Original Articles
25 July 2025
Early Access
Forecasting the vapor pressure deficit in vertical farming facilities aiming to provide optimal indoor conditions
Publisher's note
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.
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.
125
Views
11
Downloads
Authors
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.
Altmetrics
Downloads
Download data is not yet available.
Citations
Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56. Available from: https://www.fao.org/4/x0490e/x0490e00.htm
Avgoustaki, D.D., Vatsika, G., Giakoumatos, A., Bartzanas, T., 2024. How different daily light integrals and spectral treatments influence the development of Valerianella locusta plants grown in an indoor vertical farm. Sci. Hortic. 332:113044. DOI: https://doi.org/10.1016/j.scienta.2024.113044
Avgoustaki, D.D., Xydis, G., 2020. Plant factories in the water-food-energy Nexus era: a systematic bibliographical review. Food Secur. 12:253-268. DOI: https://doi.org/10.1007/s12571-019-01003-z
Castañeda-Miranda, A., Castaño, V.M., 2017. Smart frost control in greenhouses by neural networks models. Comput. Electron. Agr. 137:102-114. DOI: https://doi.org/10.1016/j.compag.2017.03.024
Castañeda-Miranda, A., Castaño-Meneses, V.M., 2020. Internet of things for smart farming and frost intelligent control in greenhouses. Comput. Electron. Agr. 176:105614. DOI: https://doi.org/10.1016/j.compag.2020.105614
De Coninck, R., Magnusson, F., Åkesson, J., Helsen, L., 2016. Toolbox for development and validation of grey-box building models for forecasting and control. J Build. Perform. Simu. 9:288-303. DOI: https://doi.org/10.1080/19401493.2015.1046933
Eldridge, B.M., Manzoni, L.R., Graham, C.A., Rodgers, B., Farmer, J.R., Dodd, A.N., 2020. Getting to the roots of aeroponic indoor farming. New Phytol. 228:1183-1192. DOI: https://doi.org/10.1111/nph.16780
Eraliev, O., Lee, C.-H., 2023. Performance analysis of time series deep learning models for climate prediction in indoor hydroponic greenhouses at different time intervals. Plants 12:2316. DOI: https://doi.org/10.3390/plants12122316
Ferracuti, F., Fonti, A., Ciabattoni, L., Pizzuti, S., Arteconi, A., Helsen, L., Comodi, G., 2017. Data-driven models for short-term thermal behavior prediction in real buildings. Appl. Energ. 204:1375-1387. DOI: https://doi.org/10.1016/j.apenergy.2017.05.015
Frausto, H.U., Pieters, J.G., Deltour, J.M., 2003. Modelling greenhouse temperature by means of auto regressive models. Biosyst. Eng. 84:147-157. DOI: https://doi.org/10.1016/S1537-5110(02)00239-8
Guillén-Navarro, M.A., Martínez-España, R., López, B., Cecilia, J.M., 2021. A high-performance IoT solution to reduce frost damages in stone fruits. Concurr. Comp.-Pract. E. 33:e5299. DOI: https://doi.org/10.1002/cpe.5299
Hauge Broholt, T., Dahl Knudsen, M., Petersen, S., 2022. The robustness of black and grey-box models of thermal building behaviour against weather changes. Energ. Buildings 275:112460. DOI: https://doi.org/10.1016/j.enbuild.2022.112460
Li, X., Zhang, X., Wang, Y., Zhang, K., Chen, Y., 2020. Temperature prediction model for solar greenhouse based on improved BP neural network. J. Phys. Conf. Ser. 1639:012036. DOI: https://doi.org/10.1088/1742-6596/1639/1/012036
Morales-García, J., Bueno-Crespo, A., Martínez-España, R., Cecilia, J.M., 2023. Data-driven evaluation of machine learning models for climate control in operational smart greenhouses. J. Amb. Intell. Smart En. 15:3-17. DOI: https://doi.org/10.3233/AIS-220441
Oh, S., Lu, C., 2023. Vertical farming - smart urban agriculture for enhancing resilience and sustainability in food security. J. Hortic. Sci. Biotech. 98:133–140. DOI: https://doi.org/10.1080/14620316.2022.2141666
Oke, T.R., 2002. Boundary layer climates. Abingdon, Routledge. DOI: https://doi.org/10.4324/9780203407219
Patil, S.L., Tantau, H.J., Salokhe, V.M., 2008. Modelling of tropical greenhouse temperature by auto regressive and neural network models. Biosyst. Eng. 99:423-431. DOI: https://doi.org/10.1016/j.biosystemseng.2007.11.009
Revathi, S., Radhakrishnan, T.K., Sivakumaran, N., 2017. Climate control in greenhouse using intelligent control algorithms. Proc. American Control Conference (ACC), Seattle; pp. 887-892. DOI: https://doi.org/10.23919/ACC.2017.7963065
Shamshiri, R., Kalantari, F., Ting, C.K., Thorp, K.R., Hameed, I.A, Weltzien, C., et al., 2018. Advances in greenhouse automation and controlled environment agriculture: A transition to plant factories and urban agriculture. Int. J. Agric. Biol. Eng. 11:1-22. DOI: https://doi.org/10.25165/j.ijabe.20181101.3210
Singh, M.C., Singh, J.P., Singh, K.G., 2018. Development of mathematical models for predicting vapour pressure deficit inside a greenhouse from internal and external climate. J. Agrometeorol. 20:238-241. DOI: https://doi.org/10.54386/jam.v20i3.552
Torto, S.O G., Pachauri, R.K., Singh, J.G., 2024. Neural Prophet driven day-ahead forecast of global horizontal irradiance for efficient micro-grid management. E-Prime – Adv. Electr. Eng. Electr. En. 10:100817. DOI: https://doi.org/10.1016/j.prime.2024.100817
Triebe, O., Hewamalage, H., Pilyugina, P., Laptev, N., Bergmeir, C., Rajagopal, R., 2021. NeuralProphet: explainable forecasting at scale. arXiv:2111.15397v1.
Tzounis, A., Katsoulas, N., Bartzanas, T., Kittas, C., 2017. Internet of Things in agriculture, recent advances and future challenges. Biosyst. Eng. 164:31-48. DOI: https://doi.org/10.1016/j.biosystemseng.2017.09.007
Vanegas-Ayala, S.C., Barón-Velandia, J., Leal-Lara, D.D., 2022. A systematic review of greenhouse humidity prediction and control models using fuzzy inference systems. Adv. Human-Comp. Interac. 2022: 8483003. DOI: https://doi.org/10.1155/2022/8483003
Yount, F.S., 2017. ASHRAE Research: Improving the Quality of Life.
Zhang, B., Xu, D., Liu, Y., Li, F., Cai, J., Du, L., 2016. Multi-scale evapotranspiration of summer maize and the controlling meteorological factors in north China. Agr. Forest Meteorol. 216:1-12. DOI: https://doi.org/10.1016/j.agrformet.2015.09.015
How to Cite
“Forecasting the vapor pressure deficit in vertical farming facilities aiming to provide optimal indoor conditions” (2025) Journal of Agricultural Engineering [Preprint]. doi:10.4081/jae.2025.1793.
Copyright (c) 2025 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
