Método de fusión de datos aplicado a redes inalámbricas de sensores para apoyar la toma de decisiones en la gestión de cultivos de palma de aceite

Dado que la agricultura es la actividad humana más dependiente de las condiciones climáticas, es vital que los agricultores tomen decisiones bien informadas. Desafortunadamente en Colombia, los agricultores generalmente tienden a decidir sobre una base de conocimiento limitada y esto somete sus sist...

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Autores:
Culman Forero, María Alejandra
Tipo de recurso:
Fecha de publicación:
2018
Institución:
Universidad Autónoma de Bucaramanga - UNAB
Repositorio:
Repositorio UNAB
Idioma:
spa
OAI Identifier:
oai:repository.unab.edu.co:20.500.12749/3549
Acceso en línea:
http://hdl.handle.net/20.500.12749/3549
Palabra clave:
Systems Engineering
Telematics
Wireless communication systems
Wireless technology
Electronic data processing
Investigations
Analysis
Decision support
Agriculture
Data fusion
Oil palm
Crop management
Wireless sensor networks
Ingeniería de sistemas
Telemática
Sistemas de comunicación inalámbrica
Tecnología inalámbrica
Procesamiento electrónico de datos
Investigaciones
Análisis
Soporte a la decisión
Fusión de datos
Agrometeorología
Palma de aceite
Gestión del cultivo
Redes Inalámbricas de sensores
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openAccess
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http://creativecommons.org/licenses/by-nc-nd/2.5/co/
id UNAB2_626a0c122ea74223418e07b266d37122
oai_identifier_str oai:repository.unab.edu.co:20.500.12749/3549
network_acronym_str UNAB2
network_name_str Repositorio UNAB
repository_id_str
dc.title.spa.fl_str_mv Método de fusión de datos aplicado a redes inalámbricas de sensores para apoyar la toma de decisiones en la gestión de cultivos de palma de aceite
dc.title.translated.eng.fl_str_mv Data fusion method applied to wireless sensor networks to support decision-making in the management of oil palm crops
title Método de fusión de datos aplicado a redes inalámbricas de sensores para apoyar la toma de decisiones en la gestión de cultivos de palma de aceite
spellingShingle Método de fusión de datos aplicado a redes inalámbricas de sensores para apoyar la toma de decisiones en la gestión de cultivos de palma de aceite
Systems Engineering
Telematics
Wireless communication systems
Wireless technology
Electronic data processing
Investigations
Analysis
Decision support
Agriculture
Data fusion
Oil palm
Crop management
Wireless sensor networks
Ingeniería de sistemas
Telemática
Sistemas de comunicación inalámbrica
Tecnología inalámbrica
Procesamiento electrónico de datos
Investigaciones
Análisis
Soporte a la decisión
Fusión de datos
Agrometeorología
Palma de aceite
Gestión del cultivo
Redes Inalámbricas de sensores
title_short Método de fusión de datos aplicado a redes inalámbricas de sensores para apoyar la toma de decisiones en la gestión de cultivos de palma de aceite
title_full Método de fusión de datos aplicado a redes inalámbricas de sensores para apoyar la toma de decisiones en la gestión de cultivos de palma de aceite
title_fullStr Método de fusión de datos aplicado a redes inalámbricas de sensores para apoyar la toma de decisiones en la gestión de cultivos de palma de aceite
title_full_unstemmed Método de fusión de datos aplicado a redes inalámbricas de sensores para apoyar la toma de decisiones en la gestión de cultivos de palma de aceite
title_sort Método de fusión de datos aplicado a redes inalámbricas de sensores para apoyar la toma de decisiones en la gestión de cultivos de palma de aceite
dc.creator.fl_str_mv Culman Forero, María Alejandra
dc.contributor.advisor.spa.fl_str_mv De Farías, Claudio Miceli
Talavera Portocarrero, Jesús Martín
Cabrera Cruz, José Daniel
Bayona Rodríguez, Cristihian Jarri
dc.contributor.author.spa.fl_str_mv Culman Forero, María Alejandra
dc.contributor.cvlac.*.fl_str_mv https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000069035
dc.contributor.cvlac.none.fl_str_mv Cabrera Cruz, José Daniel [0000069035]
