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A Semantic-Based Framework for Rice Plant Disease Management

Identification, Early Warning, and Treatment Recommendation Using Multiple Observations

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Abstract

Rice plant diseases can cause damages and yield losses. To reduce the productivity losses, farmers need to observe and decide suitable treatments for the diseases recognized from the abnormal characteristics appeared in their farms. Traditionally, farmers identify potential diseases from their experiences or by consulting other experts. However, this approach has certain disadvantages due to varying knowledge, and at times unreliable experience and perception of different farmers. Externalization of knowledge from existing reliable sources and utilization of multiple farmer’s observations can overcome such problems. Thus, this study presents the design and development of RiceMan, a semantic-based framework in agriculture for rice plant disease management using multiple observations. The framework not only manages observations within a single farm, but also integrates with neighborhood observations to cope with spreadable rice diseases. In addition, with proper design of Rice Diseases Ontology (RiceDO) and Treatment Ontology (TreatO), the framework can identify possible diseases and give early warnings to farmers for their appropriate actions. Based on realistic situations, the paper also illustrates how the proposed framework can help farmers to better: (1) identify rice diseases, (2) prepare for the early warnings, and (3) obtain recommended treatments.

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Notes

  1. The OWL file is available at https://webprotege.stanford.edu/#projects/56da2650-9804-4e71-9207-514760e92a30/edit/OWL%20Classes and https://github.com/RiceManFramework/riceman/blob/master/RiceDO.owl.

  2. The OWL file is available at https://webprotege.stanford.edu/#projects/5751206f-64d7-415d-9fc1-981f0e2be923/edit/OWL%20Classes and https://github.com/RiceManFramework/riceman/blob/master/TreatO.owl.

References

  1. Athanasiadis, I.N., Rizzoli, A.-E., Janssen, S., Andersen, E., Villa, F.: Ontology for seamless integration of agricultural data and models. In: Research conference on metadata and semantic research, pp. 282–293. Springer (2009)

  2. Consortium, P.O.T.: The plant ontology (tm) consortium and plant ontologies. Comp. Funct. Genom. 3(2), 137–142 (2002)

    Article  Google Scholar 

  3. Derwin, S., Wahyu, A., Lestari, M., Yasin, M.: Expert system in detecting coffee plant diseases. Int. J. Electr. Energy 1(3), 156–162 (2013)

    Google Scholar 

  4. Dey, A.K., Sharma, M., Meshram, M.: Image processing based leaf rot disease, detection of betel vine. (piper betlel.). Procedia Comput. Sci. 85, 748–754 (2016)

    Article  Google Scholar 

  5. Glimm, B., Horrocks, I., Motik, B., Stoilos, G., Wang, Z.: Hermit: an owl 2 reasoner. J. Autom. Reason. 53(3), 245–269 (2014)

    Article  Google Scholar 

  6. Gnanamanickam, S.S.: Biological control of rice diseases, vol. 8. Springer, New York (2009)

    Book  Google Scholar 

  7. Horridge, M., Bechhofer, S.: The owl api: a java api for owl ontologies. Semantic Web 2(1), 11–21 (2011)

    Google Scholar 

  8. I. R. R. Institute. http://www.knowledgebank.irri.org/training/fact-sheets (2018) (Online, Accessed 29 Oct 2018)

  9. Jearanaiwongkul, W., Anutariya, C., Andres, F.: An ontology-based approach to plant disease identification system. In: Proceedings of the 10th International Conference on Advances in Information Technology (IAIT2018), p. 20 (2018)

  10. Jearanaiwongkul, W., Andres, F., Anutariya, C.: A formal model for managing multiple observation data in agriculture. Int. J. Intell. Inf. Technol. (IJIIT) 15(3), 79–100 (2019)

    Article  Google Scholar 

  11. Jonquet, C.: Agroportal: an ontology repository for agronomy. In: European conference dedicated to the future use of ICT in the agri-food sector, bioresource and biomass sector, EFITA’17, demonstration session (2017)

  12. Kalita, H., Sarma, S.K., Choudhury, R.D.: Expert system for diagnosis of diseases of rice plants: prototype design and implementation. In: Automatic Control and Dynamic Optimization Techniques (ICACDOT), International Conference on IEEE, pp 723–730 (2016)

  13. Kawtrakul, A.: Ontology engineering and knowledge services for agriculture domain. J. Integr. Agric. 11(5), 741–751 (2012)

    Article  Google Scholar 

  14. Khirade, S.D., Patil, A.: Plant disease detection using image processing. In: Computing Communication Control and Automation (ICCUBEA), 2015 International Conference on IEEE, pp. 768–771 (2015)

  15. Martinelli, F., Scalenghe, R., Davino, S., Panno, S., Scuderi, G., Ruisi, P., Villa, P., Stroppiana, D., Boschetti, M., Goulart, L.R., et al.: Advanced methods of plant disease detection. A review. Agron. Sustain. Dev. 35(1), 1–25 (2015)

    Article  Google Scholar 

  16. Morco, R.C., Calanda, F.B., Bonilla, J.A., Corpuz, M.J.S., Avestro, J.E., Angeles, J.M.: e-rice: an expert system using rule-based algorithm to detect, diagnose, and prescribe control options for rice plant diseases in the philippines. In: Proceedings of the 2017 International Conference on Computer Science and Artificial Intelligence, pp 49–54 (2017)

  17. Ontology based Research Group at IITM. https://sites.google.com/site/ontoworks/ontologies (2018). (Online, Accessed 19 Nov 2018)

  18. Planteome.org. Repository for the plant disease ontology. https://github.com/Planteome/plant-disease-ontology (2016)

  19. Rice Department of Thailand. http://www.ricethailand.go.th/rkb3/Disease.htm. (Online, Accessed 5 Jan 2019)

  20. Ruangrajitpakorn, T., Kongkachandra, R., Songmuang, P., Supnithi, T.: Automatic ontology development from semi-structured data in web-portal: Towards ontology of thai rice knowledge. In: Joint International Semantic Technology Conference, pp 262–276. Springer (2018)

  21. Sankaran, S., Mishra, A., Ehsani, R., Davis, C.: A review of advanced techniques for detecting plant diseases. Comput. Electr. Agric. 72(1), 1–13 (2010)

    Article  Google Scholar 

  22. Singh, V., Misra, A.K.: Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf. Process. Agric. 4(1), 41–49 (2017)

    Google Scholar 

  23. Thind, T.S.: Diseases of field crops and their management. Daya Books, Delhi (2005)

    Google Scholar 

  24. Thunkijjanukij, A.: Ontology development for agricultural research knowledge management: a case study for Thai rice. PhD thesis, Kasetsart University (Thailand). http://eprints.rclis.org/15522/1/ak913e00.pdf (2009)

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Authors and Affiliations

  1. Asian Institute of Technology, Khlong Luang, Pathum Thani, Thailand

    Watanee Jearanaiwongkul & Chutiporn Anutariya

  2. National Institute of Informatics, Tokyo, Japan

    Frederic Andres

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  1. Watanee Jearanaiwongkul
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  2. Chutiporn Anutariya
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  3. Frederic Andres
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Correspondence to Watanee Jearanaiwongkul.

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Jearanaiwongkul, W., Anutariya, C. & Andres, F. A Semantic-Based Framework for Rice Plant Disease Management. New Gener. Comput. 37, 499–523 (2019). https://doi.org/10.1007/s00354-019-00072-0

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