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A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture
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Orientador(es)
Resumo(s)
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.
Descrição
Funding Information:
The authors would like to thank the Spanish Ministry of Science and Innovation for the support under the project PID2020-117954RB and the European Regional Development Fund and Junta de Andalucía for projects PY20-00870 and UPO-138516. This work could not have been done without the support and help of the Farmer’s Association of Baixo Alentejo and Francisco Palma during the whole project. Finally, the authors thank António Vieira Lima and Moragri S. A. for giving access to data.
Publisher Copyright:
© 2022
Palavras-chave
Big data triclustering Precision agriculture Spatio-temporal patterns Computer Science Applications Cognitive Neuroscience Artificial Intelligence