Journal
COMPUTERS AND ELECTRONICS IN AGRICULTURE
Volume 137, Issue -, Pages 102-114Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2017.03.024
Keywords
Modeling temperature; Greenhouses; Agroclimatic frost; Artificial neural networks models; Autoregressive models; Levenberg-Marquardt; ANOVA
Funding
- COZCyT [ZAC-2009-C01-121774]
- CONCYTEQ [QRO-2012-C01-191356]
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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. (C) 2017 Elsevier B.V. All rights reserved.
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