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A Process for Increasing the Samples of Coffee Rust Through Machine Learning Methods
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Author(s): Jhonn Pablo Rodríguez (University of Cauca, Popayán, Colombia), 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)and Juan Carlos Corrales (Telematic Engineering Group, University of Cauca, Popayán, Colombia)
Copyright: 2018
Volume: 9
Issue: 2
Pages: 21
Source title:
International Journal of Agricultural and Environmental Information Systems (IJAEIS)
Editor(s)-in-Chief: Frederic Andres (National Institute of Informatics, Japan), Chutiporn Anutariya (Asian Institute of Technology, Thailand), Teeradaj Racharak (Japan Advanced Institute of Science and Technology, Japan)and Watanee Jearanaiwongkul (National institute of Informatics, Japan)
DOI: 10.4018/IJAEIS.2018040103
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Abstract
This article describes how coffee rust has become a serious concern for many coffee farmers and manufacturers. The American Phytopathological Society discusses its importance saying this: “…the most economically important coffee disease in the world…” while “…in monetary value, coffee is the most important agricultural product in international trade…” The early detection has inspired researchers to apply supervised learning algorithms on predicting the disease appearance. However, the main issue of the related works is the small number of samples of the dependent variable: Incidence Percentage of Rust, since the datasets do not have a reliable representation of the disease, which will generate inaccurate predictions in the models. This article provides a process about coffee rust to select appropriate machine learning methods to increase rust samples.
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