Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering

  1. Laura Melgar-García 1
  2. Maria Teresa Godinho 24
  3. Rita Espada 3
  4. David Gutiérrez-Avilés 1
  5. Isabel Sofia Brito 25
  6. Francisco Martínez- Álvarez 1
  7. Alicia Troncoso 1
  8. Cristina Rubio-Escudero 6
  1. 1 Universidad Pablo de Olavide
    info

    Universidad Pablo de Olavide

    Sevilla, España

    ROR https://ror.org/02z749649

  2. 2 Instituto Politécnico de Beja
    info

    Instituto Politécnico de Beja

    Beja, Portugal

    ROR https://ror.org/00t9n0h58

  3. 3 Associação dos Agricultores do Baixo Alentejo (Beja, Portugal)
  4. 4 Universidade de Lisboa
    info

    Universidade de Lisboa

    Lisboa, Portugal

    ROR https://ror.org/01c27hj86

  5. 5 Instituto de Novas Tecnologias
    info

    Instituto de Novas Tecnologias

    Lisboa, Portugal

    ROR https://ror.org/00we1pa83

  6. 6 Universidad de Sevilla
    info

    Universidad de Sevilla

    Sevilla, España

    ROR https://ror.org/03yxnpp24

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Buch:
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.)

Verlag: Springer Suiza

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

Datum der Publikation: 2021

Seiten: 226-236

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

Art: Konferenz-Beitrag

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Zusammenfassung

Agriculture has undergone some very important changes over the last few decades. The emergence and evolution of precision agricultura has allowed to move from the uniform site management to the site-specific management, with both economic and environmental advantages. However, to be implemented effectively, site-specific management requires within-field spatial variability to be well-known and characterized. In this paper, an algorithm that delineates within-field management zones in a maize plantation is introduced. The algorithm, based on triclustering, mines clusters from temporal remote sensing data. Data from maize crops in Alentejo, Portugal, have been used to assess the suitability of applying triclustering to discover patterns over time, that may eventually help farmers to improve their harvests.

Fuente de los datos: Dialnet