TY - JOUR
T1 - Intelligent Data-Driven Decision Support for Agricultural Systems-ID3SAS
AU - Araújo, Sara Oleiro
AU - Peres, Ricardo Silva
AU - Filipe, Leandro
AU - Manta-Costa, Alexandre
AU - Lidon, Fernando
AU - Ramalho, José Cochicho
AU - Barata, José
N1 - info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04035%2F2020/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00239%2F2020/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0092%2F2020/PT#
Publisher 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.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Agriculture 4.0
KW - decision support system
KW - fuzzy logic
KW - Internet of Things
KW - node-RED
KW - wireless sensor and actuator network
UR - http://www.scopus.com/inward/record.url?scp=85174837174&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2023.3324813
DO - 10.1109/ACCESS.2023.3324813
M3 - Article
AN - SCOPUS:85174837174
SN - 2169-3536
VL - 11
SP - 115798
EP - 115815
JO - IEEE Access
JF - IEEE Access
ER -
