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dc.contributor.authorMorales García, Juan
dc.contributor.authorPadilla Quimbiulco, Diego
dc.contributor.authorCantabella, Magdalena
dc.contributor.authorAyuso, Belén
dc.contributor.authorMuñoz, Andrés
dc.contributor.authorCecilia, José M.
dc.date.accessioned2025-10-03T07:44:22Z
dc.date.available2025-10-03T07:44:22Z
dc.date.issued2024
dc.identifier.citationMorales-García, J., Padilla-Quimbiulco, D., Cantabella, M., Ayuso, B., Muñoz, A., & Cecilia, J. M. (2024). GreenhouseGuard: Enabling real-time warning prediction for smart greenhouse management. Journal of Ambient Intelligence and Smart Environments, 16(3), 389-405. https://doi.org/10.3233/AIS-230359es
dc.identifier.urihttp://hdl.handle.net/10952/10260
dc.description.abstractGreenhouses constitute intricate systems where numerous variables play a pivotal role in enhancing crop yields within the framework of intensive agriculture. Consequently, real-time monitoring and visualization of these variables are imperative to strike a balance between resource efficiency and production maximization. Furthermore, the ability to make predictive assessments regarding these variables is essential to avert potential greenhouse disasters. In this article, we introduce an intelligent alert system designed to efficiently oversee agricultural operations within a functioning greenhouse, ultimately bolstering productivity through the optimization of crop growth and energy consumption. This system comprises a web application, GreenhouseGuard, which improves the graphical and statistical representation of data collected by a network of sensors strategically positioned throughout the greenhouse, as well as the forecasts generated from this data. These sensors are strategically located to provide more precise real-time data readings, thereby minimizing error margins. Moreover, GreenhouseGuard offers diverse data visualization options and forecasts of greenhouse variables to enable in-depth analysis of the acquired information. Consequently, this alert system empowers greenhouse managers to proactively address abnormal situations that may jeopardize their crop yields.es
dc.language.isoenes
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectArtificial intelligencees
dc.subjectMachine learninges
dc.subjectTemperature forecastinges
dc.subjectWarning systemes
dc.subjectSmart greenhouseses
dc.titleGreenhouseGuard: Enabling real-time warning prediction for smart greenhouse managementes
dc.typejournal articlees
dc.rights.accessRightsopen accesses
dc.journal.titleJournal of Ambient Intelligence and Smart Environmentses
dc.volume.number16es
dc.description.disciplineIngeniería, Industria y Construcciónes
dc.identifier.doi10.3233/AIS-230359es
dc.description.facultyEscuela Politécnicaes


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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