Intelligent Data-Driven Decision Support for Agricultural Systems-ID3SAS

Sara Oleiro Araújo; Ricardo Silva Peres; Leandro Filipe; Alexandre Manta-Costa; Fernando Lidon; José Cochicho Ramalho; José Barata

doi:10.1109/ACCESS.2023.3324813

Intelligent Data-Driven Decision Support for Agricultural Systems-ID3SAS

Sara Oleiro Araújo, Ricardo Silva Peres, Leandro Filipe, Alexandre Manta-Costa, Fernando Lidon, José Cochicho Ramalho, José Barata

Research output: Contribution to journal › Article › peer-review

26 Citations (Scopus)

181 Downloads (Pure)

Abstract

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.

Original language	English
Pages (from-to)	115798-115815
Number of pages	18
Journal	IEEE Access
Volume	11
DOIs	https://doi.org/10.1109/ACCESS.2023.3324813
Publication status	Published - 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 2 Zero Hunger
SDG 6 Clean Water and Sanitation
SDG 8 Decent Work and Economic Growth
SDG 17 Partnerships for the Goals

Keywords

Agriculture 4.0
decision support system
fuzzy logic
Internet of Things
node-RED
wireless sensor and actuator network

Access to Document

10.1109/ACCESS.2023.3324813Licence: CC BY-NC-ND

Intelligent Data-Driven Decision SupportFinal published version, 3.69 MBLicence: CC BY-NC-ND

Cite this

@article{e617da850cb04562a243f8b0fd25a7f9,

title = "Intelligent Data-Driven Decision Support for Agricultural Systems-ID3SAS",

abstract = "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.",

keywords = "Agriculture 4.0, decision support system, fuzzy logic, Internet of Things, node-RED, wireless sensor and actuator network",

author = "Ara{\'u}jo, \{Sara Oleiro\} and Peres, \{Ricardo Silva\} and Leandro Filipe and Alexandre Manta-Costa and Fernando Lidon and Ramalho, \{Jos{\'e} Cochicho\} and Jos{\'e} Barata",

note = "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. {\textcopyright} 2023 IEEE.",

year = "2023",

doi = "10.1109/ACCESS.2023.3324813",

language = "English",

volume = "11",

pages = "115798--115815",

journal = "IEEE Access",

issn = "2169-3536",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

}

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 - https://www.scopus.com/pages/publications/85174837174

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 -

Intelligent Data-Driven Decision Support for Agricultural Systems-ID3SAS

Abstract

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this