{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T14:47:38Z","timestamp":1775486858039,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,21]],"date-time":"2023-01-21T00:00:00Z","timestamp":1674259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Project PID2021-122560OB-I00","award":["MCIN\/AEI\/10.13039\/501100011033"],"award-info":[{"award-number":["MCIN\/AEI\/10.13039\/501100011033"]}]},{"name":"ERDF","award":["MCIN\/AEI\/10.13039\/501100011033"],"award-info":[{"award-number":["MCIN\/AEI\/10.13039\/501100011033"]}]},{"name":"Spanish Ministry of Science, Innovation and Universities","award":["MCIN\/AEI\/10.13039\/501100011033"],"award-info":[{"award-number":["MCIN\/AEI\/10.13039\/501100011033"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In greenhouses, sensors are needed to measure the variables of interest. They help farmers and allow automatic controllers to determine control actions to regulate the environmental conditions that favor crop growth. This paper focuses on the problem of the lack of monitoring and control systems in traditional Mediterranean greenhouses. In such greenhouses, most farmers manually operate the opening of the vents to regulate the temperature during the daytime. Therefore, the state of vent opening is not recorded because control systems are not usually installed due to economic reasons. The solution presented in this paper consists of developing a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) as a soft sensor to estimate vent opening using the measurements of different inside and outside greenhouse climate variables as input data. A dataset from a traditional greenhouse located in Almer\u00eda (Spain) was used. The data were processed and analyzed to study the relationships between the measured climate variables and the state of vent opening, both statistically (using correlation coefficients) and graphically (with regression analysis). The dataset (with 81 recorded days) was then used to train, validate, and test a set of candidate LSTM-based networks for the soft sensor. The results show that the developed soft sensor can estimate the actual opening of the vents with a mean absolute error of 4.45%, which encourages integrating the soft sensor as part of decision support systems for farmers and using it to calculate other essential variables, such as greenhouse ventilation rate.<\/jats:p>","DOI":"10.3390\/s23031250","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T01:36:26Z","timestamp":1674437786000},"page":"1250","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A Soft Sensor to Estimate the Opening of Greenhouse Vents Based on an LSTM-RNN Neural Network"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5678-3310","authenticated-orcid":false,"given":"Mounir","family":"Guesbaya","sequence":"first","affiliation":[{"name":"LI3CUB Laboratory, Department of Electrical Engineering, University of Biskra, BP 145 RP, Biskra 07000, Algeria"}]},{"given":"Francisco","family":"Garc\u00eda-Ma\u00f1as","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Almer\u00eda, CIESOL, ceiA3, E04120 Almer\u00eda, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9536-1922","authenticated-orcid":false,"given":"Francisco","family":"Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Almer\u00eda, CIESOL, ceiA3, E04120 Almer\u00eda, Spain"}]},{"given":"Hassina","family":"Megherbi","sequence":"additional","affiliation":[{"name":"LARHYSS Laboratory, University of Biskra, BP 145 RP, Biskra 07000, Algeria"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"153128","DOI":"10.1016\/j.scitotenv.2022.153128","article-title":"The Impact of Climate Change Scenarios on Droughts and Their Propagation in an Arid Mediterranean Basin. A Useful Approach for Planning Adaptation Strategies","volume":"820","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103388","DOI":"10.1016\/j.agsy.2022.103388","article-title":"Process-Based Greenhouse Climate Models: Genealogy, Current Status, and Future Directions","volume":"198","author":"Katzin","year":"2022","journal-title":"Agric. