The q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 era

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dc.authoridEcer, Fatih/0000-0002-6174-3241
dc.authoridyaran ögel, ilkin/0000-0003-3414-753X
dc.authoridTirkolaee, Erfan Babaee/0000-0003-1664-9210
dc.authorwosidEcer, Fatih/AAE-8455-2020
dc.authorwosidyaran ögel, ilkin/GXN-2257-2022
dc.authorwosidTirkolaee, Erfan Babaee/U-3676-2017
dc.contributor.authorEcer, Fatih
dc.contributor.authorOgel, Ilkin Yaran
dc.contributor.authorKrishankumar, Raghunathan
dc.contributor.authorTirkolaee, Erfan Babaee
dc.date.accessioned2024-05-19T14:38:55Z
dc.date.available2024-05-19T14:38:55Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractSmart agriculture is gaining a lot of attention recently, owing to technological advancement and promotion of sustainable habits. Unmanned aerial vehicles (UAVs) play a crucial role in smart agriculture by aiding in different phases of agriculture. The contribution of UAVs to sustainable and precision agriculture is a critical and challenging issue to be taken into account, particularly for smallholder farmers in order to save time and money, and improve their agricultural skills. Thence, this study targets to propose an integrated group decision-making framework to determine the best agricultural UAV. Previous studies on UAV evaluation, (i) could not model uncertainty effectively, (ii) weights of experts are not methodically determined; (iii) importance of experts and criteria types are not considered during criteria weight calculation, and (iv) personalized ranking of UAVs is lacking along with consideration to dual weight entities. Herein, nine critical selection criteria are identified, drawing upon the relevant literature and experts' opinions, and five extant UAVs are considered for evaluation. To circumvent the gaps, in this work, a new integrated framework is developed considering q-rung orthopair fuzzy numbers (q-ROFNs) for apt UAV selection. Specifically, methodical estimation of experts' weights is achieved by presenting the regret measure. Further, weighted logarithmic percentage change-driven objective weighting (LOPCOW) technique is formulated for criteria weight calculation, and an algorithm for personalized ranking of UAVs is presented with visekriterijumska optimizacija i kompromisno resenje (VIKOR) approach combined with Copeland strategy. The findings show that the foremost criteria in agricultural UAV selection are camera, power system, and radar system, respectively. Further, it is inferred that the most promising UAV is the DJ AGRAS T30. Since the applicability of UAV in agriculture will get inevitable, the developed framework can be an effective decision support system for farmers, managers, policymakers, and other stakeholders.en_US
dc.identifier.doi10.1007/s10462-023-10476-6
dc.identifier.endpage13406en_US
dc.identifier.issn0269-2821
dc.identifier.issn1573-7462
dc.identifier.issue11en_US
dc.identifier.pmid37362884en_US
dc.identifier.scopus2-s2.0-85152300607en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage13373en_US
dc.identifier.urihttps://doi.org10.1007/s10462-023-10476-6
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4650
dc.identifier.volume56en_US
dc.identifier.wosWOS:000966515000001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofArtificial Intelligence Reviewen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectAgri-Food 4en_US
dc.subject0en_US
dc.subjectUnmanned Aerial Vehiclesen_US
dc.subjectSmart Agricultureen_US
dc.subjectPrecision Agricultureen_US
dc.subjectMcdmen_US
dc.subjectQ-Rofns Lopcow-Vikoren_US
dc.titleThe q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 eraen_US
dc.typeArticleen_US

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