Araújo, Sara OleiroPeres, Ricardo SilvaFilipe, LeandroManta-Costa, AlexandreLidon, FernandoRamalho, José CochichoBarata, José2024-03-052024-03-0520232169-3536PURE: 84474488PURE UUID: e617da85-0cb0-4562-a243-f8b0fd25a7f9Scopus: 85174837174WOS: 001090700500001ORCID: /0000-0003-3777-1346/work/154654801ORCID: /0000-0001-6192-8409/work/214827212http://hdl.handle.net/10362/164437Publisher Copyright: This work was partially supported by the SIMShore: SIMOcean Nearshore Bathymetry Based on Low Cost Approaches. This project received funding from the EEA Grants Portugal research and innovation program under Grant agreement No PT-INNOVATION-0027. © 2023 IEEE.The agricultural sector worldwide faces serious problems regarding water scarcity, which demands innovative management methods to optimise water use. In response, we propose the Intelligent Data-Driven Decision Support for Agricultural Systems (ID3SAS) methodology, which offers a scalable, flexible, and cloud-based decision support system for real-time supervision and control in agricultural environments. Aligned with the prevailing trends of Agriculture 4.0, ID3SAS integrates data acquisition, cloud-based storage, machine learning, predictive analysis, and run-time reasoning to facilitate decision-making processes, thereby assisting users in making more informed and sustainable decisions. In a case study with tomato plants, ID3SAS-irrigated plants showed 20.9% reduction in water consumption and 26.4% increase in crop production compared to traditional methods, which despite the controlled laboratory environment setting, highlights the methodology's promising potential in addressing water scarcity and enhancing agricultural productivity.183866113engAgriculture 4.0decision support systemfuzzy logicInternet of Thingsnode-REDwireless sensor and actuator networkGeneral Computer ScienceGeneral Materials ScienceGeneral EngineeringSDG 2 - Zero HungerSDG 6 - Clean Water and SanitationSDG 8 - Decent Work and Economic GrowthSDG 17 - Partnerships for the Goalsjournal article10.1109/ACCESS.2023.3324813https://www.scopus.com/pages/publications/85174837174