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Article type: Research Article
Authors: Morales-García, Juana; * | Padilla-Quimbiulco, Diegoa | Cantabella, Magdalenaa | Ayuso, Beléna | Muñoz, Andrésb | Cecilia, José M.c
Affiliations: [a] Computer Science Department, Catholic University of Murcia (UCAM), ES, Spain | [b] Departament of Computer Engineering, University of Cádiz (UCA), ES, Spain | [c] Computer and Systems Informatics Department, Universitat Politècnica de València (UPV), ES, Spain
Correspondence: [*] Corresponding author. E-mail: [email protected].
Abstract: Greenhouses 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.
Keywords: Artificial intelligence, machine learning, temperature forecasting, warning system, smart greenhouses
DOI: 10.3233/AIS-230359
Journal: Journal of Ambient Intelligence and Smart Environments, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
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