Early warning system for coffee rust disease based on error correcting output codes: a proposal
Colombian coffee producers have had to face the severe consequences of the coffee rust disease since it was first reported in the country in 1983. Recently, machine learning researchers have tried to predict infection through classifiers such as decision trees, regression Support Vector Machines (SVM), non-deterministic classifiers and Bayesian Networks, but it has been theoretically and empirically demonstrated that combining multiple classifiers can substantially improve the classification performance of the constituent members. An Early Warning System (EWS) for coffee rust disease was therefore proposed based on Error Correcting Output Codes (ECOC) and SVM to compute the binary functions of Plant Density, Shadow Level, Soil Acidity, Last Nighttime Rainfall Intensity and Last Days Relative Humidity.
Author BiographiesDavid Camilo Corrales, Universidad del Cauca
M.Sc. in Telematics Engineering, and Researcher of Telematics Engineering Group and Environmental Study
Group at University of Cauca, Colombia.Andrés J. Peña Q, Centro de Investigaciones del CaféM.Sc. in Meteorology, and Researcher at National Coffee Research Center (Cenicafé), ColombiaCarlos León, ParqueSoftM.Sc. in Electrical Engineering, and CEO of CreaTIC Corporation - Parquesoft, Colombia.Apolinar Figueroa, Universidad del Cauca
Doctor of Biological Sciences, and Full Professor and Leader of the Environmental Study Group at University
of Cauca, Colombia.Juan Carlos Corrales, Universidad del Cauca
Doctor of Philosophy in Sciences, Speciality Computer Science, and Full Professor and Leader of the Telematics Engineering Group at University of Cauca, Colombia
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