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Ontology-Based Shrimp and Fish Diseases Diagnosis

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Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications (FDSE 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1306))

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Abstract

Shrimp and fish farming industry is one of the major productions that contributes importantly to the Vietnam economy. It provides hundreds of thousands of jobs and the hundred millions of U.S. dollars in revenues. However, this is also considered as a high-risk industry as shrimp and fish are very sensitive to the environmental changes. In addition, the development of diseases on these species is very fast. Any late treatment may cause serious problems to the species and consequently the reduction of revenues. Therefore, in this research, we propose an approach to diagnosing shrimp and fish diseases based on ontology. It helps the farmers making early predictions on shrimp and fish diseases from their morphological symptoms. The ontology is created manually based on the experts’ knowledge. The proposed system has been validated using a set of test cases. The validation result shows that the knowledge base and a set of rules have been implemented accurately.

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Notes

  1. 1.

    https://protege.stanford.edu/.

  2. 2.

    https://github.com/stardog-union/pellet.

  3. 3.

    http://owl.cs.manchester.ac.uk/tools/fact/.

  4. 4.

    http://www.hermit-reasoner.com/.

  5. 5.

    https://github.com/semsol/arc2.

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Acknowledgements

This study is funded in part by the Can Tho University Improvement Project VN14-P6, supported by a Japanese ODA loan.

Author information

Authors and Affiliations

  1. Can Tho University, Can Tho, Vietnam

    An C. Tran

  2. Kyoto Institute of Technology, Kyoto, Japan

    M. Fukuzawa

Authors
  1. An C. Tran
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  2. M. Fukuzawa
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Corresponding author

Correspondence to An C. Tran .

Editor information

Editors and Affiliations

  1. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Hosei University, Tokyo, Japan

    Makoto Takizawa

  4. Sungkyunkwan University, Suwon, Korea (Republic of)

    Tai M. Chung

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Tran, A.C., Fukuzawa, M. (2020). Ontology-Based Shrimp and Fish Diseases Diagnosis. In: Dang, T.K., Küng, J., Takizawa, M., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2020. Communications in Computer and Information Science, vol 1306. Springer, Singapore. https://doi.org/10.1007/978-981-33-4370-2_11

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  • DOI: https://doi.org/10.1007/978-981-33-4370-2_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-4369-6

  • Online ISBN: 978-981-33-4370-2

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