Sistema de alerta temprana para la roya en el café basado en códigos de salida de corrección de error: una propuesta

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.

• Autores: Corrales, David Camilo; Universidad del Cauca; Peña Q, Andrés J.; Centro de Investigaciones del Café; León, Carlos; ParqueSoft; Figueroa, Apolinar; Universidad del Cauca; Corrales, Juan Carlos; Universidad del Cauca
• Tipo de recurso: Article of journal
• Fecha de publicación: 2014
• Institución: Universidad de Medellín
• Repositorio: Repositorio UDEM
• Idioma: eng
• OAI Identifier: oai:repository.udem.edu.co:11407/1846
• Acceso en línea: http://hdl.handle.net/11407/1846
• Palabra clave: Coffee Rust Disease; Early Warning System; ECOC; SVM; Codeword.; roya; sistema de alerta temprana; ECOC; SVM; Codeword
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/