q-rung logarithmic Pythagorean neutrosophic vague normal aggregating operators and their applications in agricultural robotics

Abstract

The article explores multiple attribute decision making problems through the use of the Pythagorean neutrosophic vague normal set (PyNVNS). The PyNVNS can be generalized to the Pythagorean neutrosophic interval valued normal set (PyNIVNS) and vague set. This study discusses $ q $-rung log Pythagorean neutrosophic vague normal weighted averaging ($ q $-rung log PyNVNWA), $ q $-rung logarithmic Pythagorean neutrosophic vague normal weighted geometric ($ q $-rung log PyNVNWG), $ q $-rung log generalized Pythagorean neutrosophic vague normal weighted averaging ($ q $-rung log GPyNVNWA), and $ q $-rung log generalized Pythagorean neutrosophic vague normal weighted geometric ($ q $-rung log GPyNVNWG) sets. The properties of $ q $-rung log PyNVNSs are discussed based on algebraic operations. The field of agricultural robotics can be described as a fusion of computer science and machine tool technology. In addition to crop harvesting, other agricultural uses are weeding, aerial photography with seed planting, autonomous robot tractors and soil sterilization robots. This study entailed selecting five types of agricultural robotics at random. There are four types of criteria to consider when choosing a robotics system: robot controller features, cheap off-line programming software, safety codes and manufacturer experience and reputation. By comparing expert judgments with the criteria, this study narrows the options down to the most suitable one. Consequently, $ q $ has a significant effect on the results of the models.

Keywords

• madm
• $ q $-rung pynvnwa
• $ q $-rung gpynvnwa
• $ q $-rung gpynvnwg