Enhancing ant colony optimization with genetic algorithm and 3-Opt for multiple drone spraying path planning in precision agriculture

Efficient and environmentally responsible pesticide application is a major challenge in precision agriculture. Excessive pesticide use in conventional farming increases costs, harms the environment, and poses health risks. Recent advancements in unmanned aerial vehicles (UAVs) or drones have enabled targeted spraying, yet optimizing multiple-drone route planning and task allocation remains complex due to dynamic field conditions and limited drone capacity. To address this gap, this study proposes a hybrid optimization approach that integrates Ant Colony Optimization (ACO), Genetic Algorithm (GA), and 3Opt to generate efficient flight routes for multiple sprayer drones based on plant health levels. In this framework, ACO assigns drones to target points, GA automatically tunes key ACO parameters, and 3Opt enhances route efficiency through local optimization. Experimental results show that GA effectively automates the tuning of four key ACO parameters and that drone capacity significantly affects route length. The integration of GA, ACO and 3Opt further reduces total route length, achieving up to 13.6% improvement in efficiency compared to traditional ACO. These findings demonstrate the potential of the proposed method to enhance route efficiency, reduce energy consumption, shorter mission completion time and offers a practical solution for improving the performance and sustainability of multiple-drone spraying operations.