Agriculture 5.0: Drone Evaluation Based on Type-2 Fuzzy Set

The shift in agricultural production enabled by modern technology has led to the adoption of intelligent and autonomous systems, marking the Agriculture 5.0 era. Drones play a crucial role in this system, and as their use continues to expand, selecting the appropriate drones presents a complex multi-criteria decision-making problem that requires careful consideration of various factors. In this study, drones were evaluated and selected based on technical, economic, and environmental criteria. The decision-making process used interval type-2 fuzzy sets (IT2F) to incorporate uncertainty, and the model focused on ten criteria and five available drones in the Serbian market. It integrated the SiWeC (Simple Weight Calculation) method to determine criteria weights and the CRADIS (Compromise Ranking of Alternatives from Distance to Ideal Solution) method to rank the drones, forming a hybrid approach. The results indicated that Ease of Use (C6), Price (C8), and Maintenance Costs (C7) had the greatest influence on the final decision. Drone 4 achieved the highest score of 0.969 based on expert assessment, outperforming the second-ranked Drone 1 by 12%. Sensitivity analysis across 30 scenarios confirmed the stability of the ranking and showed that individual criteria did not significantly impact the results. The proposed IT2F SiWeC-CRADIS model demonstrated strong robustness and reliability and could be applied to select other smart technologies within the framework of Agriculture 5.0.

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Publication Details

Journal
Journal of Intelligent & Fuzzy Systems
Published
2026-04-20
DOI
https://doi.org/10.1177/18758967261433413
Primary Topic
UAV Applications and Optimization
Type
article
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article

Agriculture 5.0: Drone Evaluation Based on Type-2 Fuzzy Set

Anđelka Štilić, Adis Puška, Miroslav Nedeljković, Darko Božanić et al.
Journal of Intelligent & Fuzzy Systems
UAV Applications and Optimization
article

Agriculture 5.0: Drone Evaluation Based on Type-2 Fuzzy Set

Anđelka Štilić, Adis Puška, Miroslav Nedeljković, Darko Božanić, Dragan Pamučar
article en

Abstract

The shift in agricultural production enabled by modern technology has led to the adoption of intelligent and autonomous systems, marking the Agriculture 5.0 era. Drones play a crucial role in this system, and as their use continues to expand, selecting the appropriate drones presents a complex multi-criteria decision-making problem that requires careful consideration of various factors. In this study, drones were evaluated and selected based on technical, economic, and environmental criteria. The decision-making process used interval type-2 fuzzy sets (IT2F) to incorporate uncertainty, and the model focused on ten criteria and five available drones in the Serbian market. It integrated the SiWeC (Simple Weight Calculation) method to determine criteria weights and the CRADIS (Compromise Ranking of Alternatives from Distance to Ideal Solution) method to rank the drones, forming a hybrid approach. The results indicated that Ease of Use (C6), Price (C8), and Maintenance Costs (C7) had the greatest influence on the final decision. Drone 4 achieved the highest score of 0.969 based on expert assessment, outperforming the second-ranked Drone 1 by 12%. Sensitivity analysis across 30 scenarios confirmed the stability of the ranking and showed that individual criteria did not significantly impact the results. The proposed IT2F SiWeC-CRADIS model demonstrated strong robustness and reliability and could be applied to select other smart technologies within the framework of Agriculture 5.0.

Journal of Intelligent & Fuzzy Systems
Institute of Metrology of Bosnia and Herzegovina (BA), Institute of Agricultural Economics Belgrade (RS), Department of Mathematical Sciences (RU), University of Defence (RS), Sunway University (MY)
Zero hunger
Openalex Percentile: Top 5%
UAV Applications and Optimization
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Agriculture 5.0: Drone Evaluation Based on Type-2 Fuzzy Set — Anđelka Štilić, Adis Puška, et al. · Journal of Intelligent & Fuzzy Systems (2026) | TGRS Research Map | TGRS