Bird damage to orchards results in significant financial losses for farmers, with traditional methods like bird cannons and tree netting proving ineffective long-term due to high maintenance and limited mobility. Unmanned Aerial Vehicles (UAVs) offer a versatile solution, but their limited autonomy requires advanced flight planning. This paper introduces a path-planning optimization algorithm for UAVs based on Particle Swarm Optimization (PSO), chosen for its simplicity and effectiveness as an initial optimization method. The algorithm optimizes the drone’s path by considering distance, flight time and randomness to overcome the limitations of other systems. Compared to existing Gray Wolf Optimization and African Vulture Optimization, our proposed PSO method demonstrated superior performance. The evaluation was conducted using a simulated tree case to represent various scenarios, with our PSO algorithm achieving an accuracy of 0.7785, a loss of 0.0239 and a runtime of 01.8624, outperforming the other methods.