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 (S...

Full description

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
Rights
License
http://creativecommons.org/licenses/by-nc-sa/4.0/
Description
Summary: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.