An Expert Opinion-Based Soft Computing Framework for Comparing Nanotechnologies used in Agriculture
Year: 2025
Type: article
Abstract: Nanotechnology (NT) has revolutionized agriculture through precision farming, environmental protection, enhanced nutrient delivery and crop production, and effective pesticide control. The purpose of the present research is twofold. First, it aims to develop a novel soft computing model for multi-criteria decision analysis (MCDA) by proposing an Interval-valued p, q–Quasirung Orthopair Fuzzy Number (p, q–IVQROFN)-based hybrid MCDA framework. The CIMAS (Criteria Importance Assessment) method is employed to determine the weights of the criteria, while the WISP method ranks the alternatives. This paper introduces a modification to the WISP method by aggregating four distinct utility degrees using the Heron Mean. The second objective is to apply the proposed framework to compare leading NTs used in agriculture. An expert group decision-making approach is developed to evaluate eight NTs, guided by the theoretical frameworks of TOE (Technology–Organization–Environment) and TRI (Technology Readiness Index). Alternatives are ranked using experts’ ratings within the proposed framework, followed by a risk assessment using the Fine–Kinney framework (FKF) to identify potential vulnerabilities. The NTs are compared based on both performance and risk profiles. The results indicate that nano-sensors (NT5), nano-fungicides (NT3), nano-fertilizers (NT2), nano-clays (NT6), and nano-herbicides (NT4) rank as the top nanotechnologies for agricultural applications. The reliability of the proposed model is confirmed through comparisons with other MCDA methods and sensitivity analysis. Overall, this paper presents a robust and practical methodology for sustainable agricultural planning.
DOI: 10.31181/sor4156
Authors: Sanjib Biswas, Sandeep Bhattacharjee, Biplab Biswas, Kaushik Mitra, Nibir Khawas
Institution: Amity University
Language: English
Field: Decision Sciences
Domain: Social Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W4413209611