dc.contributor.googlescholar.*.fl_str_mv https://scholar.google.es/citations?hl=es#user=hses_w0AAAAJ
dc.contributor.googlescholar.none.fl_str_mv Cabrera Cruz, José Daniel [0000069035]
dc.contributor.orcid.*.fl_str_mv https://orcid.org/0000-0002-1815-5057
dc.contributor.orcid.none.fl_str_mv Cabrera Cruz, José Daniel [0000-0002-1815-5057]
dc.contributor.researchgate.*.fl_str_mv https://www.researchgate.net/profile/Jose_Cabrera_Cruz
dc.contributor.researchgate.none.fl_str_mv Cabrera Cruz, José Daniel [Jose_Cabrera_Cruz]
dc.contributor.researchgroup.spa.fl_str_mv Grupo de Investigación Pensamiento Sistémico - GPS
Grupo de Investigaciones Clínicas
dc.contributor.apolounab.none.fl_str_mv
dc.contributor.linkedin.none.fl_str_mv Cabrera Cruz, José Daniel [josé-daniel-cabrera-cruz-23900b10]
dc.subject.keywords.eng.fl_str_mv Systems Engineering
Telematics
Wireless communication systems
Wireless technology
Electronic data processing
Investigations
Analysis
Decision support
Agriculture
Data fusion
Oil palm
Crop management
Wireless sensor networks
topic Systems Engineering
Telematics
Wireless communication systems
Wireless technology
Electronic data processing
Investigations
Analysis
Decision support
Agriculture
Data fusion
Oil palm
Crop management
Wireless sensor networks
Ingeniería de sistemas
Telemática
Sistemas de comunicación inalámbrica
Tecnología inalámbrica
Procesamiento electrónico de datos
Investigaciones
Análisis
Soporte a la decisión
Fusión de datos
Agrometeorología
Palma de aceite
Gestión del cultivo
Redes Inalámbricas de sensores
dc.subject.lemb.spa.fl_str_mv Ingeniería de sistemas
Telemática
Sistemas de comunicación inalámbrica
Tecnología inalámbrica
Procesamiento electrónico de datos
Investigaciones
Análisis
dc.subject.proposal.spa.fl_str_mv Soporte a la decisión
Fusión de datos
Agrometeorología
Palma de aceite
Gestión del cultivo
Redes Inalámbricas de sensores
description Dado que la agricultura es la actividad humana más dependiente de las condiciones climáticas, es vital que los agricultores tomen decisiones bien informadas. Desafortunadamente en Colombia, los agricultores generalmente tienden a decidir sobre una base de conocimiento limitada y esto somete sus sistemas productivos a la incertidumbre generada por la variabilidad y el cambio climático. Las causas de este problema se pueden resumir en tres situaciones: los agricultores no tienen acceso a información agrometeorológica y a previsiones agroclimáticas a nivel local; los agricultores no tienen la competencia para tomar decisiones basadas en la información; y los agricultores no tienen el recurso económico para respaldar sus decisiones. Este Trabajo de investigación se centra en atender la segunda causa, respecto a llevar la información agrometeorológica a información accionable para apoyar la toma de decisiones en la gestión del cultivo de palma de aceite. Suponiendo un escenario agrícola donde está desplegada una Red Inalámbrica de Sensores para adquirir datos locales y representativos en el campo, se formuló un método de Fusión de Datos que apoya la gestión del riego al inferir el estado del cultivo y decidir sobre la necesidad de riego. El método compromete dos niveles, un primer nivel de decisión que combina datos de la humedad del suelo, la temperatura ambiente y la humedad relativa para decidir sí regar o no regar el lote de cultivo mediante la técnica de Inferencia Dempster–Shafer; y un segundo nivel de evaluación a la decisión que combina datos de la evapotranspiración de cultivo, la precipitación y la decisión de riego en el lote de cultivo para calificar el desempeño de la decisión en el contexto de la plantación mediante la técnica de Lógica Difusa. El impacto del método en la gestión del cultivo de palma de aceite fue establecido por medio de la simulación de dos escenarios: lote de cultivo con riego gestionado por el primer nivel del método, y lote de cultivo sin riego. Los resultados indican un impacto potencial de incrementar en un 27% el rendimiento del cultivo, gracias a las decisiones de riego tomadas por el método.
