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A Guideline for Building Large Coffee Rust Samples Applying Machine Learning Methods

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Advances in Information and Communication Technologies for Adapting Agriculture to Climate Change (AACC'17 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 687))

Abstract

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 drawback of the related works is the few data samples of the dependent variable: Incidence Rate of Rust, since the datasets do not have a reliable representation of the disease, which will generate inaccurate classifiers. This paper provides a guide to increase coffee rust samples applying machine learning methods through a systematic review about coffee rust in order to select appropriate algorithms to increase rust samples.

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Authors and Affiliations

  1. Department of Telematics Engineering, Engineering Telematics Group, University of Cauca, Popayán, Colombia

    Jhonn Pablo Rodríguez, Edwar Javier Girón, David Camilo Corrales & Juan Carlos Corrales

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  1. Jhonn Pablo Rodríguez
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  2. Edwar Javier Girón
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  3. David Camilo Corrales
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  4. Juan Carlos Corrales
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Correspondence to Jhonn Pablo Rodríguez .

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  1. School of Computing and Communications, Lancaster University, Lancaster, United Kingdom

    Plamen Angelov

  2. Computer Science Department, Carlos III University of Madrid, Leganés, Madrid, Spain

    Jose Antonio Iglesias

  3. Campus de Tulcán, University of Cauca, Popayán, Colombia

    Juan Carlos Corrales

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Rodríguez, J.P., Girón, E.J., Corrales, D.C., Corrales, J.C. (2018). A Guideline for Building Large Coffee Rust Samples Applying Machine Learning Methods. In: Angelov, P., Iglesias, J., Corrales, J. (eds) Advances in Information and Communication Technologies for Adapting Agriculture to Climate Change. AACC'17 2017. Advances in Intelligent Systems and Computing, vol 687. Springer, Cham. https://doi.org/10.1007/978-3-319-70187-5_8

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