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Disease diagnosis on short-cycle and perennial crops: An approach guided by ontologies

  • Katty Lagos-Ortiz
  • José Medina-Moreira
  • José Omar Salavarria-Melo
  • Mario An-drés Paredes-Valverde
  • Rafael Valencia-García
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 620)

Abstract

It is extremely important that farmers regularly monitor their crop looking for symptoms that may reveal the presence of diseases. However, sometimes farmers have no access to information that helps them to respond to questions such as: what is wrong with their crop? and what can they do to deal with the problem? This situation could cause them to lose their crops, which in turn represents economic losses. Nowadays, there are solutions focused on the automatic diagnosis of diseases, including human diseases and diseases of specific crops such as maize. However, there is a lack of solutions focused on the diagnosis of diseases of short-cycle and perennial crops. In this sense, we propose an ontology-based solution for helping farmers to diagnose disease of such kind of crops from a set of symptoms perceived by farmers. For this purpose, our solution implements a rule-based engine that can diagnose a disease from the symptoms provided. The ontology and rule-based engine were designed in conjunction with a group of experts in plant pathology. Our proposal was evaluated in conjunction with farmers from the Costa Region of Ecuador achieving encouraging results.

Keywords

ontology plant disease diagnosis crop 

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References

  1. 1. K. Leonberger, K. Jackson, R. Smith, and N. W. Gauthier, “Plant Diseases [2016],” Agric. Nat. Resour. Publ., Mar. 2016.Google Scholar
  2. 2. A. Deshpande, “A Review on Preharvesting Soybean Crop Pests and Detecting Techniques,” Int. J. Adv. Res. Comput. Sci. Manag. Stud., vol. 2, no. 2, 2014.Google Scholar
  3. 3. G. N. Agrios, Plant Pathology. Elsevier, 2012.Google Scholar
  4. 4. T. Berners-Lee, J. Hendler, O. Lassila, and others, “The semantic web,” Sci. Am., vol. 284, no. 5, pp. 28–37, 2001.Google Scholar
  5. 5. R. Studer, V. R. Benjamins, and D. Fensel, “Knowledge engineering: Principles and meth-ods,” Data Knowl. Eng., vol. 25, no. 1, pp. 161–197, Mar. 1998.Google Scholar
  6. 6. L. O. Colombo-Mendoza, R. Valencia-García, A. Rodríguez-González, G. Alor-Hernández, and J. J. Samper-Zapater, “RecomMetz: A context-aware knowledge-based mobile recommender system for movie showtimes,” Expert Syst. Appl., vol. 42, no. 3, pp. 1202–1222, Feb. 2015.Google Scholar
  7. 7. M. A. Paredes-Valverde, R. Valencia-García, M. Á. Rodríguez-García, R. Colomo-Palacios, and G. Alor-Hernández, “A semantic-based approach for querying linked data using natural language,” J. Inf. Sci., p. 0165551515616311, Nov. 2015.Google Scholar
  8. 8. M. del P. Salas-Zárate, R. Valencia-García, A. Ruiz-Martínez, and R. Colomo-Palacios, “Feature-based opinion mining in financial news: An ontology-driven approach,” J. Inf. Sci., p. 0165551516645528, May 2016.Google Scholar
  9. 9. M. Á. Rodríguez-García, R. Valencia-García, F. García-Sánchez, and J. J. Samper-Zapater, “Ontology-based annotation and retrieval of services in the cloud,” Knowl.-Based Syst., vol. 56, pp. 15–25, enero 2014.Google Scholar
  10. 10. A. Rodríguez-González, J. E. Labra-Gayo, R. Colomo-Palacios, M. A. Mayer, J. M. Gómez-Berbís, and A. García-Crespo, “SeDeLo: Using Semantics and Description Logics to Support Aided Clinical Diagnosis,” J. Med. Syst., vol. 36, no. 4, pp. 2471–2481, Aug. 2012.Google Scholar
  11. 11. F. Baader, I. Horrocks, and U. Sattler, “Description logics,” Found. Artif. Intell., vol. 3, pp. 135–179, 2008.Google Scholar
