An ontology model to represent aquaponics 4.0 system’s knowledge

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY Information Processing in Agriculture Pub Date : 2022-12-01 DOI:10.1016/j.inpa.2021.12.001
Rabiya Abbasi, Pablo Martinez, Rafiq Ahmad
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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.

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用本体模型表示鱼菜共生4.0系统的知识
水培法是水产养殖和水培法的结合,是一种垂直养殖方法。为了提高鱼菜共生系统的生产能力,并在商业层面上实现作物产量最大化,需要整合工业4.0技术。工业4.0是一项战略举措,其特点是融合了大数据和分析、物联网、机器人、云计算和人工智能等新兴技术。然而,由于存在复杂的生物过程,实现鱼菜共生4.0需要有效的数据流和集成。这个本质上的一个关键挑战是处理多个数据资源的语义异构性。本体是通过描述、提取和共享领域知识来解决语义互操作问题的规范工具之一。在农业领域,针对基于土壤的耕作方法开发了几个本体,但到目前为止,还没有尝试以本体模型的形式来表示鱼菜共生4.0系统的知识。因此,本研究提出了一个统一的本体模型AquaONT来表示和存储鱼菜共生4.0系统的基本知识。该本体为鱼菜共生4.0系统知识的共享和重用提供了一种机制,以解决语义互操作问题。AquaONT是根据室内垂直农业术语构建的,并通过考虑与环境参数、设计配置和产品质量相关的实验测试案例进行验证和实施。提出的本体模型将有助于垂直农场从业者对水产养殖场的作物生产、产品质量和设施布局进行更透明的决策。在未来的工作中,将使用该本体模型和人工智能技术开发一个决策支持系统,用于自主数据驱动决策。
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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
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
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