Ontology-based big data analysis for orchid smart farming

Background. Precision agriculture or smart farming is becoming more and more important in modern orchid farming in Thailand. Sensing and communication technologies have witnessed explosive growth in the recent past. These technologies are empowering information systems from many domains such as heal...

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Main Authors: Kaewboonma, Nattapong, Chansanam, Wirapong, Buranarach, Marut
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/154412
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1544122021-12-22T20:11:41Z Ontology-based big data analysis for orchid smart farming Kaewboonma, Nattapong Chansanam, Wirapong Buranarach, Marut Library and information science Background. Precision agriculture or smart farming is becoming more and more important in modern orchid farming in Thailand. Sensing and communication technologies have witnessed explosive growth in the recent past. These technologies are empowering information systems from many domains such as health care, environmental monitoring and farming, to collect and store large volume of data. Objectives. The research aims to develop an ontology for big data analysis for the smart farming in Rajamangala University of Technology Srivijaya (RUTS), Nakhon Si Thammarat campus. Methods. The ontology design and development process comprises: (1) Ontology design: the domain ontology provide vocabularies for concepts and relations within the orchid domain, and information ontology which specifies the record structure of databases; (2) Ontology development, which consists of five processes: (i) defining the scope, (ii) investigating the existing ontologies and plan to reuse, (iii) defining terms and its relations, (iv) create instances, and (v) implementation and evaluation. Results. The research outcome is the domain ontology and information ontology wherein 11 concepts of smart farming were identified and classified into classes and sub-classes. Contributions.The system is designed for assisting orchid farmers by giving recommended measures and expected results based on the knowledge extracted from best practices. Published version 2021-12-22T06:04:05Z 2021-12-22T06:04:05Z 2020 Journal Article Kaewboonma, N., Chansanam, W. & Buranarach, M. (2020). Ontology-based big data analysis for orchid smart farming. Library and Information Science Research E-Journal, 29(2), 91-98. https://dx.doi.org/10.32655/LIBRES.2019.2.2 1058-6768 https://hdl.handle.net/10356/154412 10.32655/LIBRES.2019.2.2 2 29 91 98 en Library and Information Science Research E-Journal © 2020 Nattapong Kaewboonma, Wirapong Chansanam, Marut Buranarach. All rights reserved. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Library and information science
spellingShingle Library and information science
Kaewboonma, Nattapong
Chansanam, Wirapong
Buranarach, Marut
Ontology-based big data analysis for orchid smart farming
description Background. Precision agriculture or smart farming is becoming more and more important in modern orchid farming in Thailand. Sensing and communication technologies have witnessed explosive growth in the recent past. These technologies are empowering information systems from many domains such as health care, environmental monitoring and farming, to collect and store large volume of data. Objectives. The research aims to develop an ontology for big data analysis for the smart farming in Rajamangala University of Technology Srivijaya (RUTS), Nakhon Si Thammarat campus. Methods. The ontology design and development process comprises: (1) Ontology design: the domain ontology provide vocabularies for concepts and relations within the orchid domain, and information ontology which specifies the record structure of databases; (2) Ontology development, which consists of five processes: (i) defining the scope, (ii) investigating the existing ontologies and plan to reuse, (iii) defining terms and its relations, (iv) create instances, and (v) implementation and evaluation. Results. The research outcome is the domain ontology and information ontology wherein 11 concepts of smart farming were identified and classified into classes and sub-classes. Contributions.The system is designed for assisting orchid farmers by giving recommended measures and expected results based on the knowledge extracted from best practices.
format Article
author Kaewboonma, Nattapong
Chansanam, Wirapong
Buranarach, Marut
author_facet Kaewboonma, Nattapong
Chansanam, Wirapong
Buranarach, Marut
author_sort Kaewboonma, Nattapong
title Ontology-based big data analysis for orchid smart farming
title_short Ontology-based big data analysis for orchid smart farming
title_full Ontology-based big data analysis for orchid smart farming
title_fullStr Ontology-based big data analysis for orchid smart farming
title_full_unstemmed Ontology-based big data analysis for orchid smart farming
title_sort ontology-based big data analysis for orchid smart farming
publishDate 2021
url https://hdl.handle.net/10356/154412
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