publishDate 2018
dc.date.issued.none.fl_str_mv 2018-03
dc.date.accessioned.none.fl_str_mv 2020-06-26T21:35:50Z
dc.date.available.none.fl_str_mv 2020-06-26T21:35:50Z
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.local.spa.fl_str_mv Tesis
dc.type.redcol.none.fl_str_mv http://purl.org/redcol/resource_type/TM
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12749/3549
dc.identifier.instname.spa.fl_str_mv instname:Universidad Autónoma de Bucaramanga - UNAB
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional UNAB
url http://hdl.handle.net/20.500.12749/3549
identifier_str_mv instname:Universidad Autónoma de Bucaramanga - UNAB
reponame:Repositorio Institucional UNAB
dc.language.iso.spa.fl_str_mv spa
language spa
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spelling De Farías, Claudio Micelid9f17db2-60c4-4099-a033-577e500b7d98-1Talavera Portocarrero, Jesús Martínf210e4ef-3f25-4517-8c74-c0d4c40188f9-1Cabrera Cruz, José Daniel15e242b3-32d0-4e32-95f6-2b6ca1abd623-1Bayona Rodríguez, Cristihian Jarrib4061dbc-3fb5-4060-87f6-24b1c0664efd-1Culman Forero, María Alejandrac9cce20b-c130-44cc-8d52-a1f27c7b1c5a-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000069035Cabrera Cruz, José Daniel [0000069035]https://scholar.google.es/citations?hl=es#user=hses_w0AAAAJCabrera Cruz, José Daniel [0000069035]https://orcid.org/0000-0002-1815-5057Cabrera Cruz, José Daniel [0000-0002-1815-5057]https://www.researchgate.net/profile/Jose_Cabrera_CruzCabrera Cruz, José Daniel [Jose_Cabrera_Cruz]Grupo de Investigación Pensamiento Sistémico - GPSGrupo de Investigaciones ClínicasCabrera Cruz, José Daniel [josé-daniel-cabrera-cruz-23900b10]2020-06-26T21:35:50Z2020-06-26T21:35:50Z2018-03http://hdl.handle.net/20.500.12749/3549instname:Universidad Autónoma de Bucaramanga - UNABreponame:Repositorio Institucional UNABDado que la agricultura es la actividad humana más dependiente de las condiciones climáticas, es vital que los agricultores tomen decisiones bien informadas. Desafortunadamente en Colombia, los agricultores generalmente tienden a decidir sobre una base de conocimiento limitada y esto somete sus sistemas productivos a la incertidumbre generada por la variabilidad y el cambio climático. Las causas de este problema se pueden resumir en tres situaciones: los agricultores no tienen acceso a información agrometeorológica y a previsiones agroclimáticas a nivel local; los agricultores no tienen la competencia para tomar decisiones basadas en la información; y los agricultores no tienen el recurso económico para respaldar sus decisiones. Este Trabajo de investigación se centra en atender la segunda causa, respecto a llevar la información agrometeorológica a información accionable para apoyar la toma de decisiones en la gestión del cultivo de palma de aceite. Suponiendo un escenario agrícola donde está desplegada una Red Inalámbrica de Sensores para adquirir datos locales y representativos en el campo, se formuló un método de Fusión de Datos que apoya la gestión del riego al inferir el estado del cultivo y decidir sobre la necesidad de riego. El método compromete dos niveles, un primer nivel de decisión que combina datos de la humedad del suelo, la temperatura ambiente y la humedad relativa para decidir sí regar o no regar el lote de cultivo mediante la técnica de Inferencia Dempster–Shafer; y un segundo nivel de evaluación a la decisión