Digital Twinning of Hydroponic Grow Beds in Intelligent Aquaponic Systems.


Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
28 Sep 2022
Historique:
received: 10 08 2022
revised: 21 09 2022
accepted: 22 09 2022
entrez: 14 10 2022
pubmed: 15 10 2022
medline: 18 10 2022
Statut: epublish

Résumé

The use of automation, Internet-of-Things (IoT), and smart technologies is being rapidly introduced into the development of agriculture. Technologies such as sensing, remote monitoring, and predictive tools have been used with the purpose of enhancing agriculture processes, aquaponics among them, and improving the quality of the products. Digital twinning enables the testing and implementing of improvements in the physical component through the implementation of computational tools in a 'twin' virtual environment. This paper presents a framework for the development of a digital twin for an aquaponic system. This framework is validated by developing a digital twin for the grow beds of an aquaponics system for real-time monitoring parameters, namely pH, electroconductivity, water temperature, relative humidity, air temperature, and light intensity, and supports the use of artificial intelligent techniques to, for example, predict the growth rate and fresh weight of the growing crops. The digital twin presented is based on IoT technology, databases, a centralized control of the system, and a virtual interface that allows users to have feedback control of the system while visualizing the state of the aquaponic system in real time.

Identifiants

pubmed: 36236490
pii: s22197393
doi: 10.3390/s22197393
pmc: PMC9570900
pii:
doi:

Substances chimiques

Water 059QF0KO0R

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Consejo Nacional de Ciencia y Tecnología
ID : 2018-000039-01EXTF-00050
Organisme : Natural Sciences and Engineering Research Council
ID : ALLRP 545537-19
Organisme : Natural Sciences and Engineering Research Council
ID : RGPIN-2017-04516

Références

IEEE Trans Pattern Anal Mach Intell. 2004 Jul;26(7):892-9
pubmed: 18579947
PLoS One. 2014 Jul 16;9(7):e102662
pubmed: 25029125
Manuf Lett. 2020;24:
pubmed: 32832379

Auteurs

Abraham Reyes Yanes (A)

Aquaponics 4.0 Learning Factory (AllFactory), Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 2G8, Canada.

Rabiya Abbasi (R)

Aquaponics 4.0 Learning Factory (AllFactory), Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 2G8, Canada.

Pablo Martinez (P)

Department of Mechanical and Construction Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.

Rafiq Ahmad (R)

Aquaponics 4.0 Learning Factory (AllFactory), Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 2G8, Canada.

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Classifications MeSH