1729/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:12:y:2022:i:10:p:1729-:d:947472

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().