A perspective study for the assessment of field robots in agriculture: An enhanced fuzzy MADM approach

Abstract

The industrial revolution led in the creation of robots capable of performing complicated tasks in a range of industries. Robotic agrifarming is an innovative smart farming technology that can improve food safety and crop yield and address the global labour crisis. Agribots are used to undertake field tasks ranging from seeding through harvesting, which improve soil quality and guarantee long-term growth. As a result, the development of a decision-support system for determining the most appropriate robot for an agricultural operation is necessary. This study develops a T2IF-based integrated decision system for evaluating the situation at hand. The performance of five field robots is assessed based on technical, economic, social, political, and environmental aspects using the integrated BWM-MULTIMOORA methodology. The advantage of the developed ranking technique is that it follows Borda’s rule for prioritizing the alternatives rather than dominance theory, where both the utility and the subordinate ranking order of methods are considered. In addition, a comparative and sensitivity investigation is carried out to examine the stability of the results.