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

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

Jhonn Pablo Rodríguez, David Camilo Corrales, Juan Carlos Corrales
Copyright: © 2018 |Volume: 9 |Issue: 2 |Pages: 21
ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781522545217|DOI: 10.4018/IJAEIS.2018040103
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MLA

Rodríguez, Jhonn Pablo, et al. "A Process for Increasing the Samples of Coffee Rust Through Machine Learning Methods." IJAEIS vol.9, no.2 2018: pp.32-52. http://doi.org/10.4018/IJAEIS.2018040103

APA

Rodríguez, J. P., Corrales, D. C., & Corrales, J. C. (2018). A Process for Increasing the Samples of Coffee Rust Through Machine Learning Methods. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 9(2), 32-52. http://doi.org/10.4018/IJAEIS.2018040103

Chicago

Rodríguez, Jhonn Pablo, David Camilo Corrales, and Juan Carlos Corrales. "A Process for Increasing the Samples of Coffee Rust Through Machine Learning Methods," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 9, no.2: 32-52. http://doi.org/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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