Towards a contextual model for data quality in precision agriculture
DOI:
https://doi.org/10.22395/rium.v15n29a6Keywords:
data quality control, precision agriculture, metadata, data acquisition systems, contextual modelAbstract
Precision agriculture is a farming management concept, based on the crop variability in the field; it comprises several stages: data collection, information processing and decision-making. After an extensive review of the literature, it appears that data quality control is an important process in precision agriculture and can be considered in the data collection process. This paper makes an approach to data architecture quality control by applying the contextual information of the acquisition system (sad) and environment context information. This approach can provide the sad the capability to understand the situations of their environment in order to improve the quality of data for decision-making.
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[3] E. C.-H. Ngai and P. Gunningberg, “Quality-of-information-aware data collection for mobile sensor networksâ€, Pervasive Mob. Comput., vol. 11, pp. 203–215, 2014.
[4] S. Ji, R. Beyah, and Y. Li, “Continuous data collection capacity of wireless sensor networks under physical interference modelâ€, Mob. Adhoc Sens. Syst. …, 2011.
[5] C. L. Muller, L. Chapman, C. S. B. Grimmond, D. T. Young, and X.-M. Cai, “Toward a Standardized Metadata Protocol for Urban Meteorological Networksâ€, Bull. Am. Meteorol. Soc., vol. 94, no. 8, pp. 1161–1185, 2013.
[6] K. Hubbard, J. You, and M. Shulski, “Toward a Better Quality Control of Weather Dataâ€, pp. 3–30, 2012.
[7] C. Gwilliams, A. Preece, and A. Hardisty, “Local and global knowledge to improve the quality of sensed dataâ€, Int. J. …, vol. 2, N.° 2, pp. 164–180, 2012.
[8] A. K. Dey, “Understanding and Using Context,†Pers. Ubiquitous Comput., vol. 5, N.° 1, pp. 4–7, 2001.
[9] Organizacion Meteorológica Mundial - OMM, GuÃa de prácticas climatológicas Edición de 2011 OMM N.° 100. Ginebra Suiza, 2011.
[10] D. C. Van Essen, K. Ugurbil, E. Auerbach, D. Barch, T. E. J. Behrens, R. Bucholz, A. Chang, L. Chen, M. Corbetta, S. W. Curtiss, S. Della Penna, D. Feinberg, M. F. Glasser, N. Harel, a C. Heath, L. Larson-Prior, D. Marcus, G. Michalareas, S. Moeller, R. Oostenveld, S. E. Petersen, F. Prior, B. L. Schlaggar, S. M. Smith, a Z. Snyder, J. Xu, and E. Yacoub, “The Human Connectome Project: a data acquisition perspectiveâ€, Neuroimage, vol. 62, N.° 4, pp. 2222–31, 2012.
[11] C. a. Fiebrich, C. R. Morgan, A. G. McCombs, P. K. Hall, and R. a. McPherson, “Quality Assurance Procedures for Mesoscale Meteorological Dataâ€, J. Atmos. Ocean. Technol., vol. 27, N.° 10, pp. 1565–1582, 2010.
[12] D. Ballari, M. Wachowicz, and M. A. M. Callejo, “Metadata behind the interoperability of wireless sensor networksâ€, Sensors, vol. 9, N.° 5, pp. 3635–3651, 2009.
[13] G. Percivall, C. Reed, and J. Davidson, “Open Geospatial Consortium Inc . OGC White Paper OGC ® Sensor Web Enablement: Overview And High Level Architectureâ€, 2007 IEEE Autotestcon, vol. 4540, N.° December, pp. 1–14, 2007.
[14] R. Lemmens, T. Everding, C. Stasch, I. Simonis, J. Echterhoff, S. Liang, A. Bröring, and S. Jirka, New generation Sensor Web Enablement., vol. 11, N.° 3. 2011.
[15] S. Cox, “Observations and measurements-XML implementationâ€, OGC document. Open Geospatial Consortium Inc., pp. 1–76, 2011.
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[19] G. Huang, X. Y. Wu, M. Yuan, and R. F. Li, “Research on Data Quality of E&P Database Base on Metadata-Driven Data Quality Assessment Architectureâ€, Appl. Mech. Mater., vol. 530–531, pp. 813–817, 2014.
[20] J. Estévez, P. Gavilán, and J. V. Giráldez, “Guidelines on validation procedures for meteorological data from automatic weather stationsâ€, J. Hydrol., vol. 402, N.° 1–2, pp. 144–154, 2011.
[21] A. M. D. S. A. D. J. A. Ritaban Dutta Claire D’Este, “Dynamic Evaluation and Visualisation of the Quality and Reliability of Sensor Data Sourcesâ€, Int. J. Adv. Comput. Sci. Appl., vol. 4, N.° 8, pp. 96–103, 2013.
[22] C. Atzberger, “Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needsâ€, Remote Sens., vol. 5, N.° 2, pp. 949–981, 2013.
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Published
2015-11-07
How to Cite
Vivas Cantero, F. Y., Corrales, J. C., & Ramirez Gonzalez, G. A. (2015). Towards a contextual model for data quality in precision agriculture. Revista IngenierÃas Universidad De MedellÃn, 15(29), 99–112. https://doi.org/10.22395/rium.v15n29a6
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