Smart frost control in greenhouses by neural networks models

Development of ARX and ANN models to predict the internal temperature of greenhouses.Temperature predictor using a MLP-ANN trained by Levenberg-Marquardt-BP algorithm.Data validation by the ANOVA method and compared with the ARX model.The frost predictor of MLP-ANN could be used for an intelligent control in greenhouses. Thermal comfort in greenhouses is a key fact to enhance productivity, due to the excess demand of energy for heating, ventilation and agroclimatic conditioning. Frost, in particular, represents a serious technological challenge if the crop sustainability is to be ensured. A Multi-Layer Perceptron artificial neural network, trained by a Levenberg-Marquardt backpropagation algorithm was designed and implemented for the smart frost control in greenhouses in the central region of Mexico, with the outside air temperature, outside air relative humidity, wind speed, global solar radiation flux, and inside air relative humidity as the input variables. The results showed a 95% confidence temperature prediction, with a coefficient of determination of 0.9549 and 0.9590, for summer and winter, respectively.

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