{"title":"An ontology model to represent aquaponics 4.0 system’s knowledge","authors":"Rabiya Abbasi, Pablo Martinez, Rafiq Ahmad","doi":"10.1016/j.inpa.2021.12.001","DOIUrl":null,"url":null,"abstract":"<div><p>Aquaponics, one of the vertical farming methods, is a combination of aquaculture and hydroponics. To enhance the production capabilities of the aquaponics system and maximize crop yield on a commercial level, integration of Industry 4.0 technologies is needed. Industry 4.0 is a strategic initiative characterized by the fusion of emerging technologies such as big data and analytics, internet of things, robotics, cloud computing, and artificial intelligence. The realization of aquaponics 4.0, however, requires an efficient flow and integration of data due to the presence of complex biological processes. A key challenge in this essence is to deal with the semantic heterogeneity of multiple data resources. An ontology that is regarded as one of the normative tools solves the semantic interoperation problem by describing, extracting, and sharing the domains’ knowledge. In the field of agriculture, several ontologies are developed for the soil-based farming methods, but so far, no attempt has been made to represent the knowledge of the aquaponics 4.0 system in the form of an ontology model. Therefore, this study proposes a unified ontology model, AquaONT, to represent and store the essential knowledge of an aquaponics 4.0 system. This ontology provides a mechanism for sharing and reusing the aquaponics 4.0 system’s knowledge to solve the semantic interoperation problem. AquaONT is built from indoor vertical farming terminologies and is validated and implemented by considering experimental test cases related to environmental parameters, design configuration, and product quality. The proposed ontology model will help vertical farm practitioners with more transparent decision-making regarding crop production, product quality, and facility layout of the aquaponics farm. For future work, a decision support system will be developed using this ontology model and artificial intelligence techniques for autonomous data-driven decisions.</p></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"9 4","pages":"Pages 514-532"},"PeriodicalIF":7.7000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214317321000937/pdfft?md5=b12f06f413e309595f304e4b5f187655&pid=1-s2.0-S2214317321000937-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing in Agriculture","FirstCategoryId":"1091","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214317321000937","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Aquaponics, one of the vertical farming methods, is a combination of aquaculture and hydroponics. To enhance the production capabilities of the aquaponics system and maximize crop yield on a commercial level, integration of Industry 4.0 technologies is needed. Industry 4.0 is a strategic initiative characterized by the fusion of emerging technologies such as big data and analytics, internet of things, robotics, cloud computing, and artificial intelligence. The realization of aquaponics 4.0, however, requires an efficient flow and integration of data due to the presence of complex biological processes. A key challenge in this essence is to deal with the semantic heterogeneity of multiple data resources. An ontology that is regarded as one of the normative tools solves the semantic interoperation problem by describing, extracting, and sharing the domains’ knowledge. In the field of agriculture, several ontologies are developed for the soil-based farming methods, but so far, no attempt has been made to represent the knowledge of the aquaponics 4.0 system in the form of an ontology model. Therefore, this study proposes a unified ontology model, AquaONT, to represent and store the essential knowledge of an aquaponics 4.0 system. This ontology provides a mechanism for sharing and reusing the aquaponics 4.0 system’s knowledge to solve the semantic interoperation problem. AquaONT is built from indoor vertical farming terminologies and is validated and implemented by considering experimental test cases related to environmental parameters, design configuration, and product quality. The proposed ontology model will help vertical farm practitioners with more transparent decision-making regarding crop production, product quality, and facility layout of the aquaponics farm. For future work, a decision support system will be developed using this ontology model and artificial intelligence techniques for autonomous data-driven decisions.
期刊介绍:
Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining