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Regression Techniques to Predict the Growth of Potato Tubers

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17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022) (SOCO 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 531))

Abstract

Potatoes (Solanum tuberosum) is one of the highest produced commodities for human consumption, with a global production of 359.07 mega metric ton per year and covering a huge cultivation area in the world. As a result, being able to predict the yield of this crop is an interesting topic, with a high impact on agriculture production. Accordingly, to predict some potato-tuber production and quality indicators, this work innovates by using phenotypical plant characteristics to which soft-computing techniques are applied. More precisely, the Linear Multiple-Regression, the Radial Basis Function Network, the Multilayer Perceptron, and the Support Vector Machine are benchmarked in the present work. Promising results have been obtained, validating the application of soft-computing techniques to predict the growth of potato tubers.

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Acknowledgements

Part of this work was funded by a grant agreement between Lab-Ferrer and UBUCOMP. Authors are grateful to the farmer Mr. José María Izquierdo for providing the experimental field and the monitoring of irrigation. Special thanks to Ms. Mercedes Yartu, whose master thesis contributed to the data gathering.

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Authors and Affiliations

  1. Grupo de Inteligencia Computacional Aplicada (GICAP), Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria s/n, 09006, Burgos, Spain

    Ángel Arroyo, Carlos Cambra, Nuño Basurto & Álvaro Herrero

  2. Grupo de Investigación en Compostaje (UBUCOMP), Escuela Politécnica Superior La Milanera, Universidad de Burgos, c/ Villadiego s/n, 09001, Burgos, Spain

    Carlos Rad & Milagros Navarro

Authors
  1. Ángel Arroyo
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  2. Carlos Cambra
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  3. Nuño Basurto
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  4. Carlos Rad
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  5. Milagros Navarro
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  6. Álvaro Herrero
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Corresponding authors

Correspondence to Ángel Arroyo or Carlos Cambra .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of León, León, Spain

    Hilde Pérez García

  3. Mechanical Engineering Department, University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martinez-de-Pison

  4. Inteligencia Artificial, University of Oviedo, A Coruña, La Coruña, Spain

    José Ramón Villar Flecha

  5. Data Science and Big Data Lab, Pablo de Olavide University, Sevilla, Spain

    Alicia Troncoso Lora

  6. University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  7. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  8. School of engineering, Pablo Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  9. DIGIP, University of Bergamo, Dalmine, Bergamo, Italy

    Giuseppe Psaila

  10. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  11. Department of Computing Science, University of Salamanca, Salamanca, Spain

    Emilio S. Corchado Rodriguez

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Arroyo, Á., Cambra, C., Basurto, N., Rad, C., Navarro, M., Herrero, Á. (2023). Regression Techniques to Predict the Growth of Potato Tubers. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_21

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