Two-Level Classifier Ensembles for Coffee Rust Estimation in Colombian Crops

Two-Level Classifier Ensembles for Coffee Rust Estimation in Colombian Crops

David Camilo Corrales (Telematic Engineering Group, University of Cauca, Popayán, Colombia and Department of Computer Science and Engineering, Carlos III University of Madrid, Madrid, Spain), Apolinar Figueroa Casas (Environmental Studies Group, University of Cauca, Popayán, Colombia), Agapito Ledezma (Department of Computer Science and Engineering, Charles III University of Madrid, Madrid, Spain) and Juan Carlos Corrales (Telematic Engineering Group, University of Cauca, Popayán, Colombia)
DOI: 10.4018/IJAEIS.2016070103
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Abstract

Rust is a disease that leads to considerable losses in the worldwide coffee industry. There are many contributing factors to the onset of coffee rust e.g. Crop management decisions and the prevailing weather. In Colombia the coffee production has been considerably reduced by 31% on average during the epidemic years compared with 2007. Recent research efforts focus on detection of disease incidence using simple classifiers. Authors in the computer field propose alternatives for improve the outcomes, making use of techniques that combine classifiers named ensemble methods. Therefore they proposed two-level classifier ensembles for coffee rust estimation in Colombian crops using Back Propagation Neural Networks, Regression Tree M5 and Support Vector Regression. Their ensemble approach outperformed the classical approaches as simple classifiers and ensemble methods in terms of Pearson's Correlation Coefficient, Mean Absolute Error and Root Mean Squared Error.
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2. Background

This section describes the data collection process and the generation of the dataset used in experiments, introduces algorithms which comprise the two-level classifier ensembles.

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