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Coffee Rust Detection Based on a Graph Similarity Approach

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Advances in Information and Communication Technologies for Adapting Agriculture to Climate Change (AACC'17 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 687))

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Abstract

Diseases affecting agricultural sectors are often closely related to weather conditions and crop management. In this regard, different researches have focused on identifying patterns that lead to the incidence of these diseases. This research was carried out in order to detect favorable conditions for rust in coffee trees (hemileia vastatrix) based on a graph representation of the Agroclimatic information of the crops. Furthermore, we adapted 4 error-correcting graph pattern matching algorithms, classified taking into account the precision and the execution time, in order to find a similarity percentage between current conditions of a coffee crop and the graph patterns that describe coffee rust infection rates.

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References

  1. Rivillas, C., Serna, C., Cristancho, M., Gaitán, A.: Roya del Cafeto en Colombia: Impacto, Manejo y Costos del Control. In: Chinchiná Bol. Téc., no. 36 (2011)

    Google Scholar 

  2. Perez, A.L.T., Vigil, A.L.M., Morales, M.E.S.: Producción de café (Coffea arabica L.): cultivo, beneficio, plagas y enfermedades. In: Temas selectos de Ingenieria de alimentos, pp. 54–74 (2011)

    Google Scholar 

  3. Waller, J.M., Bigger, M., Hillocks, R.J.: Coffee pests, diseases and their management. CABI (2007)

    Google Scholar 

  4. Muller, R.A., Berry, D., Avelino, J., Bieysse, D.: Coffee diseases. In: Coffee: Growing, Processing, Sustainable Production: A Guidebook for Growers, Processors, Traders, and Researchers, pp. 491–545 (2004)

    Google Scholar 

  5. Avelino, J., Willocquet, L., Savary, S.: Effects of crop management patterns on coffee rust epidemics. Plant. Pathol. 53(5), 541–547 (2004)

    Article  Google Scholar 

  6. Kushalappa, A.C., Eskes, A.B.: Advances in coffee rust research. Annu. Rev. Phytopathol. 27(1), 503–531 (1989)

    Article  Google Scholar 

  7. Corrales, D.C., Ledezma, A., Hoyos, J., Figueroa, A., Corrales, J.C.: A new dataset for coffee rust detection in Colombian crops based on classifiers. Sist. y Telemática 12(29), 9–23 (2014)

    Article  Google Scholar 

  8. Perez-Ariza, C.B., Nicholson, A.E., Flores, M.J.: Prediction of coffee rust disease using bayesian networks. In: Proceedings of the Sixth European Workshop on Probabilistic Graphical Models, pp. 259–266 (2012)

    Google Scholar 

  9. Lasso, E., Corrales, J.C.: Expert system for crop disease based on graph pattern matching: a proposal. Revista Ingenierías Universidad de Medellín 15(29), 81–98 (2016)

    Article  Google Scholar 

  10. Lasso, E., Ortega, S.M., Corrales, J.C.: Enriquecimiento semántico e inferencia de relaciones en una red social en línea. Ing. Univ. 17(2), 355–373 (2013)

    Google Scholar 

  11. Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. Int. J. Pattern Recognit. Artif. Intell. 18(3), 265–298 (2004)

    Article  Google Scholar 

  12. Riesen, K., Bunke, H.: Approximate graph edit distance computation by means of bipartite graph matching. Image Vis. Comput. 27(7), 950–959 (2009)

    Article  Google Scholar 

  13. Fischer, A., Suen, C.Y., Frinken, V., Riesen, K., Bunke, H.A.: Fast matching algorithm for graph-based handwriting recognition. In: International Workshop on Graph-Based Representations in Pattern Recognition, pp. 194–203 (2013)

    Google Scholar 

  14. Nikolic, M., Measuring similarity of graphs and their nodes by neighbor matching. In: ArXiv preprint arXiv:1009.5290 (2010)

  15. Abu-Aisheh, Z., Raveaux, R., Ramel, J.Y., Martineau, P.: An exact graph edit distance algorithm for solving pattern recognition problems. In: 4th International Conference on Pattern Recognition Application and Methods (2015)

    Google Scholar 

  16. Lasso, E., Thamada, T.T., Meira, C.A.A., Corrales, J.C.: Graph patterns as representation of rules extracted from decision trees for coffee rust detection. In: Garoufallou, E., Hartley, R.J., Gaitanou, P. (eds.) Metadata and Semantics Research, pp. 405–414. Springer, Heidelberg (2015)

    Google Scholar 

  17. Corrales, D., Ledezma, A., Peña, A., Hoyos, J., Figueroa, A., Corrales, J.: A new dataset for coffee rust detection in Colombian crops base on classifiers. Revista S&T 12(29), 9–23 (2014)

    Google Scholar 

  18. Holt, R.C., Winter, A., Schurr, A.: GXL: toward a standard exchange format. In: 2000 Proceedings of Seventh Working Conference on Reverse Engineering, pp. 162–171 (2000)

