DYNAMIC NEURAL NETWORK MODELING OF THERMAL ENVIRONMENTS OF TWO ADJACENT SINGLE-SPAN GREENHOUSES WITH DIFFERENT THERMAL CURTAIN POSITIONS

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dc.contributor.authorAkpenpuun, Timothy Denen
dc.contributor.authorOgunlowo, Qazeem Opeyemi
dc.contributor.authorNa, Wook-Ho
dc.contributor.authorDutta, Prabhat
dc.contributor.authorRabiu, Anis
dc.contributor.authorAdesanya, Misbaudeen Aderemi
dc.contributor.authorNariman, Mohammadreza
dc.contributor.authorZakir, Ezatullah
dc.contributor.authorKim, Hyeon-Tae
dc.contributor.authorLee, Hyun-Woo
dc.date.accessioned2024-04-17T09:00:44Z
dc.date.available2024-04-17T09:00:44Z
dc.date.issued2024
dc.description.abstractIn 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.
dc.description.sponsorshipThis study was supported by the Korea Institute of Planning, and Evaluation for Technology in Food, Agriculture, Forestry, and Fisheries (IPET) through the Agriculture, Food, and Rural Affairs Convergence Technologies Program for Educating Creative Global Leaders, funded by the Ministry of Agriculture, Food, and Rural Affairs (MAFRA) (717001-7). This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R1I1A3A01051739).
dc.identifier.otherhttps://doi.org/10.4081/jae.2024.1563
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/123456789/12107
dc.language.isoen
dc.relation.ispartofseries1563
dc.subjectneural network
dc.subjectNARX
dc.subjectmodeling
dc.subjecttime series
dc.subjectalgorithm
dc.subjectnormalization
dc.titleDYNAMIC NEURAL NETWORK MODELING OF THERMAL ENVIRONMENTS OF TWO ADJACENT SINGLE-SPAN GREENHOUSES WITH DIFFERENT THERMAL CURTAIN POSITIONS
dc.typeArticle

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