Artículo
A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture
Autor/es | Melgar García, Laura
Gutiérrez Avilés, David Godinho, María Teresa Espada, Rita Brito, Isabel Sofía Martínez Álvarez, Francisco Troncoso Lora, Alicia Rubio Escudero, Cristina |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2022 |
Fecha de depósito | 2022-12-01 |
Publicado en |
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Resumen | 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, ... 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 pat-terns 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. |
Agencias financiadoras | Ministerio de Ciencia e Innovación (MICIN). España Junta de Andalucía Fundação para a Ciência e a Tecnologia (FCT) |
Identificador del proyecto | PID2020-117954RB
PY20-00870 UPO-138516 UIDB/00066/2020 |
Cita | Melgar García, L., Gutiérrez Avilés, D., Godinho, M.T., Espada, R., Brito, I.S., Martínez Álvarez, F.,...,Rubio Escudero, C. (2022). A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture. Neurocomputing, 500 (August 2022), 268-278. https://doi.org/10.1016/j.neucom.2021.06.101. |
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