Outlier detection methods and sensor data fusion for precision agriculture
Abstract
Precision agriculture is a concept regarding the use of technology to increase production yield while preserving and optimizing resources. One of the means to achieve that goal is to use sensors to monitor crops and adjust the cultivation according to its needs. This paper compares different techniques for sensor data fusion and detection and removal of outliers from gathered data to improve sensors accuracy and to identify possible sensor malfunction. As a case study, we monitored an experimental crop of precocious dwarf cashew using soil moisture sensors. Combining generalized ESD method and a weighted outlier-robust Kalman filter generated the best result, leading to more accurate data.
References
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Boström, H.; Andler, S. F.; Brohede, M.; Johansson, R.; Karlsson, A.; Laere, J. V.; Niklasson, L.; Nilsson, M.; Persson, A.; & Ziemke, T. “On the Definition of Information Fusion as a Field of Research.” In IKI Technical Reports, (October):S. 1–8 [2007]. doi:HS-IKI-TR-07-006.
Callegaro, R.; Montez, C.; Pinto, A. R.; & Moraes, R. “Uma Arquitetura para Fusão de Dados e Detecção de Outliers em Sensores de Baixo Custo de Redes de Sensores sem Fio.” In Anais do II Workshop de Comunicação em Sistemas Embarcados Críticos - WoCCES, S. 3–16 [2014]. DOI: 10.5753/cbie.wcbie.2015.1007.
Hubert, M. & Vandervieren, E. “An adjusted boxplot for skewed distributions.” In Computational statistics & data analysis, Band 52 (12):S. 5186–5201 [2008].
Iyengar, S. S.; Chakrabaraty, K.; & Qi, H. “Introduction to special issue on ’distributed sensor networks for real-tie systems with adaptive configuration’.” In Journal of the Franklin Institute, Band 338:S. 651–653 [2001].
Nakamura, E. F.; Loureiro, A. a. F.; & Frery, A. C. “Information fusion for wireless sensor networks.” In ACM Computing Surveys, Band 39 (3) [2007]. ISSN 03600300. DOI: 10.1145/1267070.1267073.
Natural Resources Conservation Service. “Precision Agriculture: NRCS Support for Emerging Technologies.” In Agronomy Technical Note [2007].
O’Haver, T. “A pragmatic introduction to signal processing.” [1997]. Ravichandran, J. & Arulappan, a. I. “Data validation algorithm for wireless sensor networks.” In International Journal of Distributed Sensor Networks, Band 2013 (iv) [2013]. DOI: 10.1155/2013/634278.
Ross, S. M. “Peirce’s criterion for the elimination of suspect experimental data.” In Journal of Engineering Technology, Band 20 (2):S. 38–41 [2003].
Sanchez, L.; Muñoz, L.; Galache, J. A.; Sotres, P.; Santana, J. R.; Gutierrez, V.; Ramdhany, R.; Gluhak, A.; Krco, S.; Theodoridis, E.; & Pfisterer, D. “SmartSantander: IoT experimentation over a smart city testbed.” In Computer Networks, Band 61:S. 217–238 [2013]. ISSN 13891286. DOI: 10.1016/j.bjp.2013.12.020.
SEMATECH, N. “e-Handbook of Statistical Methods.” [2003]. URL [link]. Accessed: 2017-03-16.
Taylor, J. R. An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements. University Science Books, 2nd Auflage [1997]. ISBN 0935702423,9780935702422.
Van Der Lee, R. “Vinduino: Open license project for agricultural irrigation management.” [2017]. URL [link]. Accessed: 2017-03-17.
Published
2017-07-02
How to Cite
TORRES, Andrei B. B.; ADRIANO FILHO, José; DA ROCHA, Atslands R.; GONDIM, Rubens Sonsol; DE SOUZA, José Neuman.
Outlier detection methods and sensor data fusion for precision agriculture. In: PROCEEDINGS OF BRAZILIAN SYMPOSIUM ON UBIQUITOUS AND PERVASIVE COMPUTING (SBCUP), 9. , 2017, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 928-937.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2017.3316.