  12. 12. E. Sanchez et al., “A knowledge-based clinical decision support system for the diagnosis of Alzheimer disease,” in e-Health Networking Applications and Services (Healthcom), 2011 13th IEEE International Conference on, 2011, pp. 351–357.Google Scholar
  13. 13. C. Toro et al., “Using Set of Experience Knowledge Structure to Extend a Rule Set of Clinical Decision Support System for Alzheimer’s Disease Diagnosis,” Cybern. Syst., vol. 43, no. 2, pp. 81–95, Feb. 2012.Google Scholar
  14. 14. R.-C. Chen, Y.-H. Huang, C.-T. Bau, and S.-M. Chen, “A recommendation system based on domain ontology and SWRL for anti-diabetic drugs selection,” Expert Syst. Appl., vol. 39, no. 4, pp. 3995–4006, Mar. 2012.Google Scholar
  15. 15. I. Horrocks et al., “SWRL: A semantic web rule language combining OWL and RuleML,” W3C Memb. Submiss., vol. 21, p. 79, 2004.Google Scholar
  16. 16. P. Delir Haghighi, F. Burstein, A. Zaslavsky, and P. Arbon, “Development and evaluation of ontology for intelligent decision support in medical emergency management for mass gatherings,” Decis. Support Syst., vol. 54, no. 2, pp. 1192–1204, Jan. 2013.Google Scholar
  17. 17. L. Ma, H. Yu, G. Chen, L. Cao, and Y. Zhao, “Research on Construction and SWRL Reasoning of Ontology of Maize Diseases,” in Computer and Computing Technologies in Agriculture VI, 2012, pp. 386–393.Google Scholar
  18. 18. M. O’Connor and A. Das, “SQWRL: a query language for OWL,” in Proceedings of the 6th International Conference on OWL: Experiences and Directions-Volume 529, 2009, pp. 208–215.Google Scholar
  19. 19. E. Friedman-Hill, “Jess, the expert system shell for the java platform,” USA Distrib. Comput. Syst., 2002.Google Scholar
  20. 20. A. R. Iglesias, M. E. Aranguren, A. R. Gonzalez, and M. D. Wilkinson, “Plant Pathogen Interactions Ontology (PPIO),” Proc. IWBBIO 2013 Int. Work-Conf. Bioinforma. Biomed. Eng. 2013, pp. 695–702, 2013.Google Scholar
  21. 21. A. Halabi, “Ontology for Plant Protection,” Ontology for Plant Protection, 2015. [Online]. Available: https://sites.google.com/site/ppontology/. [Accessed: 17-Dec-2016].
  22. 22. J. K. Patil and R. Kumar, “Advances in image processing for detection of plant diseases,” J. Adv. Bioinforma. Appl. Res., vol. 2, no. 2, pp. 135–141, 2011.Google Scholar
  23. 23. T. Rattanasawad, K. R. Saikaew, M. Buranarach, and T. Supnithi, “A review and comparison of rule languages and rule-based inference engines for the Semantic Web,” in 2013 International Computer Science and Engineering Conference (ICSEC), 2013, pp. 1–6.Google Scholar
  24. 24. E. Sirin, B. Parsia, B. C. Grau, A. Kalyanpur, and Y. Katz, “Pellet: A practical owl-dl reasoner,” Web Semant. Sci. Serv. Agents World Wide Web, vol. 5, no. 2, pp. 51–53, 2007.Google Scholar
  25. 25. J. M. Gómez-Pérez and C. Ruiz, “Ontological Engineering and the Semantic Web,” in Advanced Techniques in Web Intelligence - I, J. D. Velásquez and L. C. Jain, Eds. Springer Berlin Heidelberg, 2010, pp. 191–224.Google Scholar
  26. 26. S. J. Clarke and P. Willett, “Estimating the recall performance of Web search engines,” in Aslib Proceedings, 1997, vol. 49, pp. 184–189.Google Scholar
  27. 27. Y. Yang and X. Liu, “A re-examination of text categorization methods,” in Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, 1999, pp. 42–49.Google Scholar
  28. 28. A. Rodríguez-González, J. Torres-Niño, R. Valencia-Garcia, M. A. Mayer, and G. Alor-Hernandez, “Using experts feedback in clinical case resolution and arbitration as accuracy diagnosis methodology,” Comput. Biol. Med., vol. 43, no. 8, pp. 975–986, Sep. 2013.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Katty Lagos-Ortiz
    • 1
  • José Medina-Moreira
    • 1
  • José Omar Salavarria-Melo
    • 1
  • Mario An-drés Paredes-Valverde
    • 2
  • Rafael Valencia-García
    • 2
  1. 1.Universidad Agraria del EcuadorGuayaquilEcuador
  2. 2.Facultad de InformáticaUniversidad de MurciaMurciaSpain

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