Application of fractional fuzzy soft sets using hamacher aggregation operators in agriculture robots selection

Abstract

In the development of digital agriculture, agricultural robots play a unique role and discuss numerous advantages in farming production. Robotic agri-farming is a smart farming technique that increases food safety and agricultural productivity while solving the worldwide workforce shortage. Selecting an agricultural reboot is a very challenging task and a critical component to increasing crop productivity and sustainability, often involving multiple conflicting criteria. Therefore, we introduce a novel decision-making model based on evaluation based on the distance from the average solution (EDAS) method under fractional fuzzy soft set (FF $${S}_{t} S$$ ) to sort out the challenges of the agri-forming robot selection. The soft sets ( $${S}_{t} S$$ ) theory provides a general mechanism for handling uncertainty based on the point of view of parameterization tools. The main theme of this manuscript is to extend the notion of Hamacher operators by establishing an interesting connection between two mathematical concepts $${S}_{t} S$$ theory and fractional fuzzy sets (FFSs). In this paper we define fractional fuzzy soft set (FF $${S}_{t} S$$ ), which may capture ambiguity and apprehension in a manner that is more flexible. Some basic operational laws, score and accuracy functions, and certain aggregation operators under fractional fuzzy soft sets, such as the fractional fuzzy soft Hamacher weighted average (FF $${S}_{t}$$ HWA) operator, (FF $${S}_{t}$$ HOWA) operator, (FF $${S}_{t}$$ HWHA) operator, (FF $${S}_{t}$$ HWG) operator, (FF $${S}_{t}$$ HOWG) operator, and (FF $${S}_{t}$$ HWHG) operator, are defined for fractional fuzzy soft sets (FF $${S}_{t} S$$ ). Furthermore, the proposed method is applied to solve real-life problems based on the selection of agri-forming robots. According to the proposed model, Semios Robots is the best alternative. In addition, comparative research is conducted to demonstrate the feasibility and reliability of the suggested technique in contrast to current methods.