A Process for Increasing the Samples of Coffee Rust Through Machine Learning Methods

Jhonn Pablo Rodríguez, David Camilo Corrales and Juan Carlos Corrales
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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
Juan Carlos Corrales: Telematic Engineering Group, University of Cauca, Popayán, Colombia

International Journal of Agricultural and Environmental Information Systems (IJAEIS), 2018, vol. 9, issue 2, 32-52

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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jaeis0:v:9:y:2018:i:2:p:32-52

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