que combina datos de la evapotranspiración de cultivo, la precipitación y la decisión de riego en el lote de cultivo para calificar el desempeño de la decisión en el contexto de la plantación mediante la técnica de Lógica Difusa. El impacto del método en la gestión del cultivo de palma de aceite fue establecido por medio de la simulación de dos escenarios: lote de cultivo con riego gestionado por el primer nivel del método, y lote de cultivo sin riego. Los resultados indican un impacto potencial de incrementar en un 27% el rendimiento del cultivo, gracias a las decisiones de riego tomadas por el método.INTRODUCCIÓN 24 1. DESCRIPCIÓN DEL TRABAJO 28 1.1 PROBLEMA 28 1.2 PREGUNTA DE INVESTIGACIÓN 31 1.3 MOTIVACIÓN 31 1.4 HIPÓTESIS 32 1.5 JUSTIFICACIÓN 33 2. OBJETIVOS 36 2.1 GENERAL 36 2.2 ESPECÍFICOS 36 3. MARCO REFERENCIAL 37 3.1 MARCO CONCEPTUAL 37 3.1.1 Fusión de Datos 37 3.1.2 Método a partir de la Fusión de Datos basado en la Inferencia 39 3.1.3 Redes Inalámbricas de Sensores 40 3.1.4 Telemática 40 3.1.5 Agrometeorología 40 3.1.6 Naturaleza de los datos agrometeorológicos 41 3.1.7 Gestión del cultivo de palma de aceite 42 3.2 MARCO TEÓRICO 47 3.2.1 Fusión de datos aplicada a sensores 47 3.2.2 Técnicas de Fusión de Datos basadas en la Inferencia 49 3.2.3 Agrometeorología y Redes Inalámbricas de Sensores 54 3.2.4 Agricultura ante los cambios tecnológicos y climáticos 56 3.3 ESTADO DEL ARTE 59 3.3.1 Soluciones que integran Redes Inalámbricas de Sensores y Fusión de Datos para apoyar la toma de decisiones en la agricultura 60 3.3.2 Soluciones que integran Redes Inalámbricas de Sensores y otras áreas para apoyar la toma de decisiones en la agricultura 64 3.3.3 Síntesis sobre las soluciones reportadas y la toma de decisiones en la agricultura 68 3.4 MARCO CONTEXTUAL Y ANTECEDENTES 70 3.4.1 La palma de aceite en Colombia 70 3.4.2 Corporación Centro de Investigación en Palma de Aceite – CENIPALMA 71 3.4.3 Iniciativas en el territorio colombiano para una agricultura climáticamente inteligente 72 3.4.4 Red Temática CYTED RiegoNets 74 3.4.5 Centro de Excelencia y Apropiación en Internet de las Cosas – CEA–IoT 75 3.5 MARCO LEGAL Y NORMATIVO 77 4. ASPECTOS METODOLÓGICOS 78 4.1 ENFOQUE Y TIPO DE INVESTIGACIÓN 78 4.2 UNIVERSO Y MUESTRA 79 4.2.1 Universo y muestra para recolección de información 79 4.2.2 Universo con potencial de ser impacto por contribuciones 80 4.3 TÉCNICAS E INSTRUMENTOS DE RECOLECCIÓN DE DATOS 80 4.4 ACTIVIDADES REALIZADAS 80 4.4.1 Fase 1: análisis de técnicas de Fusión de Datos y oportunidades de investigación 81 4.4.2 Fase 2: propuesta de un método, utilizando una técnica de Fusión de Datos basada en la Inferencia 83 4.4.3 Fase 3: comparación del comportamiento agronómico de parcelas de palma de aceite 85 5. FUSIÓN DE DATOS APLICADA A REDES INALÁMBRICAS DE SENSORES PARA APOYAR LA TOMA DE DECISIONES EN AGRICULTURA: TÉCNICAS Y OPORTUNIDADES DE INVESTIGACIÓN 87 5.1 REVISIÓN DE LA LITERATURA 87 5.1.1 Preguntas de investigación 88 5.1.2 Proceso de búsqueda 89 5.1.3 Criterios de exclusión, inclusión y calidad 91 5.1.4 Extracción de datos 93 5.1.5 Resultados de la búsqueda 94 5.2 TÉCNICAS DE FUSIÓN DE DATOS APLICADAS A INFORMACIÓN RECOLECTADA POR REDES INALÁMBRICAS DE SENSORES PARA APOYAR LA TOMA DE DECISIONES EN AGRICULTURA 97 5.3 OPORTUNIDADES DE INVESTIGACIÓN EN REDES INALÁMBRICAS DE SENSORES Y FUSIÓN DE DATOS PARA