In the rapidly evolving world of agriculture, drones have emerged as a game-changer, offering unprecedented efficiency and sustainability in farming practices. Among their various applications, spraying drones have gained significant traction, promising to revolutionize how agrarian lands are managed. However, with a plethora of options available, selecting the right drone for the job can be a daunting task. A recent study published in *Scientific Reports* (translated from Turkish as “Scientific Reports”) aims to simplify this process, providing a robust framework for evaluating and selecting agricultural spraying drones.
Led by Esra Boz from the Department of Industrial Engineering at KTO Karatay University, the research focuses on identifying critical selection criteria and ranking potential drone alternatives based on these criteria. “The growing adoption of agricultural drones has brought about the challenge of selecting the most suitable drones for spraying tasks on agrarian lands,” Boz explains. “Our study aims to address this problem by providing a systematic approach for evaluation and selection.”
The research team conducted comprehensive literature reviews and expert interviews to determine the key selection criteria. They developed the SIWEC method, utilizing p, q-Quasirung Orthopair Fuzzy Sets (p, q-QOFS), 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.
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,” Boz states.
The integrated fuzzy model offers a robust decision-making framework that can be adapted for similar selection problems in other domains. This research is not just about selecting the right drone; it’s about shaping the future of agriculture. As drones become more sophisticated and their applications more diverse, the need for such evaluation frameworks will only grow. This study lays the groundwork for future developments, ensuring that as technology advances, so too does our ability to make informed, efficient, and sustainable choices.
In the broader context, this research has significant implications for the energy sector as well. As the world shifts towards renewable energy sources, the need for efficient and sustainable agricultural practices becomes even more critical. Drones can play a pivotal role in this transition, and having a robust framework for their selection can accelerate this shift. The study’s findings can guide policymakers, farmers, and technology providers in making informed decisions, ultimately contributing to a more sustainable and efficient agricultural sector.
The research published in *Scientific Reports* is a testament to the power of interdisciplinary collaboration and innovative thinking. It underscores the importance of integrating advanced technologies like fuzzy logic and multi-criteria decision-making methods into traditional fields like agriculture. As we stand on the cusp of a new agricultural revolution, such studies will be instrumental in shaping the future of farming and beyond.