A Multi-Objective Optimization Approach to Coverage Path Planning of Agricultural Drone

Agricultural drones have been widely used in precision agriculture operations due to their high performance and adaptability to outdoor tasks, such as spraying, mapping, and monitoring. Nevertheless, mission planning remains challenging due to the constraints imposed by operational performance under real field conditions. In this work, a novel multi-objective optimization approach for mission planning of agricultural spraying drones is proposed. This approach contemplated an off-line coverage path planning strategy that simultaneously minimizes total mission time and energy consumption. The parameters that define the coverage path are optimized while considering the operational requirements of the spraying tasks. The framework is applied to a real case study using the DJI AGRAS T10 drone in a crop field. The results obtained from the multi-objective optimization demonstrate significant performance gains in terms of reduction in battery energy consumption of up to 14.2% and a mission time decrease of 9.5% when compared to conventional geometric methods. Thus, the proposed method potentially reduces both the drag energy and the mission time. These findings highlight the potential of multi-objective optimization as a decision-support tool to improve the efficiency and sustainability of drone-based spraying operations in agriculture.