A Hierarchical Soft Computational Model for Optimizing Agricultural UAVs: Recruiting Neutrosophic Theory and Tree Soft Sets

Precision agriculture is being transformed by Unmanned Aerial Vehicles (UAVs), which make it possible for yield optimization, targeted spraying, and sophisticated crop monitoring. With an emphasis on their operational capabilities, economic feasibility, and environmental implications, this research explores the revolutionary potential of UAV technology in contemporary farming systems. Practically speaking, the procedure of opting UAVs for agricultural applications is complicated by several competing aspects, inherent uncertainties, and differing stakeholder agendas. This paper suggests a new hybrid decision framework that combines Tree Soft Sets (TrSS), Neutrosophic theory, and Multi-Criteria Decision-Making (MCDM) to methodically handle these issues. Hence, the robust hybrid model known as soft computational model, that combines MEthod based on the Removal Effects of Criteria (MEREC) and COmbinative Distance-based ASsessment (CODAS) with Triangular Neutrosophic sets (TrNSs) to deal with uncertainty situations. These methodologies are integrated with the notion of soft sets in the form of TrSS.