Aproximación a un modelo contextual para calidad de datos en agricultura de precisión
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 pr...
- Autores:
-
Vivas Cantero, Fulvio Yesid
Corrales, Juan Carlos
Ramirez Gonzalez, Gustavo Adolfo
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2016
- Institución:
- Universidad de Medellín
- Repositorio:
- Repositorio UDEM
- Idioma:
- spa
- OAI Identifier:
- oai:repository.udem.edu.co:11407/3530
- Acceso en línea:
- http://hdl.handle.net/11407/3530
- Palabra clave:
- Data quality control
Precision agriculture
Metadata
data acquisition systems
Contextual model
Control de calidad de los datos
Agricultura de precisión
metadatos
Sistemas de adquisición de datos
Modelo contextual
- Rights
- License
- http://creativecommons.org/licenses/by-nc-sa/4.0/
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dc.title.spa.fl_str_mv |
Aproximación a un modelo contextual para calidad de datos en agricultura de precisión Towards a contextual model for data quality in precision agriculture |
title |
Aproximación a un modelo contextual para calidad de datos en agricultura de precisión |
spellingShingle |
Aproximación a un modelo contextual para calidad de datos en agricultura de precisión Data quality control Precision agriculture Metadata data acquisition systems Contextual model Control de calidad de los datos Agricultura de precisión metadatos Sistemas de adquisición de datos Modelo contextual |
title_short |
Aproximación a un modelo contextual para calidad de datos en agricultura de precisión |
title_full |
Aproximación a un modelo contextual para calidad de datos en agricultura de precisión |
title_fullStr |
Aproximación a un modelo contextual para calidad de datos en agricultura de precisión |
title_full_unstemmed |
Aproximación a un modelo contextual para calidad de datos en agricultura de precisión |
title_sort |
Aproximación a un modelo contextual para calidad de datos en agricultura de precisión |
dc.creator.fl_str_mv |
Vivas Cantero, Fulvio Yesid Corrales, Juan Carlos Ramirez Gonzalez, Gustavo Adolfo |
dc.contributor.author.none.fl_str_mv |
Vivas Cantero, Fulvio Yesid Corrales, Juan Carlos Ramirez Gonzalez, Gustavo Adolfo |
dc.subject.spa.fl_str_mv |
Data quality control Precision agriculture Metadata data acquisition systems Contextual model Control de calidad de los datos Agricultura de precisión metadatos Sistemas de adquisición de datos Modelo contextual |
topic |
Data quality control Precision agriculture Metadata data acquisition systems Contextual model Control de calidad de los datos Agricultura de precisión metadatos Sistemas de adquisición de datos Modelo contextual |
description |
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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C. LAU, A. Jarvis, and J. Ramírez, “Agricultura Colombiana: Adaptación al Cambio Climático”, Cent. Int. Agric. Trop. (CIAT). 4p. …, vol. 1, p. 4, 2011. K. Ni, M. Srivastava, N. Ramanathan, M. N. H. Chehade, L. Balzano, S. Nair, S. Zahedi, E. Kohler, G. Pottie, and M. Hansen, “Sensor network data fault types”, ACM Transactions on Sensor Networks, vol. 5, N.° 3. pp. 1–29, 2009. E. C.-H. Ngai and P. Gunningberg, “Quality-of-information-aware data collection for mobile sensor networks”, Pervasive Mob. Comput., vol. 11, pp. 203–215, 2014. S. Ji, R. Beyah, and Y. Li, “Continuous data collection capacity of wireless sensor networks under physical interference model”, Mob. Adhoc Sens. Syst. …, 2011. C. L. Muller, L. Chapman, C. S. B. Grimmond, D. T. Young, and X.-M. Cai, “Toward a Standardized Metadata Protocol for Urban Meteorological Networks”, Bull. Am. Meteorol. Soc., vol. 94, no. 8, pp. 1161–1185, 2013. K. Hubbard, J. You, and M. Shulski, “Toward a Better Quality Control of Weather Data”, pp. 3–30, 2012. C. Gwilliams, A. Preece, and A. Hardisty, “Local and global knowledge to improve the quality of sensed data”, Int. J. …, vol. 2, N.° 2, pp. 164–180, 2012. A. K. Dey, “Understanding and Using Context,” Pers. Ubiquitous Comput., vol. 5, N.° 1, pp. 4–7, 2001. Organizacion Meteorológica Mundial - OMM, Guía de prácticas climatológicas Edición de 2011 OMM N.° 100. Ginebra Suiza, 2011. 