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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
1. K. Leonberger, K. Jackson, R. Smith, and N. W. Gauthier, “Plant Diseases [2016],” Agric. Nat. Resour. Publ., Mar. 2016.
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.
3. G. N. Agrios, Plant Pathology. Elsevier, 2012.
4. T. Berners-Lee, J. Hendler, O. Lassila, and others, “The semantic web,” Sci. Am., vol. 284, no. 5, pp. 28–37, 2001.
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.
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.
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.
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.
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.
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.
11. F. Baader, I. Horrocks, and U. Sattler, “Description logics,” Found. Artif. Intell., vol. 3, pp. 135–179, 2008.
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.
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.
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.
15. I. Horrocks et al., “SWRL: A semantic web rule language combining OWL and RuleML,” W3C Memb. Submiss., vol. 21, p. 79, 2004.
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.
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.
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.
19. E. Friedman-Hill, “Jess, the expert system shell for the java platform,” USA Distrib. Comput. Syst., 2002.
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.
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. 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.
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.
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.
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.
26. S. J. Clarke and P. Willett, “Estimating the recall performance of Web search engines,” in Aslib Proceedings, 1997, vol. 49, pp. 184–189.
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.
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Lagos-Ortiz, K., Medina-Moreira, J., Salavarria-Melo, J.O., Paredes-Valverde, M.Ad., Valencia-García, R. (2018). Disease diagnosis on short-cycle and perennial crops: An approach guided by ontologies. In: Omatu, S., Rodríguez, S., Villarrubia, G., Faria, P., Sitek, P., Prieto, J. (eds) Distributed Computing and Artificial Intelligence, 14th International Conference. DCAI 2017. Advances in Intelligent Systems and Computing, vol 620. Springer, Cham. https://doi.org/10.1007/978-3-319-62410-5_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-62410-5_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-62409-9
Online ISBN: 978-3-319-62410-5
eBook Packages: EngineeringEngineering (R0)