A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture

Laura Melgar-García, David Gutiérrez-Avilés, Maria Teresa Godinho, Rita Espada, Isabel Sofia Brito, Francisco Martínez-Álvarez, Alicia Troncoso, Cristina Rubio-Escudero

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
7 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)268-278
Number of pages11
JournalNeurocomputing
Volume500
DOIs
Publication statusPublished - 21 Aug 2022

Keywords

  • Big data triclustering
  • Precision agriculture
  • Spatio-temporal patterns

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