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 perspective”, Neuroimage, vol. 62, N.° 4, pp. 2222–31, 2012. C. a. Fiebrich, C. R. Morgan, A. G. McCombs, P. K. Hall, and R. a. McPherson, “Quality Assurance Procedures for Mesoscale Meteorological Data”, J. Atmos. Ocean. Technol., vol. 27, N.° 10, pp. 1565–1582, 2010. D. Ballari, M. Wachowicz, and M. A. M. Callejo, “Metadata behind the interoperability of wireless sensor networks”, Sensors, vol. 9, N.° 5, pp. 3635–3651, 2009. G. Percivall, C. Reed, and J. Davidson, “Open Geospatial Consortium Inc . OGC White Paper OGC ® Sensor Web Enablement: Overview And High Level Architecture”, 2007 IEEE Autotestcon, vol. 4540, N.° December, pp. 1–14, 2007. 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. S. Cox, “Observations and measurements-XML implementation”, OGC document. Open Geospatial Consortium Inc., pp. 1–76, 2011. M. Botts and A. Robin, “OpenGIS ® Sensor Model Language ( SensorML) Implementation Specification”, Design. p. 180, 2007. A. Na and M. Priest, “Sensor Observation Service”, English, vol. OGC 06–009, N.° OGC 06–009r6, pp. 1–104, 2007. S. J. K. Mason, S. B. Cleveland, P. Llovet, C. Izurieta, and G. C. Poole, “A centralized tool for managing, archiving, and serving point-in-time data in ecological research laboratories”, Environ. Model. Softw., vol. 51, pp. 59–69, 2014. 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 Architecture”, Appl. Mech. Mater., vol. 530–531, pp. 813–817, 2014. J. Estévez, P. Gavilán, and J. V. Giráldez, “Guidelines on validation procedures for meteorological data from automatic weather stations”, J. Hydrol., vol. 402, N.° 1–2, pp. 144–154, 2011. 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 Sources”, Int. J. Adv. Comput. Sci. Appl., vol. 4, N.° 8, pp. 96–103, 2013. C. Atzberger, “Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs”, Remote Sens., vol. 5, N.° 2, pp. 949–981, 2013. |
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Vivas Cantero, Fulvio YesidCorrales, Juan CarlosRamirez Gonzalez, Gustavo AdolfoVivas Cantero, Fulvio Yesid; Universidad del CaucaCorrales, Juan Carlos; Universidad del CaucaRamirez Gonzalez, Gustavo Adolfo; Universidad del Cauca2017-06-29T22:22:34Z2017-06-29T22:22:34Z2016-12-311692-3324http://hdl.handle.net/11407/3530 http://dx.doi.org/10.22395/rium.v15n29a62248-4094reponame:Repositorio Institucional Universidad de Medellínrepourl:https://repository.udem.edu.co/instname:Universidad de MedellínPrecision 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.La agricultura de precisión es un concepto agronómico de gestión de parcelas agrícolas, basado en la existencia de variabilidad en campo; comprende varias etapas: recolección de datos, procesamiento de información y toma de decisiones. Después de una extensa revisión de la literatura, se observa que el control de calidad de los datos es un proceso muy importante para agricultura de precisión que puede ser considerado en la recolección de datos. En este artículo se da una aproximación a una arquitectura de control de calidad de datos utilizando la información de contexto del sistema de adquisición (SAD) y el medio ambiente. Este enfoque puede proporcionar a los SAD la capacidad de comprender las situaciones de su entorno con el fin de mejorar la calidad de datos para la toma de decisiones.p. 99-112Electrónicoapplication/pdfspaUniversidad de MedellínIngeniería AmbientalFacultad de IngenieríasMedellínhttp://revistas.udem.edu.co/index.php/ingenierias/article/view/1079152999112C. LAU, A. Jarvis, and J. Ramírez, “Agricultura Colombiana: Adaptación al Cambio Climático”, Cent. Int. Agric. Trop. (CIAT). 4p. …, vol. 1, p. 4, 2011.K. Ni, M. Srivastava, N. Ramanathan, M. N. H. Chehade, L. Balzano, S. Nair, S. Zahedi, E. Kohler, G. Pottie, and M. Hansen, “Sensor network data fault types”, ACM Transactions on Sensor Networks, vol. 5, N.° 3. pp. 1–29, 2009.E. C.-H. Ngai and P. Gunningberg, “Quality-of-information-aware data collection for mobile sensor networks”, Pervasive Mob. Comput., vol. 11, pp. 203–215, 2014.S. Ji, R. Beyah, and Y. Li, “Continuous data collection capacity of wireless sensor networks under physical interference model”, Mob. Adhoc Sens. Syst. …, 2011.C. L. Muller, L. Chapman, C. S. B. Grimmond, D. T. Young, and X.