Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering

Laura Melgar-García; Maria Teresa Godinho; Rita Espada; David Gutiérrez-Avilés; Isabel Sofia Brito; Francisco Martínez-Álvarez; Alicia Troncoso; Cristina Rubio-Escudero

doi:10.1007/978-3-030-57802-2_22

Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering

Laura Melgar-García, Maria Teresa Godinho, Rita Espada, David Gutiérrez-Avilés, Isabel Sofia Brito, Francisco Martínez-Álvarez, Alicia Troncoso, Cristina Rubio-Escudero

UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Citations (Scopus)

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.

Original language	English
Title of host publication	15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020
Editors	Álvaro Herrero, Carlos Cambra, Daniel Urda, Javier Sedano, Héctor Quintián, Emilio Corchado
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	226-236
Number of pages	11
ISBN (Print)	9783030578015
DOIs	https://doi.org/10.1007/978-3-030-57802-2_22
Publication status	Published - 2021
Event	15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020 - Burgos, Spain Duration: 16 Sept 2020 → 18 Sept 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1268 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020
Country/Territory	Spain
City	Burgos
Period	16/09/20 → 18/09/20

Keywords

Precision agriculture
Remote sensing
Spatio-temporal patterns
Triclustering

Access to Document

10.1007/978-3-030-57802-2_22

Cite this

Melgar-García, L., Godinho, M. T., Espada, R., Gutiérrez-Avilés, D., Brito, I. S., Martínez-Álvarez, F., Troncoso, A., & Rubio-Escudero, C. (2021). Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering. In Á. Herrero, C. Cambra, D. Urda, J. Sedano, H. Quintián, & E. Corchado (Eds.), 15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020 (pp. 226-236). (Advances in Intelligent Systems and Computing; Vol. 1268 AISC). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-57802-2_22

Melgar-García, Laura ; Godinho, Maria Teresa ; Espada, Rita et al. / Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering. 15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020. editor / Álvaro Herrero ; Carlos Cambra ; Daniel Urda ; Javier Sedano ; Héctor Quintián ; Emilio Corchado. Springer Science and Business Media Deutschland GmbH, 2021. pp. 226-236 (Advances in Intelligent Systems and Computing).

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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.",

keywords = "Precision agriculture, Remote sensing, Spatio-temporal patterns, Triclustering",

author = "Laura Melgar-Garc{\'i}a and Godinho, {Maria Teresa} and Rita Espada and David Guti{\'e}rrez-Avil{\'e}s and Brito, {Isabel Sofia} and Francisco Mart{\'i}nez-{\'A}lvarez and Alicia Troncoso and Cristina Rubio-Escudero",

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Melgar-García, L, Godinho, MT, Espada, R, Gutiérrez-Avilés, D, Brito, IS, Martínez-Álvarez, F, Troncoso, A & Rubio-Escudero, C 2021, Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering. in Á Herrero, C Cambra, D Urda, J Sedano, H Quintián & E Corchado (eds), 15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020. Advances in Intelligent Systems and Computing, vol. 1268 AISC, Springer Science and Business Media Deutschland GmbH, pp. 226-236, 15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020, Burgos, Spain, 16/09/20. https://doi.org/10.1007/978-3-030-57802-2_22

Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering. / Melgar-García, Laura; Godinho, Maria Teresa; Espada, Rita et al.
15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020. ed. / Álvaro Herrero; Carlos Cambra; Daniel Urda; Javier Sedano; Héctor Quintián; Emilio Corchado. Springer Science and Business Media Deutschland GmbH, 2021. p. 226-236 (Advances in Intelligent Systems and Computing; Vol. 1268 AISC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Melgar-García, Laura

AU - Godinho, Maria Teresa

AU - Espada, Rita

AU - Gutiérrez-Avilés, David

AU - Brito, Isabel Sofia

AU - Martínez-Álvarez, Francisco

AU - Troncoso, Alicia

AU - Rubio-Escudero, Cristina

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AB - 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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A2 - Sedano, Javier

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Melgar-García L, Godinho MT, Espada R, Gutiérrez-Avilés D, Brito IS, Martínez-Álvarez F et al. Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering. In Herrero Á, Cambra C, Urda D, Sedano J, Quintián H, Corchado E, editors, 15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020. Springer Science and Business Media Deutschland GmbH. 2021. p. 226-236. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-57802-2_22

Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering

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