Spherical Fuzzy Aczel–Alsina Aggregation Operators with AHP Approach for Artificial Intelligence in Smart Agriculture

Tahira Karamat; Ilija Stojanović; Arianit Peci; Adis Puška

Authors

Tahira Karamat Department of Mathematics, Riphah International University Lahore, Lahore, Pakistan Author https://orcid.org/0009-0003-1628-7069
Ilija Stojanović Dubai International Academic City, American University in the Emirate, Dubai, United Arab Emirates Author https://orcid.org/0000-0002-9299-0384
Arianit Peci Department of Mathematics and Informatics, Faculty of Economics and Agribusiness, Agricultural University of Tirana, Tirana, Albania Author https://orcid.org/0009-0008-0972-8213
Adis Puška University of Bijeljina, Bijeljina, Bosnia and Herzegovina Author https://orcid.org/0000-0003-3274-0188

Keywords:

Spherical Fuzzy Sets, Aczel-Alsina, MAGDM, Smart Agriculture, Environmental Sustainability

Abstract

This paper investigates multi-attribute decision-making (MADM) problems within the framework of spherical fuzzy (SF) sets for the evaluation and selection of artificial intelligence (AI) technologies in smart agriculture. The increasing complexity and uncertainty associated with agricultural decision-making, driven by environmental variability, technological advancements, and sustainability requirements, necessitate robust and flexible decision-support methodologies. To address this challenge, the Aczel–Alsina (AA) t-norm and t-conorm are incorporated into the spherical fuzzy environment to extend existing aggregation mechanisms. Accordingly, two novel aggregation operators, namely the Spherical Fuzzy Aczel–Alsina Geometric (SFAAG) operator and the Spherical Fuzzy Aczel–Alsina Averaging (SFAAA) operator, are proposed, and their fundamental mathematical properties, including idempotency, monotonicity, boundedness, and stability, are thoroughly investigated. Furthermore, a MADM framework based on the proposed operators is developed to support the selection of AI-based smart agriculture technologies under spherical fuzzy information. To demonstrate the applicability of the proposed approach, a case study involving the evaluation of five AI technologies, namely smart irrigation systems, crop disease identification systems, agricultural drone surveillance, smart harvesting robots, and AI-based weather forecasting systems, is conducted using productivity improvement, cost reduction, environmental sustainability, and technical reliability as evaluation criteria. Comparative analyses with existing aggregation-based decision-making methods confirm the effectiveness, robustness, and reliability of the proposed framework in handling complex and uncertain decision-making environments. The results indicate that the proposed approach provides an efficient and reliable decision-support tool for the assessment and selection of AI technologies in smart agriculture.

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