Assessment and selection of smart agriculture solutions using an information error‐based Pythagorean fuzzy cloud algorithm. Issue 11 (12th July 2021)

• Record Type: Journal Article
• Authors: Yang, Zaoli; Lin, Mingwei; Li, Yuchen; Zhou, Wei; Xu, Bing
• Is Part Of: International journal of intelligent systems. Volume 36:Issue 11(2021)
• Page Start: 6387
• Page End: 6418
• Publication Date: 2021-07-12
• Subjects: assessment and selection decision -- information error -- Pythagorean fuzzy cloud -- smart agriculture solutions; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22554
• Languages: English
• ISSNs: 0884-8173

Abstract

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.