Forecasting the vapor pressure deficit in vertical farming facilities aiming to provide optimal indoor conditions
Vertical farming is a sustainable solution for urban agriculture by optimizing space and resources. However, this requires ideal indoor climatic conditions to achieve maximum crop yield and quality. This research develops and validates a prediction model based on NeuralProphet algorithm to assess t...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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PAGEPress Publications
2025-07-01
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| Series: | Journal of Agricultural Engineering |
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| Online Access: | https://www.agroengineering.org/jae/article/view/1793 |
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| _version_ | 1849249374907924480 |
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| author | Carlos Alejandro Perez Garcia Dafni Despoina Avgoustaki Enrica Santolini Daniele Torreggiani Thomas Bartzanas Patrizia Tassinari Marco Bovo |
| author_facet | Carlos Alejandro Perez Garcia Dafni Despoina Avgoustaki Enrica Santolini Daniele Torreggiani Thomas Bartzanas Patrizia Tassinari Marco Bovo |
| author_sort | Carlos Alejandro Perez Garcia |
| collection | DOAJ |
| description |
Vertical farming is a sustainable solution for urban agriculture by optimizing space and resources. However, this requires ideal indoor climatic conditions to achieve maximum crop yield and quality. This research develops and validates a prediction model based on NeuralProphet algorithm to assess the vapor pressure deficit in a vertical farming facility. The model uses environmental data such as temperature, relative humidity, and solar radiation to predict vapor pressure deficit (VPD), a key indicator of vegetation health and crop growth status. The model shows high accuracy and reliability with a root mean squared error (RMSE) of 34.80 and a mean absolute error (MAE) of 25.28. The model, demonstrating satisfactory performance in predicting VPD, enables optimization of indoor growth conditions, thereby improving resources use efficiency and minimizing operational costs. Finally, it indicates a promising application of advanced artificial intelligence tools in vertical farming management to establish a sustainable and economically feasible agricultural practice since the model can help to produce high quality crops through a precise control of environmental parameters.
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| format | Article |
| id | doaj-art-1b26e8bc9cb74992a7dc9edc3255a1f6 |
| institution | Kabale University |
| issn | 1974-7071 2239-6268 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | PAGEPress Publications |
| record_format | Article |
| series | Journal of Agricultural Engineering |
| spelling | doaj-art-1b26e8bc9cb74992a7dc9edc3255a1f62025-08-20T03:57:36ZengPAGEPress PublicationsJournal of Agricultural Engineering1974-70712239-62682025-07-0110.4081/jae.2025.1793Forecasting the vapor pressure deficit in vertical farming facilities aiming to provide optimal indoor conditionsCarlos Alejandro Perez Garcia0Dafni Despoina Avgoustaki1Enrica Santolini2Daniele Torreggiani3Thomas Bartzanas4Patrizia Tassinari5Marco Bovo6https://orcid.org/0000-0001-5757-8514Department of Agricultural and Food Sciences, University of BolognaDepartment of Natural Resources Management and Agricultural Engineering, Agricultural University of AthensDepartment of Agricultural and Food Sciences, University of BolognaDepartment of Agricultural and Food Sciences, University of BolognaDepartment of Natural Resources Management and Agricultural Engineering, Agricultural University of AthensDepartment of Agricultural and Food Sciences, University of BolognaDepartment of Agricultural and Food Sciences, University of Bologna Vertical farming is a sustainable solution for urban agriculture by optimizing space and resources. However, this requires ideal indoor climatic conditions to achieve maximum crop yield and quality. This research develops and validates a prediction model based on NeuralProphet algorithm to assess the vapor pressure deficit in a vertical farming facility. The model uses environmental data such as temperature, relative humidity, and solar radiation to predict vapor pressure deficit (VPD), a key indicator of vegetation health and crop growth status. The model shows high accuracy and reliability with a root mean squared error (RMSE) of 34.80 and a mean absolute error (MAE) of 25.28. The model, demonstrating satisfactory performance in predicting VPD, enables optimization of indoor growth conditions, thereby improving resources use efficiency and minimizing operational costs. Finally, it indicates a promising application of advanced artificial intelligence tools in vertical farming management to establish a sustainable and economically feasible agricultural practice since the model can help to produce high quality crops through a precise control of environmental parameters. https://www.agroengineering.org/jae/article/view/1793microclimate forecastingindoor agricultureNeuralProphetvertical farmingvapor pressure deficit |
| spellingShingle | Carlos Alejandro Perez Garcia Dafni Despoina Avgoustaki Enrica Santolini Daniele Torreggiani Thomas Bartzanas Patrizia Tassinari Marco Bovo Forecasting the vapor pressure deficit in vertical farming facilities aiming to provide optimal indoor conditions Journal of Agricultural Engineering microclimate forecasting indoor agriculture NeuralProphet vertical farming vapor pressure deficit |
| title | Forecasting the vapor pressure deficit in vertical farming facilities aiming to provide optimal indoor conditions |
| title_full | Forecasting the vapor pressure deficit in vertical farming facilities aiming to provide optimal indoor conditions |
| title_fullStr | Forecasting the vapor pressure deficit in vertical farming facilities aiming to provide optimal indoor conditions |
| title_full_unstemmed | Forecasting the vapor pressure deficit in vertical farming facilities aiming to provide optimal indoor conditions |
| title_short | Forecasting the vapor pressure deficit in vertical farming facilities aiming to provide optimal indoor conditions |
| title_sort | forecasting the vapor pressure deficit in vertical farming facilities aiming to provide optimal indoor conditions |
| topic | microclimate forecasting indoor agriculture NeuralProphet vertical farming vapor pressure deficit |
| url | https://www.agroengineering.org/jae/article/view/1793 |
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