Neural Models to Predict Irrigation Needs of a Potato Plantation

  1. Mercedes Yartu 1
  2. Carlos Cambra 1
  3. Milagros Navarro 1
  4. Carlos Rad 1
  5. Ángel Arroyo 1
  6. Álvaro Herrero 1
  1. 1 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

Libro:
15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020): Burgos, Spain ; September 2020
  1. Álvaro Herrero (coord.)
  2. Carlos Cambra (coord.)
  3. Daniel Urda (coord.)
  4. Javier Sedano (coord.)
  5. Héctor Quintián (coord.)
  6. Emilio Corchado (coord.)

Editorial: Springer Suiza

ISBN: 978-3-030-57801-5,  978-3-030-57802-2

Año de publicación: 2021

Páginas: 600-613

Congreso: International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO (15. 2020. Burgos)

Tipo: Aportación congreso

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Resumen

Reducing water consumption is an important target required for a sustainable farming. In order to do that, the actual water needs of different crops must be known and irrigation scheduling must be adjusted to satisfy them. This is a complex task as the phenology of plants and its water demand vary with soil properties and weather conditions. To address such problem, present paper proposes the application of time-series neural networks in order to predict the soilwater content in a potato field crop, in which a soil humidity probe was installed. More precisely, Non-linear Input-Output, Non-linear Autoregressive and Non-linear Autoregressive with Exogenous Input models are applied. They are benchmarked, together with different interpolationmethods in order to find the best combination for accurately predicting water needs. Promising results have been obtained, supporting the proposed models and their viability when predicting the real humidity level in the soil.

Fuente de los datos: Dialnet