    Google Scholar 

  19. Lasinger, K.: Overview of existing software tools for graph matching. Technical report, Global Grid Forum Pattern Recognition and Image Processing Group, Institute of Computer Aided Automation, Vienna University of Technology (2003)

    Google Scholar 

  20. Serratosa, F.: Fast computation of bipartite graph matching. Pattern Recognit. Lett. 45, 244–250 (2014)

    Article  Google Scholar 

  21. Thamada, T.T., Neto, C.D.G., Meira, C.A.A.: Sistema de alerta da ferrugem do cafeeiro: resultado de um processo de mineração de dados. In: Embrapa Informática Agropecuária-Artigo em anais de congresso (ALICE). In: Congresso Brasileiro de Agroinformática, 9, 2013, Cuiabá. Agroinformática: inovação para a sustentabilidade do agronegócio brasileiro: anais. Cuiabá: Universidade Federal de Mato Grosso (2013)

    Google Scholar 

  22. Meira C.A.A., Rodrigues, L.H.A.: Mineração de dados no desenvolvimento de sistemas de alerta contra doençias de culturas agrícolas. In: Congresso Brasileiro de Agroinformática, vol. 5 (2005)

    Google Scholar 

  23. Neto, C.G., Rodrigues, L.H.A., Meira, C.A.A.: Modelos de predição da ferrugem do cafeeiro (Hemileia vastatrix Berkeley & Broome) por técnicas de mineração de dados. In: Coffee Science, vol. 9, no. 3, pp. 408–418 (2014)

    Google Scholar 

  24. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)

    Article  Google Scholar 

  25. Neuhaus, M., Riesen, K., Bunke, H.: Fast suboptimal algorithms for the computation of graph edit distance. In: Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), pp. 163–172 (2006)

    Google Scholar 

  26. Riesen, K., Neuhaus, M., Bunke, H.: Bipartite graph matching for computing the edit distance of graphs. In: International Workshop on Graph-Based Representations in Pattern Recognition, pp. 1–12. Springer, Heidelberg (2007)

    Google Scholar 

  27. Bellur, U., Kulkarni, R.: Improved matchmaking algorithm for semantic web services based on bipartite graph matching. In: IEEE International Conference on Web Services, ICWS 2007, pp. 86–93 (2007)

    Google Scholar 

  28. Fankhauser, S., Riesen, K., Bunke, H.: Speeding up graph edit distance computation through fast bipartite matching. In: International Workshop on Graph-Based Representations in Pattern Recognition, pp. 102–111. Springer, Heidelberg (2011)

    Google Scholar 

  29. Fan, W., Li, J., Ma, S., Tang, N., Wu, Y., Wu, Y.: Graph pattern matching: from intractable to polynomial time. VLDB Endow. 3(1–2), 264–275 (2010)

    Article  Google Scholar 

  30. Lasso, E.: Sistema experto basado en emparejamiento de patrones, Popayán, Cauca, Colombia (2016)

    Google Scholar 

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Acknowledgments

The authors are grateful to the Telematics Engineering Group (GIT) of the university of Cauca, the Colombian Administrative Department of Science, Technology and Innovation (Colciencias), AgroCloud project of The Interinstitutional Network of Climate Change and Food Security of Colombia (RICCLISA) for supporting this research and Supracafé for providing the dataset used.

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Authors and Affiliations

  1. Grupo de Ingeniería Telemática, Universidad del Cauca, Campus Tulcán, Popayán, Cauca, Colombia

    Gersain Lozada, Geraldin Valencia, Emmanuel Lasso & Juan Carlos Corrales

Authors
  1. Gersain Lozada
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  2. Geraldin Valencia
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  3. Emmanuel Lasso
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  4. Juan Carlos Corrales
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Corresponding author

Correspondence to Gersain Lozada .

Editor information

Editors and Affiliations

  1. School of Computing and Communications, Lancaster University, Lancaster, United Kingdom

    Plamen Angelov

  2. Computer Science Department, Carlos III University of Madrid, Leganés, Madrid, Spain

    Jose Antonio Iglesias

  3. Campus de Tulcán, University of Cauca, Popayán, Colombia

    Juan Carlos Corrales

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Lozada, G., Valencia, G., Lasso, E., Corrales, J.C. (2018). Coffee Rust Detection Based on a Graph Similarity Approach. In: Angelov, P., Iglesias, J., Corrales, J. (eds) Advances in Information and Communication Technologies for Adapting Agriculture to Climate Change. AACC'17 2017. Advances in Intelligent Systems and Computing, vol 687. Springer, Cham. https://doi.org/10.1007/978-3-319-70187-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-70187-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70186-8

  • Online ISBN: 978-3-319-70187-5

  • eBook Packages: EngineeringEngineering (R0)

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