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.
CITATION STYLE
Melgar-García, L., Godinho, M. T., Espada, R., Gutiérrez-Avilés, D., Brito, I. S., Martínez-Álvarez, F., … Rubio-Escudero, C. (2021). Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering. In Advances in Intelligent Systems and Computing (Vol. 1268 AISC, pp. 226–236). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-57802-2_22
Mendeley helps you to discover research relevant for your work.