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IJMLC 2021 Vol.11(1): 92-97 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.1.1019

Ontology-Based Semantic Retrieval for Durian Pests and Diseases Control System

Porawat Visutsak

Abstract—In Southeast Asia, durian is affectionately called the king of fruit. Durian is the most popular crop planted in eastern and southern of Thailand. The total crop is around 600,000 tons per year; among this, 500,000 tons of the total production were exported worldwide. In Thailand, the knowledge of durian production is based on experience from generation to generation, especially the knowledge of durian pests and diseases control. This paper presents the ontology knowledge based for durian pests and diseases retrieval system. The major contributions of the system consist of 1) the stored knowledge of durian pests and diseases and 2) the diagnosis of durian diseases and the suggestions for the treatments. The ontology knowledge consists of 8 main classes: 1) diseases, 2) pests, 3) cultivars, 4) symptoms of bunch, 5) leaf area symptoms, 6) symptoms of the branches and trunk, 7) symptoms of fruit, and 8) symptoms of root and growth. The experimental results yielded 100% of precision, 88.33% of recall, and 93.8% of overall performance.

Index Terms—Ontology, semantic web, durian cultivars, durian pests, durian diseases, information retrieval.

Porawat Visutsak is with the Department of Computer and Information Science, Faculty of Applied Science, KMUTNB, Bangkok, Thailand (email: porawatv@kmutnb.ac.th).


Cite: Porawat Visutsak, "Ontology-Based Semantic Retrieval for Durian Pests and Diseases Control System," International Journal of Machine Learning and Computing vol. 11, no. 1, pp. 92-97, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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