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A Tool for Classification of Cacao Production in Colombia Based on Multiple Classifier Systems

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Computational Science and Its Applications – ICCSA 2017 (ICCSA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10405))

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Abstract

Cacao is one of the central crops that support the agrarian production in Colombia. In some areas, it is the main source of income for about 25.000 families. Frequently, its production is affected by several factors such as climate, soil, water, wind, among others. This paper presents a Machine Learning approach for classifying cacao production in the region of Santander, Colombia. The proposed system aims to link climate and cacao production data to develop the classification task. In this sense, several techniques were experimentally evaluated in order to determine the algorithm that generates the best model to classify new climate instances on the cacao production dataset. Experimental results showed a better precision for Random Forest in comparison with other evaluated techniques.

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Acknowledgements

The authors would like to thank Universidad del Cauca, AgroCloud project of the RICCLISA program for supporting this research, and Colciencias (Colombia) for PhD scholarship granted to MSc. Iván Darío López and MSc(c). Julián Eduardo Plazas.

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Authors and Affiliations

  1. Grupo de Ingeniería Telemática (GIT), Universidad del Cauca, Popayán, Colombia

    Julián Eduardo Plazas, Iván Darío López & Juan Carlos Corrales

Authors
  1. Julián Eduardo Plazas
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  2. Iván Darío López
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  3. Juan Carlos Corrales
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Corresponding author

Correspondence to Iván Darío López .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Trieste, Trieste, Italy

    Giuseppe Borruso

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  9. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  10. University of Trieste, Trieste, Italy

    Alfredo Cuzzocrea

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Plazas, J.E., López, I.D., Corrales, J.C. (2017). A Tool for Classification of Cacao Production in Colombia Based on Multiple Classifier Systems. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10405. Springer, Cham. https://doi.org/10.1007/978-3-319-62395-5_5

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  • DOI: https://doi.org/10.1007/978-3-319-62395-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62394-8

  • Online ISBN: 978-3-319-62395-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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