An Empirical Multi-classifier for Coffee Rust Detection in Colombian Crops

  • David Camilo CorralesEmail author
  • Apolinar Figueroa
  • Agapito Ledezma
  • Juan Carlos Corrales
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9155)


Rust is a disease that leads to considerable losses in the worldwide coffee industry. In Colombia, the disease was first reported in 1983 in the department of Caldas. Since then, it spread rapidly through all other coffee departments in the country. Recent research efforts focus on detection of disease incidence using computer science techniques such as supervised learning algorithms. However, a number of different authors demonstrate that results are not sufficiently accurate using a single classifier. Authors in the computer field propose alternatives for this problem, making use of techniques that combine classifier results. Nevertheless, the traditional approaches have a limited performance due to dataset absence. Therefore we proposed an empirical multi-classifier for coffee rust detection in Colombian crops.


Coffee rust Classifier Multi-classifier Dataset 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cristancho, M., Rozo, Y., Escobar, C., Rivillas, C., Gaitán, Á.: Razas de roya (2012)Google Scholar
  2. 2.
    Rivillas-Osorio, C., Serna-Giraldo, C., Cristancho-Ardila, M., Gaitán-Bustamante, A.: La roya del cafeto en Colombia, impacto, manejo y costos de control. Cenicafé (2011)Google Scholar
  3. 3.
    Luaces, O., Rodrigues, L.H.A., Alves Meira, C.A., Quevedo, J.R., Bahamonde, A.: Viability of an alarm predictor for coffee rust disease using interval regression. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds.) IEA/AIE 2010, Part II. LNCS, vol. 6097, pp. 337–346. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Luaces, O., Rodrigues, L.H.A., Alves Meira, C.A., Bahamonde, A.: Using nondeterministic learners to alert on coffee rust disease. Expert Systems with Applications 38, 14276–14283 (2011)Google Scholar
  5. 5.
    Cintra, M.E., Meira, C.A.A., Monard, M.C., Camargo, H.A., Rodrigues, L.H.A.: The use of fuzzy decision trees for coffee rust warning in Brazilian crops. In: 2011 11th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 1347–1352 (2011)Google Scholar
  6. 6.
    Pérez-Ariza, C.B., Nicholson, A.E., Flores, M.J.: Prediction of Coffee Rust Disease Using Bayesian Networks. In: Andrés Cano, MG.-O., Nielsen, T.D. (eds.) The Sixth European Workshop on Probabilistic Graphical Models. DECSAI, University of Granada, Granada (Spain) (2012)Google Scholar
  7. 7.
    Meira, C.A.A., Rodrigues, L.H.A., Moraes, S.: Modelos de alerta para o controle da ferrugem-do-cafeeiro em lavouras com alta carga pendente. Pesquisa Agropecuária Brasileira 44, 233–242 (2009)CrossRefGoogle Scholar
  8. 8.
    Meira, C., Rodrigues, L., Moraes, S.: Análise da epidemia da ferrugem do cafeeiro com árvore de decisão. Tropical Plant Pathology 33, 114–124 (2008)CrossRefGoogle Scholar
  9. 9.
    Li, L., Zou, B., Hu, Q., Wu, X., Yu, D.: Dynamic classifier ensemble using classification confidence. Neurocomputing 99, 581–591 (2013)CrossRefGoogle Scholar
  10. 10.
    Ranawana, R., Palade, V.: Multi-Classifier Systems: Review and a roadmap for developers. Int. J. Hybrid Intell. Syst. 3, 35–61 (2006)zbMATHGoogle Scholar
  11. 11.
    Ghosh, J.: Multiclassifier systems: back to the future. In: Roli, F., Kittler, J. (eds.) MCS 2002. LNCS, vol. 2364, pp. 1–15. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Corrales, D.C., Ledezma, A., Peña, A., Hoyos, J., Figueroa, A., Corrales, J.C.: A new dataset for coffee rust detection in Colombian crops base on classifiers. Sistemas y Telemática 12, 9–22 (2014)Google Scholar
  13. 13.
    Jain, R., Minz, S.: Ramasubramanian: Machine Learning for Forewarning Crop Diseases. Journal of the Indian Society of Agricultural Statistics 63, 97–107 (2009)MathSciNetGoogle Scholar
  14. 14.
    Korada, N.K., Kumar, N.S.P., Deekshitulu, Y.: Implementation of Naive Bayesian Classifier and Ada-Boost Algorithm Using Maize Expert System. International Journal of Information Sciences and Techniques (IJIST) 2 (2012)Google Scholar
  15. 15.
    Mitchell, T.: Machine learning. McGraw-Hill (1997)Google Scholar
