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Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering

  • Laura Melgar-García [1] ; Maria Teresa Godinho [2] [3] ; Rita Espada [6] ; David Gutiérrez-Avilés [1] ; Isabel Sofia Brito [2] [4] ; Francisco Martínez- Álvarez [1] ; Alicia Troncoso [1] ; Cristina Rubio-Escudero [5]
    1. [1] Universidad Pablo de Olavide

      Universidad Pablo de Olavide

      Sevilla, España

    2. [2] Instituto Politécnico de Beja

      Instituto Politécnico de Beja

      Beja (Santa Maria da Feira), Portugal

    3. [3] Universidade de Lisboa

      Universidade de Lisboa

      Socorro, Portugal

    4. [4] Instituto de Novas Tecnologias

      Instituto de Novas Tecnologias

      Socorro, Portugal

    5. [5] Universidad de Sevilla

      Universidad de Sevilla

      Sevilla, España

    6. [6] Associação dos Agricultores do Baixo Alentejo (Beja, Portugal)
  • Localización: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020): Burgos, Spain ; September 2020 / coord. por Álvaro Herrero Cosío Árbol académico, Carlos Cambra Baseca Árbol académico, Daniel Urda Muñoz Árbol académico, Javier Sedano Franco Árbol académico, Héctor Quintián Pardo Árbol académico, Emilio Santiago Corchado Rodríguez Árbol académico, 2021, ISBN 978-3-030-57802-2, págs. 226-236
  • Idioma: inglés
  • Texto completo no disponible (Saber más ...)
  • Resumen
    • 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.


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