APOYAR LA TOMA DE DECISIONES EN AGRICULTURA 112 5.3.1 Problemas abiertos en Redes Inalámbricas de Sensores y Fusión de Datos 112 5.3.2 Oportunidades de investigación en Redes Inalámbricas de Sensores y Fusión de Datos 114 6. MÉTODO DE FUSIÓN DE DATOS APLICADO A REDES INALÁMBRICAS DE SENSORES PARA APOYAR LA TOMA DE DECISIONES EN LA GESTIÓN DE CULTIVOS DE PALMA DE ACEITE 118 6.1 SOBRE LA PALMA DE ACEITE 118 6.1.1 Relación entre el suelo, el cultivo y el clima en la productividad de la palma de aceite 119 6.1.2 Información agrometeorológica disponible en palma de aceite en Colombia 125 6.1.3 Decisiones para apoyar la gestión del cultivo de palma de aceite 130 6.2 MÉTODO DE FUSIÓN DE DATOS AGROMETEOROLÓGICOS 148 6.2.1 Fusión de datos a nivel de lote 149 6.2.2 Fusión de datos a nivel de plantación 164 6.3 VALIDACIÓN DEL MÉTODO DE FUSIÓN DE DATOS AGROMETEOROLÓGICOS 180 6.3.1 Simulación de la fusión de datos a nivel de lote 180 6.3.2 Simulación de la fusión de datos a nivel de plantación 193 7. COMPORTAMIENTO AGRONÓMICO DE PARCELAS PARA MEDIR EL IMPACTO DEL MÉTODO DE FUSIÓN DE DATOS EN LA GESTIÓN DEL RIEGO EN CULTIVOS DE PALMA DE ACEITE 203 7.1 SIMULACIÓN DEL COMPORTAMIENTO AGRONÓMICO DEL LOTE DE ESTUDIO 203 7.2 COMPORTAMIENTO AGRONÓMICO DEL LOTE DE ESTUDIO BAJO DOS ESCENARIOS: CON RIEGO Y SIN RIEGO 212 7.2.1 Resultados de la simulación del comportamiento agronómico del lote de estudio 212 7.2.2 Impacto del método de inferencia en la productividad e ingresos del cultivo de palma de aceite 219 8. CONCLUSIONES 222 8.1 CONCLUSIONES 222 8.2 CONTRIBUCIONES 225 8.3 TRABAJO FUTURO 228 REFERENCIAS 231 ANEXOS 287MaestríaSince agriculture is the human activity most dependent on climatic conditions, it is vital that farmers make informed decisions. Unfortunately, in Colombia, farmers tend to decide on a limited knowledge base, and this subjects their production systems to the uncertainty generated by climate variability and change. The causes of this problem can be summarized in three situations: farmers do not have access to agrometeorological information and agroclimatic forecasts at the local level, farmers do not have the competence to make decisions based on the information, and farmers do not have the economic resource to back their decisions. This research work focuses on addressing the second cause, about bringing the agrometeorological information to actionable information to support decision making in the management of oil palm cultivation. Assuming an agricultural scenario where a Wireless Sensor Network is deployed to acquire local and representative data in the field, a Data Fusion method was formulated that supports irrigation management by inferring the state of the crop and deciding on the need for irrigation. The method involves two levels, the first level of decision that combines data on soil moisture, ambient temperature, and relative humidity to decide whether to water or not irrigate the crop plot using the Dempster-Shafer Inference technique. And the second level of evaluation to the decision, which combines data of the crop evapotranspiration, precipitation and the decision of irrigation in the crop plot to qualify the performance of the decision in the context of the plantation using the Fuzzy Logic technique. The impact of the method in the management of oil palm cultivation was established through the simulation of