  16. 16.
    Poh, H.L.: A neural network approach for marketing strategies research and decision support, vol. Ph.D. Thesis. Stanford University (1991)Google Scholar
  17. 17.
    Haykin, S.S.: Neural networks: a comprehensive foundation. Prentice Hall (2003)Google Scholar
  18. 18.
    Suhasini, A., Palanivel, S., Ramalingam, V.: Multimodel decision support system for psychiatry problem. Expert Systems with Applications 38, 4990–4997 (2011)CrossRefGoogle Scholar
  19. 19.
    Bonakdar, L., Etemad-Shahidi, A.: Predicting wave run-up on rubble-mound structures using M5 model tree. Ocean Engineering 38, 111–118 (2011)CrossRefGoogle Scholar
  20. 20.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (2000)zbMATHCrossRefGoogle Scholar
  21. 21.
    Vapnik, V.N.: An overview of statistical learning theory. IEEE Transactions on Neural Networks 10, 988–999 (1999)CrossRefGoogle Scholar
  22. 22.
    Balasundaram, S., Gupta, D.: Training Lagrangian twin support vector regression via unconstrained convex minimization. Knowledge-Based Systems 59, 85–96 (2014)CrossRefGoogle Scholar
  23. 23.
    Skurichina, M., Duin, R.P.: Bagging, boosting and the random subspace method for linear classifiers. Pattern Analysis & Applications 5, 121–135 (2002)zbMATHMathSciNetCrossRefGoogle Scholar
  24. 24.
    Breiman, L.: Bagging Predictors. Machine Learning 24, 123–140 (1996)zbMATHMathSciNetGoogle Scholar
  25. 25.
    Marqués, A.I., García, V., Sánchez, J.S.: Two-level classifier ensembles for credit risk assessment. Expert Systems with Applications 39, 10916–10922 (2012)CrossRefGoogle Scholar
  26. 26.
    Tin Kam, H.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 832–844 (1998)CrossRefGoogle Scholar
  27. 27.
    Rodriguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation Forest: A New Classifier Ensemble Method. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 1619–1630 (2006)CrossRefGoogle Scholar
  28. 28.
    Menahem, E., Rokach, L., Elovici, Y.: Troika – An improved stacking schema for classification tasks. Information Sciences 179, 4097–4122 (2009)CrossRefGoogle Scholar
  29. 29.
    Gama, J., Brazdil, P.: Cascade Generalization. Machine Learning 41, 315–343 (2000)zbMATHCrossRefGoogle Scholar
  30. 30.
    McAlister, D.: The Law of the Geometric Mean. Proceedings of the Royal Society of London 29, 367–376 (1879)CrossRefGoogle Scholar
  31. 31.
    Grubbs, F.: Procedures for Detecting Outlying Observations in Samples. Technometrics 11, 1–21 (1969)CrossRefGoogle Scholar
  32. 32.
    Mucherino, A., Papajorgji, P., Pardalos, P.: Clustering by k-means. In: Du, D.-Z. (ed.) Data Mining in Agriculture, vol. 30, pp. 47–56. Springer, New York (2009)CrossRefGoogle Scholar
  33. 33.
    Araujo, B.S.: Aprendizaje automático: conceptos básicos y avanzados: aspectos prácticos utilizando el software Weka. Pearson Prentice Hall, España (2006)Google Scholar
  34. 34.
    Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc (1993)Google Scholar
  35. 35.
    Kotsiantis, S.B.: Supervised machine learning: a review of classification techniques. In: Proceedings of the 2007 Conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies, pp. 3–24. IOS Press (2007)Google Scholar
  36. 36.
    Bhavsar, H., Ganatra, A.: A Comparative Study of Training Algorithms for Supervised Machine Learning. International Journal of Soft Computing and Engineering (IJSCE) 2, 74–81 (2012)Google Scholar
  37. 37.
    Corrales, D.C., Figueroa, A., Corrales, J.C.: Toward detecting crop diseases and pest by supervised learning. Revista Ingeniería y Universidad 19 (2015)Google Scholar
  38. 38.
    Schlimmer, J., Granger Jr, R.: Incremental learning from noisy data. Machine Learning 1, 317–354 (1986)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • David Camilo Corrales
    • 1
    • 3
    Email author
  • Apolinar Figueroa
    • 2
  • Agapito Ledezma
    • 3
  • Juan Carlos Corrales
    • 1
  1. 1.Grupo de Ingeniería TelemáticaUniversidad del CaucaPopayánColombia
  2. 2.Grupo de Estudios AmbientalesUniversidad del CaucaPopayánColombia
  3. 3.Departamento de Ciencias de la Computación e IngenieríaUniversidad Carlos III de MadridLeganésSpain

Personalised recommendations