Viability of an Alarm Predictor for Coffee Rust Disease Using Interval Regression

Abstract

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.

The research reported here is supported in part under grant TIN2008-06247 from the MICINN (Ministerio de Ciencia e Innovación, of Spain), and grant 2009/07366- 5 from the FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo, Brazil).