two scenarios: crop plot with irrigation managed by the first level of the method, and crop plot without irrigation. The results indicate a potential impact of increasing crop yield by 27%, thanks to the irrigation decisions made by the method.Modalidad Presencialapplication/pdfspahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial-SinDerivadas 2.5 ColombiaMétodo de fusión de datos aplicado a redes inalámbricas de sensores para apoyar la toma de decisiones en la gestión de cultivos de palma de aceiteData fusion method applied to wireless sensor networks to support decision-making in the management of oil palm cropsMagíster en TelemáticaBucaramanga (Colombia)UNAB Campus BucaramangaUniversidad Autónoma de Bucaramanga UNABFacultad IngenieríaMaestría en Telemáticainfo:eu-repo/semantics/masterThesisTesishttp://purl.org/redcol/resource_type/TMSystems EngineeringTelematicsWireless communication systemsWireless technologyElectronic data processingInvestigationsAnalysisDecision supportAgricultureData fusionOil palmCrop managementWireless sensor networksIngeniería de sistemasTelemáticaSistemas de comunicación inalámbricaTecnología inalámbricaProcesamiento electrónico de datosInvestigacionesAnálisisSoporte a la decisiónFusión de datosAgrometeorologíaPalma de aceiteGestión del cultivoRedes Inalámbricas de sensoresCulman Forero, María Alejandra (2018). Método de fusión de datos aplicado a redes inalámbricas para apoyar la toma de decisiones en la gestión de cultivos de palma de aceite. Bucaramanga (Colombia) : Universidad Autónoma de Bucaramanga UNABAbdelgawad, A., & Bayoumi, M. (2012). Data Fusion in WSN. In Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks (Volume 118, pp. 17–35). Boston, MA: Springer US. https://doi.org/10.1007/978-1-4614-1350-9_2Abouzar, P., Michelson, D. G., & Hamdi, M. (2016). RSSI-Based Distributed Self-Localization for Wireless Sensor Networks Used in Precision Agriculture. IEEE Transactions on Wireless Communications, 15(10), 6638–6650. https://doi.org/10.1109/TWC.2016.2586844Abu Bakar, R., Darus, S. Z., Kulaseharan, S., & Jamaluddin, N. (2011). Effects of ten year application of empty fruit bunches in an oil palm plantation on soil chemical properties. Nutrient Cycling in Agroecosystems, 89(3), 341–349. https://doi.org/10.1007/s10705-010-9398-9ACM. (2012). 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H., Abdul-Salaam, G., & Abdullah, A. H. (2015). A survey of wireless sensor network approaches and their energy consumption for monitoring farm fields in precision agriculture. Precision Agriculture, 16(2), 216–238. https://doi.org/10.1007/s11119-014-9371-8APSIM Initiative. (n.d.-a). APSIM: about us. Retrieved January 20, 2018, from http://www.apsim.info/AboutUs.aspxAPSIM Initiative. (n.d.-b). Creating an APSIM met file using Excel. Retrieved January 18, 2018, from https://www.apsim.info/Documentation/CommonTasksinAPSIM/CreatinganAPSIMmetfileusingExcel.aspxAPSIM Initiative. (n.d.-c). What is the Operations Schedule Module? Retrieved January 18, 2018, from https://www.apsim.info/Documentation/Model,CropandSoil/InfrastuctureandManagementDocumentation/OPERATIONS.aspxAquino, G., Pirmez, L., Farias, C. M. de, Delicato, F. C., & Pires, P. F. (2016). Hephaestus: A multisensor data fusion algorithm for multiple applications on wireless sensor networks. 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