Evaluation for the selection of agricultural spraying drones using p, q-Quasirung orthopair fuzzy logic and multi-criteria methods.

Drones are increasingly used across various sectors due to their numerous advantages. In agriculture, drones play a critical role in activities like spraying, offering significant benefits in terms of sustainability and efficiency. The growing adoption of agricultural drones has brought about the challenge of selecting the most suitable drones for spraying tasks on agrarian lands. This study aims to address the problem of selecting the most appropriate agricultural drones for spraying purposes. It focuses on identifying critical selection criteria and ranking potential drone alternatives based on these criteria. Comprehensive literature research and expert interviews were conducted to determine the key selection criteria. The SIWEC method, utilizing p, q-Quasirung Orthopair Fuzzy Sets (p, q-QOFS), was developed to ascertain the importance levels of these criteria. Subsequently, alternatives were identified through detailed research on agricultural drones, and the AROMAN method, also employing p, q-QOFS, was proposed to rank the alternatives. Sensitivity analysis was applied to evaluate the robustness of the rankings produced by the integrated fuzzy model. The study found that the most critical criteria for selecting agricultural drones are payload, ease of use, and flight time. Among the alternatives, Alternative 2 was identified as the most preferred option, demonstrating superior alignment with the identified criteria. This research provides a systematic approach for evaluating and selecting agricultural drones for spraying, contributing to more efficient and sustainable agricultural practices. The integrated fuzzy model offers a robust decision-making framework that can be adapted for similar selection problems in other domains.