Optimizing Drone Coverage in Agriculture: an Overview and New Approaches

This article investigates the problem of trajectory optimization for unmanned aerial vehicles during multispectral imaging of agricultural lands within the framework of precision agriculture concepts. The main problems related to complex field geometry, presence of natural and artificial obstacles, as well as limited battery capacity of drones are considered. A new hybrid route optimization method is proposed that integrates the ant colony optimization algorithm for global planning of zone traversal sequence with the binary gridding method for detailed local replanning within complex areas and obstacle avoidance. A key feature of the method is an adaptive mission recovery mechanism that allows the drone to dynamically return to the charging station, save mission state, and automatically continue operation from the last uncovered area. Simulation and comparative analysis results demonstrate that the developed approach significantly reduces total traveled route length and optimizes mission execution time compared to traditional methods, confirming its effectiveness for increasing autonomy and productivity of agricultural unmanned aerial vehicles.