Digital Twinning of Hydroponic Grow Beds in Intelligent Aquaponic Systems

Reyes Yanes, Abraham, Abbasi, Rabiya, Martinez Rodriguez, Pablo and Ahmad, Rafiq (2022) Digital Twinning of Hydroponic Grow Beds in Intelligent Aquaponic Systems. Sensors, 22 (19). p. 7393. ISSN 1424-8220

sensors-22-07393.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (2MB) | Preview
Official URL:


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.

Item Type: Article
Additional Information: Funding information: The authors acknowledge the financial support of this work from the Council on Science and Technology (CONACYT) (File No. 2018-000039 01EXTF-00050) and the Transportes Pitic Scholarship. In addition, the authors acknowledge the financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC) (Grant File No. ALLRP 545537-19 and RGPIN- 2017-04516).
Uncontrolled Keywords: digital twin, IoT, precision farming, aquaponics farm 4.0
Subjects: D700 Agricultural Sciences
G400 Computer Science
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: John Coen
Date Deposited: 28 Sep 2022 14:29
Last Modified: 28 Sep 2022 14:30

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics