Assessment and selection of smart agriculture solutions using an information error‐based Pythagorean fuzzy cloud algorithm

Abstract

Smart agriculture can enhance agricultural production efficiency, improve the ecological environment, and realize the sustainable development of agriculture. Many countries and companies are working hard to develop or introduce smart agricultural solutions. Because of the shackles of traditional agricultural management methods and fierce competition with a variety of different solutions, it is a difficult task for enterprises to select and implement smart agricultural solutions smoothly. Hence, enterprises must assess alternative solutions and select a feasible solution in advance. This study drew a novel assessment and selection for smart agriculture solutions using an information error‐based Pythagorean fuzzy cloud algorithm. First, an evaluation index system built on smart agriculture solutions was constructed from four aspects. Then, a new concept of Pythagorean fuzzy clouds was defined to express the evaluation information for each indicator. Simultaneously, the Pythagorean fuzzy cloud weighted Bonferroni mean (PFCWBM) operator was developed to aggregate the assessment information of multiple indicators. Next, an assessment and selection decision framework for smart agriculture solutions based on the PFCWBM operator was presented. In addition, an example was given to illustrate the effectiveness of the proposed algorithm. Finally, a discussion was conducted to verify the superiority of our approach. The results showed that our algorithm can characterize and evaluate complex information and has high sensitivity and environmental adaptability.

Development Potential Evaluation for Land Resources of Forest Tourism Based on Fuzzy AHP Method

“…Steps are as follows: calculat weight to calculate the judgment matrix of the maximum characteristic root and corresponding eigenvectors, the largest eigenvalue of the matrix by solving consistency corresponding eigenvectors as weight vector [31][32][33][34].…”

A study on weighted aggregation operators for q‐ rung orthopair m‐ polar fuzzy set with utility to multistage decision analysis

“…Riaz et al 22 proposed the MCDM approach for robotic agrifarming by using the novel concept of q‐ rung orthopair m‐ polar fuzzy sets (qROmPFSs). Yang et al 23 proposed “assessment and selection of smart agriculture solutions using an information error‐based PF cloud algorithm.” Yang and Feng 24 extended PFS to the study of the PF Petri net‐based security assessment model for the civil aviation airport security inspection information system. Naeem et al 25 inaugurated a mathematical approach to medical diagnosis via Pythagorean fuzzy soft TOPSIS, Vlsekriterijumska Optimizacija I KOmpromisno Resenje (VIKOR), and generalized aggregation operators (AOs).…”

“…Riaz et al 22 proposed the MCDM approach for robotic agrifarming by using the novel concept of q-rung orthopair m-polar fuzzy sets (qROmPFSs). Yang et al 23 proposed "assessment and selection of smart agriculture solutions using an information error-based PF cloud algorithm." Yang and Feng 24 extended PFS to the study of the PF Petri net-based security assessment model for the civil aviation airport security inspection information system.…”

Probabilistic Linguistic PROMETHEE I and II Methods for Evaluation of the Reform Scheme of Postgraduate Innovation and Entrepreneurship Education Talent Training Mode under the Big Data Environment

“…For example, when DMs are evaluating the reform scheme of the talent training mode, based on the self-cognition and knowledge system of research problems, the DMs may consider that they are 70% sure the reform scheme effect is "very good," 20% sure it is "good," and 10% sure it is "bad." Because of the advantages of accurate expression of PTLS, some MCDMs are extended with probabilistic linguistic information to accurately express qualitative data or fuzzy data [30][31][32][33][34][35][36]. Liao et al [37] proposed a linear programming method with probabilistic linguistic information for solving MCDM problems.…”