-M. Cai, “Toward a Standardized Metadata Protocol for Urban Meteorological Networks”, Bull. Am. Meteorol. Soc., vol. 94, no. 8, pp. 1161–1185, 2013.K. Hubbard, J. You, and M. Shulski, “Toward a Better Quality Control of Weather Data”, pp. 3–30, 2012.C. Gwilliams, A. Preece, and A. Hardisty, “Local and global knowledge to improve the quality of sensed data”, Int. J. …, vol. 2, N.° 2, pp. 164–180, 2012.A. K. Dey, “Understanding and Using Context,” Pers. Ubiquitous Comput., vol. 5, N.° 1, pp. 4–7, 2001.Organizacion Meteorológica Mundial - OMM, Guía de prácticas climatológicas Edición de 2011 OMM N.° 100. Ginebra Suiza, 2011.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 perspective”, Neuroimage, vol. 62, N.° 4, pp. 2222–31, 2012.C. a. Fiebrich, C. R. Morgan, A. G. McCombs, P. K. Hall, and R. a. McPherson, “Quality Assurance Procedures for Mesoscale Meteorological Data”, J. Atmos. Ocean. Technol., vol. 27, N.° 10, pp. 1565–1582, 2010.D. Ballari, M. Wachowicz, and M. A. M. Callejo, “Metadata behind the interoperability of wireless sensor networks”, Sensors, vol. 9, N.° 5, pp. 3635–3651, 2009.G. Percivall, C. Reed, and J. Davidson, “Open Geospatial Consortium Inc . OGC White Paper OGC ® Sensor Web Enablement: Overview And High Level Architecture”, 2007 IEEE Autotestcon, vol. 4540, N.° December, pp. 1–14, 2007.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.S. Cox, “Observations and measurements-XML implementation”, OGC document. Open Geospatial Consortium Inc., pp. 1–76, 2011.M. Botts and A. Robin, “OpenGIS ® Sensor Model Language ( SensorML) Implementation Specification”, Design. p. 180, 2007.A. Na and M. Priest, “Sensor Observation Service”, English, vol. OGC 06–009, N.° OGC 06–009r6, pp. 1–104, 2007.S. J. K. Mason, S. B. Cleveland, P. Llovet, C. Izurieta, and G. C. Poole, “A centralized tool for managing, archiving, and serving point-in-time data in ecological research laboratories”, Environ. Model. Softw., vol. 51, pp. 59–69, 2014.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 Architecture”, Appl. Mech. Mater., vol. 530–531, pp. 813–817, 2014.J. Estévez, P. Gavilán, and J. V. Giráldez, “Guidelines on validation procedures for meteorological data from automatic weather stations”, J. Hydrol., vol. 402, N.° 1–2, pp. 144–154, 2011.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 Sources”, Int. J. Adv. Comput. Sci. Appl., vol. 4, N.° 8, pp. 96–103, 2013.C. Atzberger, “Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs”, Remote Sens., vol. 5, N.° 2, pp. 949–981, 2013.Revista Ingenierías Universidad de Medellínhttp://creativecommons.org/licenses/by-nc-sa/4.0/Attribution-NonCommercial-ShareAlike 4.0 Internationalhttp://purl.org/coar/access_right/c_abf2Revista Ingenierías Universidad de Medellín; Vol. 15, núm. 29 (2016); 99-1122248-40941692-3324Data quality controlPrecision agricultureMetadatadata acquisition systemsContextual modelControl de calidad de los datosAgricultura de precisiónmetadatosSistemas de adquisición de datosModelo contextualAproximación a un modelo contextual para calidad de datos en agricultura de precisiónTowards a contextual model for data quality in precision agricultureArticlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Artículo científicoinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85Comunidad Universidad de MedellínLat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degreesLong: 075 36 00 W degrees minutes Long: -75.6000 decimal degreesTHUMBNAILRevista_Ingenierias_UdeM_291.pdf.jpgRevista_Ingenierias_UdeM_291.pdf.jpgIM Thumbnailimage/jpeg8497http://repository.udem.edu.co/bitstream/11407/3530/3/Revista_Ingenierias_UdeM_291.pdf.jpg99b2142abeca985926d8675f065dea98MD53ORIGINALArticulo.htmltext/html497http://repository.udem.edu.co/bitstream/11407/3530/1/Articulo.html310e8ad0758802facb42268923169f15MD51Revista_Ingenierias_UdeM_291.pdfRevista_Ingenierias_UdeM_291.pdfapplication/pdf838507http://repository.udem.edu.co/bitstream/11407/3530/2/Revista_Ingenierias_UdeM_291.pdf26875a982f8f215b5c11480a5286c3b0MD5211407/3530oai:repository.udem.edu.co:11407/35302021-05-14 14:28:59.065Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co |