Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering
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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]
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[1] Universidad Pablo de Olavide
Universidad Pablo de Olavide
Sevilla, España
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[2] Instituto Politécnico de Beja
Instituto Politécnico de Beja
Beja (Santa Maria da Feira), Portugal
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[3] Universidade de Lisboa
Universidade de Lisboa
Socorro, Portugal
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[4] Instituto de Novas Tecnologias
Instituto de Novas Tecnologias
Socorro, Portugal
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[5] Universidad de Sevilla
Universidad de Sevilla
Sevilla, España
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[6] Associação dos Agricultores do Baixo Alentejo (Beja, Portugal)
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- Localización: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020): Burgos, Spain ; September 2020 / coord. por Álvaro Herrero Cosío
, Carlos Cambra Baseca , Daniel Urda Muñoz , Javier Sedano Franco , Héctor Quintián Pardo , Emilio Santiago Corchado Rodríguez , 2021, ISBN 978-3-030-57802-2, págs. 226-236 - Idioma: inglés
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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.
