Abstract
Agriculture has undergone some very important changes over the last few decades. The emergence and evolution of precision agriculture has allowed to move from the uniform site management to the site-specific management, with both economic and environmental advantages. However, to be implemented effectively, site-specific management requires within-field spatial variability to be well-known and characterized. In this paper, an algorithm that delineates within-field management zones in a maize plantation is introduced. The algorithm, based on triclustering, mines clusters from temporal remote sensing data. Data from maize crops in Alentejo, Portugal, have been used to assess the suitability of applying triclustering to discover patterns over time, that may eventually help farmers to improve their harvests.
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Acknowledgements
The authors would like to thank the Spanish Ministry of Economy and Competitiveness for the support under project TIN2017-88209 and Fundação para a Ciência e a Tecnologia (FCT), under the project UIDB/04561/2020. The authors would also like to thank António Vieira Lima for giving access to data and Francisco Palma for his support to the whole project.
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Melgar-García, L. et al. (2021). Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_22
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