An ontology model to support the automated design of aquaponic grow beds

Abbasi, Rabiya, Martinez Rodriguez, Pablo and Ahmad, Rafiq (2021) An ontology model to support the automated design of aquaponic grow beds. Procedia CIRP, 100. pp. 55-60. ISSN 2212-8271

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Official URL: https://doi.org/10.1016/j.procir.2021.05.009

Abstract

Aquaponics is a promising sustainable farming method that combines aquaculture and hydroponics. It allows the growth of crops without soil, pesticides, or fertilizers, and with a minimum amount of water. In aquaponic systems, the design of the growing area is directly linked to the type of crop about to be planted. The type of crop directly determines, for example, the spacing between plants and between channels, which is critical to determine the footprint required and estimate the system productivity. This paper proposes a knowledge modeling approach to support the design of aquaponic systems by automatically determining the required characteristics of the aquaponic system based on crop selection. The knowledge modeling is outlined as an ontology model that formally describes the existent links between the aquaponic grow bed characteristics and its design parameters. This study gives practitioners the capacity to visualize the impact of the desired crop selection on the aquaponic system design, as well as supporting clearer decision-making regarding production facility layout and system design in aquaponic farms.

Item Type: Article
Additional Information: Funding information: The authors acknowledge the financial support of this work by the Natural Sciences and Engineering Research Council of Canada (NSERC) (Grant File No. ALLRP 545537-19 and RGPIN-2017-04516).
Uncontrolled Keywords: knowledge modeling, aquaponics, precision farming, parametric design, design automation
Subjects: H800 Chemical, Process and Energy Engineering
H900 Others in Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: Rachel Branson
Date Deposited: 09 Dec 2021 16:17
Last Modified: 09 Dec 2021 16:17
URI: http://nrl.northumbria.ac.uk/id/eprint/47949

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