Modelo predictivo de producción agrícola colombiana utilizando redes neuronales y sistemas neuro difusos

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dc.contributor.advisorBejarano Garzón, Lilian Astrid
dc.contributor.authorGómez Camelo, Andrea Carolina
dc.contributor.orcidBejarano Garzón, Lilian Astrid [0000-0002-1381-6522]
dc.date.accessioned2025-07-30T17:31:35Z
dc.date.available2025-07-30T17:31:35Z
dc.date.created2025-07-09
dc.descriptionEl presente trabajo tiene como objetivo obtener modelos de producción agrícola colombiana utilizando redes neuronales y sistemas neuro-difusos. Se utilizaron datos históricos de la producción de seis artículos agrícolas. Se realizó interpolación de datos y normalización de los mismos para el preprocesamiento de los mismos, con los que se entrenaron los modelos que aplican técnicas de aprendizaje automático y lógica difusa. Se plantearon diferentes configuraciones de entrada-salida para los modelos, con el fin de determinar el mejor a partir del valor obtenido del cálculo del error cuadrático medio (MSE) con los datos de validación. En el caso de las redes neuronales, el mejor modelo es aquel que considera el valor anterior de salida (producción), y para sistemas neuro-difusos, el mejor modelo tanto en sistemas neuro-difusos con salida lineal como constante, es aquel que tiene una entrada asociada al año en el cual se realiza la medición de la producción. Esta investigación provee una base para la planificación agrícola, de la mano del uso de la inteligencia artificial como herramienta tecnológica vigente y relevante.
dc.description.abstractThis work aims to get predictiv models of colombian agriculutral prediction use artificial neural networks and neuro fuzzy systems. Historical data were used for the production of six agricultural commodities. Data imputation and normalization were performed for their preprocessing, with which the models that use learning techniques and fuzzy logic were trained. Different input-output configurations were proposed for the models, in order to determine the best one from the value obtained from the calculation of the mean square error (MSE) for validation data. For neural networks, the best model is the one that considers the previous output value (production), and for neuro-fuzzy systems, the best model, both linear and constant, is the one that has an input associated with the year in which the production measurement is made. This research provides a basis for agricultural planning, hand in hand with the use of artificial intelligence as a current and relevant technological tool.
dc.format.mimetypepdf
dc.identifier.urihttp://hdl.handle.net/11349/98332
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dc.rights.accesoAbierto (Texto Completo)
dc.rights.accessrightsOpenAccess
dc.subjectProducción Agrícola
dc.subjectRedes neuronales
dc.subjectSistemas neuro difusos
dc.subjectProducción agrícola
dc.subject.keywordAgricultural production
dc.subject.keywordArtificial neural networks
dc.subject.keywordNeuro fuzzy systems
dc.subject.keywordAgricultural production
dc.subject.lembIngeniería de Sistemas -- Tesis y disertaciones académicas
dc.subject.lembInteligencia artificial
dc.subject.lembRedes neurales (Computadores)
dc.subject.lembLógica difusa
dc.titleModelo predictivo de producción agrícola colombiana utilizando redes neuronales y sistemas neuro difusos
dc.title.titleenglishPredictive model of colombian agricultural production using neural networks and neuro-fuzzy systems
dc.typebachelorThesis
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1f
dc.type.degreeMonografía

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