Abstract
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.
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
Cristancho, M., Rozo, Y., Escobar, C., Rivillas, C., Gaitán, Á.: Razas de roya (2012)
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)
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)
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)
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)
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)
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)
Meira, C., Rodrigues, L., Moraes, S.: Análise da epidemia da ferrugem do cafeeiro com árvore de decisão. Tropical Plant Pathology 33, 114–124 (2008)
Li, L., Zou, B., Hu, Q., Wu, X., Yu, D.: Dynamic classifier ensemble using classification confidence. Neurocomputing 99, 581–591 (2013)
Ranawana, R., Palade, V.: Multi-Classifier Systems: Review and a roadmap for developers. Int. J. Hybrid Intell. Syst. 3, 35–61 (2006)
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)
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)
Jain, R., Minz, S.: Ramasubramanian: Machine Learning for Forewarning Crop Diseases. Journal of the Indian Society of Agricultural Statistics 63, 97–107 (2009)
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)
Mitchell, T.: Machine learning. McGraw-Hill (1997)
Poh, H.L.: A neural network approach for marketing strategies research and decision support, vol. Ph.D. Thesis. Stanford University (1991)
Haykin, S.S.: Neural networks: a comprehensive foundation. Prentice Hall (2003)
Suhasini, A., Palanivel, S., Ramalingam, V.: Multimodel decision support system for psychiatry problem. Expert Systems with Applications 38, 4990–4997 (2011)
Bonakdar, L., Etemad-Shahidi, A.: Predicting wave run-up on rubble-mound structures using M5 model tree. Ocean Engineering 38, 111–118 (2011)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (2000)
Vapnik, V.N.: An overview of statistical learning theory. IEEE Transactions on Neural Networks 10, 988–999 (1999)
Balasundaram, S., Gupta, D.: Training Lagrangian twin support vector regression via unconstrained convex minimization. Knowledge-Based Systems 59, 85–96 (2014)
Skurichina, M., Duin, R.P.: Bagging, boosting and the random subspace method for linear classifiers. Pattern Analysis & Applications 5, 121–135 (2002)
Breiman, L.: Bagging Predictors. Machine Learning 24, 123–140 (1996)
Marqués, A.I., García, V., Sánchez, J.S.: Two-level classifier ensembles for credit risk assessment. Expert Systems with Applications 39, 10916–10922 (2012)
Tin Kam, H.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 832–844 (1998)
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)
Menahem, E., Rokach, L., Elovici, Y.: Troika – An improved stacking schema for classification tasks. Information Sciences 179, 4097–4122 (2009)
Gama, J., Brazdil, P.: Cascade Generalization. Machine Learning 41, 315–343 (2000)
McAlister, D.: The Law of the Geometric Mean. Proceedings of the Royal Society of London 29, 367–376 (1879)
Grubbs, F.: Procedures for Detecting Outlying Observations in Samples. Technometrics 11, 1–21 (1969)
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)
Araujo, B.S.: Aprendizaje automático: conceptos básicos y avanzados: aspectos prácticos utilizando el software Weka. Pearson Prentice Hall, España (2006)
Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc (1993)
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)
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)
Corrales, D.C., Figueroa, A., Corrales, J.C.: Toward detecting crop diseases and pest by supervised learning. Revista Ingeniería y Universidad 19 (2015)
Schlimmer, J., Granger Jr, R.: Incremental learning from noisy data. Machine Learning 1, 317–354 (1986)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Corrales, D.C., Figueroa, A., Ledezma, A., Corrales, J.C. (2015). An Empirical Multi-classifier for Coffee Rust Detection in Colombian Crops. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2015. ICCSA 2015. Lecture Notes in Computer Science(), vol 9155. Springer, Cham. https://doi.org/10.1007/978-3-319-21404-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-21404-7_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-21403-0
Online ISBN: 978-3-319-21404-7
eBook Packages: Computer ScienceComputer Science (R0)