Hybrid Multi-Attribute Decision Framework for Optimal UAV Selection in Smart Farming Applications

The innovation in UAVs had a great impact on efficiency improvement and environmental sustainability in modern agriculture, especially for the applications of precision spraying and spreading. However, due to several conflicting criteria, it is difficult to pick an appropriate agricultural UAV. In this study, a combined MADA framework based on AHP, TOPSIS, MABAC, and ARAS methods was developed for the selection/prioritization of agricultural UAVs. Based on the key operational and technical parameters, such as spray tank volume, spreading reservoir volume, payload, wide spraying area, net weight, and maximum takeoff weight, three popular UAVs (EFT Z۵۰, XAG P۱۵۰ (۲۰۲۴), and DJI T۶۰) were evaluated. The relative importance of the evaluation criteria was derived using the AHP, and the results showed that payload-based and spreading-efficiency factors were the most influential factors. The final ranking from TOPSIS, MABAC, and ARAS clearly demonstrates XAG P۱۵۰ (۲۰۲۴) as the optimal choice. Additionally, Spearman rank correlation analysis verified the consistency and robustness of the MADA methods applied. The results illustrate that the proposed hybrid evaluation scheme increases decision-making reliability and decreases the MADA process uncertainty as a whole for agriculture UAV selection cases. Thus, the presented method can serve as a systematic and useful decision support for applications in precision agriculture and smart farming.

کلیدواژه ها:

Precision agriculture ، agricultural UAV ، multi-attribute decision approach ، spraying drones ، UAV performance evaluation