Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering
- Laura Melgar-García 1
- Maria Teresa Godinho 24
- Rita Espada 3
- David Gutiérrez-Avilés 1
- Isabel Sofia Brito 25
- Francisco Martínez- Álvarez 1
- Alicia Troncoso 1
- Cristina Rubio-Escudero 6
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1
Universidad Pablo de Olavide
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2
Instituto Politécnico de Beja
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- 3 Associação dos Agricultores do Baixo Alentejo (Beja, Portugal)
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4
Universidade de Lisboa
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5
Instituto de Novas Tecnologias
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6
Universidad de Sevilla
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- Álvaro Herrero (coord.)
- Carlos Cambra (coord.)
- Daniel Urda (coord.)
- Javier Sedano (coord.)
- Héctor Quintián (coord.)
- Emilio Corchado (coord.)
Editorial: Springer Suiza
ISBN: 978-3-030-57801-5, 978-3-030-57802-2
Año de publicación: 2021
Páginas: 226-236
Congreso: International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO (15. 2020. Burgos)
Tipo: Aportación congreso
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.