https://doi.org/10.4081/jae.2024.1563
Dynamic neural network modeling of thermal environments of two adjacent single-span greenhouses with different thermal curtain positions
Published: 20 February 2024
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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In order to produce marketable yield, scientific methodologies must be used to forecast the greenhouse microclimate, which is affected by the surrounding macroclimate and crop management techniques. The MATLAB tool NARX was used in this study to predict the strawberry yield, indoor air temperature, relative humidity, and vapor pressure deficit using input parameters such as indoor air temperature, relative humidity, solar radiation, indoor roof temperature, and indoor relative humidity. The data were normalized to improve the accuracy of the model, which was developed using the Levenberg–Marquardt backpropagation algorithm. The accuracy of the models was determined using various evaluation metrics, such as the coefficient of determination, mean square error, root mean square error, mean absolute deviation, and Nash–Sutcliffe efficiency coefficient. The results showed that the models had a high level of accuracy, with no significant difference between the experimental and predicted values. The VPD model was found to be the most important as it influences crop metabolic activities and its accuracy can be used as an indoor climate control parameter.
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How to Cite
Akpenpuun, T. D., Ogunlowo, Q. O., Na, W.-H., Dutta, P., Rabiu, A., Adesanya, M. A., Nariman, M., Zakir, E., Kim, H. T. and Lee, H.-W. (2024) “Dynamic neural network modeling of thermal environments of two adjacent single-span greenhouses with different thermal curtain positions”, Journal of Agricultural Engineering. doi: 10.4081/jae.2024.1563.
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