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez, F., Berenguel, M., Guzm\u00e1n, J.L., and Ram\u00edrez-Arias, A. (2015). Modeling and Control of Greenhouse Crop Growth, Springer.","DOI":"10.1007\/978-3-319-11134-6"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"104877","DOI":"10.1016\/j.compag.2019.104877","article-title":"Wireless Sensor Networks for Greenhouses: An End-to-End Review","volume":"163","author":"Kochhar","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bersani, C., Ruggiero, C., Sacile, R., Soussi, A., and Zero, E. (2022). Internet of Things Approaches for Monitoring and Control of Smart Greenhouses in Industry 4.0. Energies, 15.","DOI":"10.3390\/en15103834"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.biosystemseng.2017.09.007","article-title":"Internet of Things in Agriculture, Recent Advances and Future Challenges","volume":"164","author":"Tzounis","year":"2017","journal-title":"Biosyst. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Rezvani, S.M., Abyaneh, H.Z., Shamshiri, R.R., Balasundram, S.K., Dworak, V., Goodarzi, M., Sultan, M., and Mahns, B. (2020). IoT-Based Sensor Data Fusion for Determining Optimality Degrees of Microclimate Parameters in Commercial Greenhouse Production of Tomato. Sensors, 20.","DOI":"10.3390\/s20226474"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6325","DOI":"10.1109\/JIOT.2020.2996081","article-title":"A New IoT-Based Platform for Greenhouse Crop Production","volume":"9","author":"Torres","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105031","DOI":"10.1016\/j.envsoft.2021.105031","article-title":"Introductory Overview: Systems and Control Methods for Operational Management Support in Agricultural Production Systems","volume":"139","author":"Linker","year":"2021","journal-title":"Environ. Model. Softw."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"509","DOI":"10.5424\/sjar\/2010083-1247","article-title":"Characterization of Technological Levels in Mediterranean Horticultural Greenhouses","volume":"8","author":"Balasch","year":"2010","journal-title":"Span. J. Agric. Res."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mendoza-Fern\u00e1ndez, A.J., Pe\u00f1a-Fern\u00e1ndez, A., Molina, L., and Aguilera, P.A. (2021). The Role of Technology in Greenhouse Agriculture: Towards a Sustainable Intensification in Campo de Dal\u00edas (Almer\u00eda, Spain). Agronomy, 11.","DOI":"10.3390\/agronomy11010101"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"219","DOI":"10.17660\/ActaHortic.2017.1170.25","article-title":"The Greenhouses of Almer\u00eda, Spain: Technological Analysis and Profitability","volume":"1170","author":"Valera","year":"2017","journal-title":"Acta Hortic."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"31","DOI":"10.17660\/ActaHortic.1997.443.3","article-title":"Greenhouse Ventilation Rates through Combined Roof and Side Openings: An Experimental Study","volume":"443","author":"Kittas","year":"1997","journal-title":"Acta Hortic."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/s42853-020-00075-6","article-title":"Sensor Systems for Greenhouse Microclimate Monitoring and Control: A Review","volume":"45","author":"Bhujel","year":"2020","journal-title":"J. Biosyst. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.ifacol.2019.12.520","article-title":"Greenhouse Models as a Service (GMaaS) for Simulation and Control","volume":"52","author":"Torres","year":"2019","journal-title":"IFAC-PapersOnLine"},{"key":"ref_16","unstructured":"Guesbaya, M., Garc\u00eda-Ma\u00f1as, F., Rodr\u00edguez, F., Megherbi, H., and Ouamane, M.R. (2021, January 11\u201312). Virtual Sensor for Ventilation Flux Estimation in Greenhouses. Proceedings of the XI Congreso Ib\u00e9rico de Agroingenier\u00eda, Valladolid, Spain."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106627","DOI":"10.1016\/j.compag.2021.106627","article-title":"Real-Time Adaptation of a Greenhouse Microclimate Model Using an Online Parameter Estimator Based on a Bat Algorithm Variant","volume":"192","author":"Guesbaya","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","unstructured":"Fortuna, L., Graziani, S., Rizzo, A., and Xibilia, M.G. (2007). Soft Sensors for Monitoring and Control of Industrial Processes, Springer."