A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture
- Others:
- Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
- Universidad de Sevilla. TIC-254: Data Science and Big Data Lab
- Universidad de Sevilla. TIC-134: Sistemas Informáticos
- Ministerio de Ciencia e Innovación (MICIN). España
- Junta de Andalucía
- Fundação para a Ciência e a Tecnologia (FCT)
Description
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.
Abstract
Ministerio de Ciencia e Innovación PID2020-117954RB
Abstract
Junta de Andalucía PY20-00870
Abstract
Junta de Andalucía UPO-138516
Abstract
Fundação para a Ciência e a Tecnologia (FCT) UIDB/00066/2020
Additional details
- URL
- https://idus.us.es/handle//11441/140020
- URN
- urn:oai:idus.us.es:11441/140020
- Origin repository
- USE