Yildirim, B.E.Durmaz, S.Yildiz, A.Kucukkomurcu, B.Ozbek, T.Türkmen, G.2026-03-052026-03-05202597983315997682521-164110.1109/UBMK67458.2025.112069032-s2.0-105030815191https://doi.org/10.1109/UBMK67458.2025.11206903https://hdl.handle.net/20.500.14411/11207This project explores tree detection and tracking using drones coordinated by swarm intelligence. By enabling autonomous coordination and real-time communication between multiple drones, swarm-based systems significantly enhance area coverage, reduce redundancy, and increase data reliability compared to traditional single-drone approaches. Drones equipped with high-resolution cameras collect aerial imagery, which is then processed through image analysis and machine learning algorithms to identify tree locations accurately. Dynamic task allocation and route optimization enable efficient regional coverage while minimizing error rates. The entire system is developed and evaluated in a simulation environment, allowing for controlled testing and iterative refinement of the swarm behaviors. This framework offers scalable and adaptive solutions for applications in forest conservation, environmental monitoring, and ecosystem management. © 2025 IEEE.eninfo:eu-repo/semantics/closedAccessDrone TechnologyEnvironmental MonitoringMachine LearningSwarm IntelligenceTree DetectionConference Object