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The research aims to optimize multiple-drone path planning for pesticide application in precision agriculture using a hybrid approach.

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

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The research aims to optimize multiple-drone path planning for pesticide application in precision agriculture using a hybrid approach.
Integrates Ant Colony Optimization (ACO), Genetic Algorithm (GA), and 3Opt for optimal route planning.
Uses ACO to assign drones to targets based on plant health.
Employs GA to tune ACO parameters automatically.
Implements 3Opt for local route efficiency enhancement.
GA tunes four key ACO parameters effectively.
Drone capacity impacts route length significantly.
Integration of GA, ACO, and 3Opt leads to up to 13.6% improvement in efficiency over traditional ACO.

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Hybrid optimization approaches improve efficiency in drone spraying routes, indicating sustainable agricultural practices.

Wardana et al. (2026) studied this question.

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