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Title: A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture
Authors: Melgar-García, L.
Gutiérrez-Avilés, D.
Godinho, Maria Teresa
Espada, R.
Brito, Isabel Sofia
Keywords: Computer Science
Machine learning
Big data triclustering
Precision agriculture
Spatio-temporal patterns
Issue Date: Aug-2022
Publisher: Elsevier
Citation: Melgar-García, L., Gutiérrez-Avilés, D., Godinho, M., Espada, R., Brito, I., Martínez-Álvarez, F., Troncoso, A. & Rubio-Escudero, E. (2022). A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture. Neurocomputing, 500, 268–278.
Abstract: Precision agriculture focuses on the development of site-specific harvest considering the variability of each crop area. Vegetation indices allow the study and delineation of different characteristics of each field zone, generally invisible to the naked-eye. This paper introduces a new big data triclustering approach based on evolutionary algorithms. The algorithm shows its capability to discover three-dimensional patterns on the basis of vegetation indices from vine crops. Different vegetation indices have been tested to find different patterns in the crops. The results reported using a vineyard crop located in Portugal depicts four areas with different moisture stress particularities that can lead to changes in the management of the vineyard. Furthermore, scalability studies have been performed, showing that the proposed algorithm is suitable for dealing with big datasets.
Peer reviewed: yes
ISSN: 1872-8286
Publisher version:
Appears in Collections:D-ENG - Artigos em revistas indexadas à WoS/Scopus

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