q-rung模糊LOPCOW-VIKOR模型,用于评估无人机在农业食品4.0时代实现精准农业方面的作用。

IF 10.7 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence Review Pub Date : 2023-04-11 DOI:10.1007/s10462-023-10476-6
Fatih Ecer, İlkin Yaran Ögel, Raghunathan Krishankumar, Erfan Babaee Tirkolaee
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引用次数: 7

摘要

由于技术进步和可持续习惯的推广,智能农业最近受到了广泛关注。无人机通过在农业的不同阶段提供帮助,在智能农业中发挥着至关重要的作用。无人机对可持续精确农业的贡献是一个需要考虑的关键和具有挑战性的问题,特别是对于小农户来说,为了节省时间和金钱,提高他们的农业技能。因此,本研究旨在提出一个综合的群体决策框架,以确定最佳农业无人机。以前关于无人机评估的研究,(i)不能有效地对不确定性建模,(ii)没有有条不紊地确定专家的权重;(iii)在标准权重计算过程中没有考虑专家和标准类型的重要性,以及(iv)在考虑双重权重实体的同时,缺乏无人机的个性化排名。在此,根据相关文献和专家的意见,确定了九个关键的选择标准,并考虑了五个现存的无人机进行评估。为了弥补这些差距,在这项工作中,开发了一个新的综合框架,考虑了q阶正射空气模糊数(q-ROFN),用于适当的无人机选择。具体来说,通过提出遗憾度量,可以有条不紊地估计专家的权重。此外,建立了加权对数百分比变化驱动目标加权(LOPCOW)技术用于标准权重计算,并结合Copeland策略,提出了一种无人机个性化排序算法。研究结果表明,农业无人机选择的首要标准分别是“相机”、“动力系统”和“雷达系统”。此外,据推断,最有前途的无人机是DJ AGRAS T30。由于无人机在农业中的应用将是不可避免的,因此所开发的框架可以成为农民、管理者、决策者和其他利益相关者的有效决策支持系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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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

Smart 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.

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来源期刊
Artificial Intelligence Review
Artificial Intelligence Review 工程技术-计算机:人工智能
CiteScore
22.00
自引率
3.30%
发文量
194
审稿时长
5.3 months
期刊介绍: Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.
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