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      Redes bayesianas aplicadas a un modelo CFD del entorno de un cultivo en invernadero Translated title: Bayesian networks applied in a CFD model of the crop in greenhouse

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          Abstract

          Los avances en sistemas y recursos informáticos permiten desarrollar modelos para simular el comportamiento de los fluidos en invernaderos. Sin embargo, la predicción de los gradientes de masa y energía, en los invernaderos con el cultivo y ventilación natural, es difícil por la naturaleza estocástica del viento y las relaciones de dependencia entre la temperatura, CO2 y humedad relativa. Existen técnicas heurísticas, como las Redes Bayesianas, que ayudan a conocer las relaciones entre las variables que no pueden determinarse con herramientas estadísticas. El objetivo del presente estudio fue determinar la temperatura, concentración de CO2 y humedad relativa con respecto a la altura del cultivo, en un invernadero con ventilación natural, mediante Redes Bayesianas aplicadas a un modelo de Dinámica de Fluidos Computacional. La Red Bayesiana permitió determinar los espacios del invernadero con condiciones ambientales adversas para el desarrollo del cultivo y los estados climáticos más probables, a partir de las relaciones entre las variables estudiadas.

          Translated abstract

          The advances in computer systems and resources make it possible to develop models to simulate the behavior of the fluids in greenhouses. However, the prediction of the gradients of mass and energy in the greenhouses with the crop and natural ventilation is difficult due to the stochastic nature of the wind and the relationships of dependence among temperature, CO2 and relative humidity. There are heuristic techniques, such as the Bayesian Networks, which help to know the relationships among the variables that cannot be determined with statistical tools. The objective of the present study was to determine temperature, CO2 concentration and relative humidity with respect to crop height, in a greenhouse with natural ventilation, by means of Bayesian Networks applied to a model of Computational Fluid Dynamic. The Bayesian Network made it possible to determine the spaces of the greenhouse with adverse environmental conditions for the crop development and the most probable climatic states, from the relationships among the variables studied.

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          Learning Bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood

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            Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process

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              Effect of ventilator configuration on the distributed climate of greenhouses: A review of experimental and CFD studies

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                Author and article information

                Contributors
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Journal
                agro
                Agrociencia
                Agrociencia
                Colegio de Postgraduados (México )
                1405-3195
                May 2014
                : 48
                : 3
                : 307-319
                Affiliations
                [1 ] Universidad Tecnológica de Corregidora México
                [2 ] Universidad Autónoma de Querétaro México
                Article
                S1405-31952014000300006
                f25cf7ed-e890-463d-b1a1-6a338426c367

                http://creativecommons.org/licenses/by/4.0/

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                Categories
                Agriculture, Multidisciplinary

                General agriculture
                CFD,air flow,greenhouse,Solanum lycopersicum,natural ventilation,flujo de aire,invernadero,ventilación natural

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