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"15244","DOI":"10.3390\/s121115244","article-title":"Virtual Sensors for Designing Irrigation Controllers in Greenhouses","volume":"12","author":"Arahal","year":"2012","journal-title":"Sensors"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Guzm\u00e1n, C.H., Carrera, J.L., Dur\u00e1n, H.A., Berumen, J., Ortiz, A.A., Guirette, O.A., Arroyo, A., Brizuela, J.A., G\u00f3mez, F., and Blanco, A. (2019). Implementation of Virtual Sensors for Monitoring Temperature in Greenhouses Using CFD and Control. Sensors, 19.","DOI":"10.3390\/s19010060"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"15796","DOI":"10.1016\/j.ifacol.2020.12.230","article-title":"Leaf Area Index Soft Sensor for Tomato Crops in Greenhouses","volume":"53","author":"Berenguel","year":"2020","journal-title":"IFAC-Papers OnLine"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.jprocont.2014.01.012","article-title":"Data-Driven Soft Sensor Development Based on Deep Learning Technique","volume":"24","author":"Shang","year":"2014","journal-title":"J. Process Control"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"125","DOI":"10.3182\/20050703-6-CZ-1902.02111","article-title":"On-Line Estimation of the Ventilation Rate of Greenhouses","volume":"38","author":"Bontsema","year":"2005","journal-title":"IFAC Proc. Vol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"415","DOI":"10.13031\/2013.21268","article-title":"Natural Ventilation of a Greenhouse with Ridge and Side Openings: Sensitivity to Temperature and Wind Effects","volume":"40","author":"Kittas","year":"1997","journal-title":"Trans. ASAE"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/S0168-1923(99)00041-6","article-title":"Natural Ventilation of Greenhouses: Experiments and Model","volume":"96","author":"Teitel","year":"1999","journal-title":"Agric. For. Meteorol."},{"key":"ref_26","first-page":"197","article-title":"Ventilation Rate Models of Mediterranean Greenhouses for Control Purposes","volume":"719","author":"Berenguel","year":"2006","journal-title":"Acta Hortic."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9820","DOI":"10.3390\/s111009820","article-title":"Sonic Anemometry to Measure Natural Ventilation in Greenhouses","volume":"11","author":"Valera","year":"2011","journal-title":"Sensors"},{"key":"ref_28","unstructured":"Graziani, S., and Xibilia, M.G. (2020). Development and Analysis of Deep Learning Architectures, Springer."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ojo, M.O., and Zahid, A. (2022). Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects. Sensors, 22.","DOI":"10.3390\/s22207965"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1002\/int.22620","article-title":"A Long Short-Term Memory-Based Model for Greenhouse Climate Prediction","volume":"37","author":"Liu","year":"2022","journal-title":"Int. J. Intell. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5929","DOI":"10.1007\/s10462-020-09838-1","article-title":"A review on the Long Short-Term Memory Model","volume":"53","author":"Mosquera","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Sepp","year":"1997","journal-title":"Neural Comput."},{"key":"ref_33","unstructured":"Arahal, M.R., Berenguel, M., and Rodr\u00edguez, F. (2006). T\u00e9cnicas de Predicci\u00f3n Con Aplicaciones En Ingenier\u00eda, Secretariado de Publicaciones de la Universidad de Sevilla."},{"key":"ref_34","unstructured":"Edgar, T.W., and Manz, D.O. (2017). Research Methods for Cyber Security, Elsevier."},{"key":"ref_35","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_36","unstructured":"Chok, N.S. (2010). Pearson\u2019s Versus Spearman\u2019s and Kendall\u2019s Correlation Coefficients for Continuous Data. [Doctoral Thesis, University of Pittsburgh]."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3021","DOI":"10.21105\/joss.03021","article-title":"Seaborn: Statistical Data Visualization","volume":"6","author":"Waskom","year":"2021","journal-title":"J. Open Source Softw."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1250\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:12:51Z","timestamp":1760119971000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1250"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,21]]},"references-count":37,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031250"],"URL":"https:\/\/doi.org\/10.3390\/s23031250","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,21]]}}}