Viability Of An Alarm Predictor For Coffee Rust Disease Using Interval Regression

We present a method to formulate predictions regarding continuous variables using regressors able to predict intervals rather than single points. They can be learned explicitly using the so-called insensitive zone of regression Support Vector Machines (SVM). The motivation for this research is the study of a real case; we discuss the feasibility of an alarm system for coffee rust, the main coffee crop disease in the world. The objective is to predict whether the percentage of infected coffee leaves (the incidence of the disease) will be above a given threshold. The requirements of such a system include avoiding false negatives, seeing as these would lead to not preventing the disease. The aim of reliable predictions, on the other hand, is to use chemical prevention of the disease only when necessary in order to obtain healthier products and reductions in costs and environmental impact. Although the breadth of the predicted intervals improves the reliability of predictions, it also increases the number of uncertain situations, i.e. those whose predictions include incidences both below and above the threshold. These cases would require deeper analysis. Our conclusion is that it is possible to reach a trade-off that makes the implementation of an alarm system for coffee rust disease feasible. © 2010 Springer-Verlag.6097 LNAIPART 2337346Alonso, J., Del Coz, J.J., Díez, J., Luaces, O., Bahamonde, A., Learning to predict one or more ranks in ordinal regression tasks (2008) LNCS (LNAI), 5211, pp. 39-54. , Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. Springer, HeidelbergBartlett, P., Wegkamp, M., Classiffication with a reject option using a hinge loss (2008) Journal of Machine Learning Research, 9, pp. 1823-1840Chang, C.C., Lin, C.J., (2001) LIBSVM: A Library for Support Vector Machines, , http://www.csie.ntu.edu.tw/~cjlin/libsvm, software available atChow, C., On optimum recognition error and reject tradeoff (1970) IEEE Transactions on Information Theory, 16 (1), pp. 41-46Del Coz, J.J., Díez, J., Bahamonde, A., Learning nondeterministic classifiers (2009) Journal of Machine Learning Research, 10, pp. 2273-2293Japiassú, L., Garcia, A., Miguel, A., Carvalho, C., Ferreira, R., Padilha, L., Matiello, J., Influência da carga pendente, do espaçamento e de fatores climáticos no desenvolvimento da ferrugem do cafeeiro Simpósio de Pesquisa Dos Cafés Do Brasil, Águas de Lindóia, SP, Brasil (2007)Meira, C., Rodrigues, L., De Moraes, S., Análise da epidemia da ferrugem do cafeeiro com árvore de decisão (2008) Tropical Plant Pathology, 33 (2), pp. 114-124Meira, C., Rodrigues, L., De Moraes, S., Modelos de alerta para o controle da ferrugem-do-cafeeiro em lavouras com alta carga pendente (2009) Pesq. Agropec. Bras, 44 (3), pp. 233-24