Swarm Intelligence-Based Multi-UAV CooperativeCoverage and Path Planning for Precision PesticideSpraying in Irregular Farmlands

Achieving precise pesticide spraying tasks in irregular farmland faces multiple challenges such as multi-UAV path coordination, coverage efficiency optimization, and energy consumption control, especially in environments with complex terrain and significant changes in crop density. To this end, this paper proposes a multi-UAV collaborative coverage and path planning model (SI-MUCCPP) based on swarm intelligence, which is improved on the basis of the existing artificial bee colony algorithm (ABC), and integrates the Voronoi adaptive zoning strategy and dynamic Dubins curve path planning method with area awareness. Model includes three core innovations: (1) The adaptive regional subdivision module, which constructs the load- balanced Voronoi division of irregular farmland by introducing crop density and terrain complexity weights. (2) The role-dividing group collaboration mechanism simulates the foraging behavior of bee colonies, and dynamically assigns the roles of detectors, boundary followers and replenishers to improve the efficiency of multi-machine cooperation and reduce repeated spraying. (3) The hybrid fitness function design comprehensively considers the coverage integrity, flight time, energy consumption minimization and obstacle avoidance constraints, and introduces an improved search operator based on reverse learning and local perturbation to achieve optimal path convergence under complex terrain in the global-local two-stage iteration. Experimental results show that the proposed SI-MUCCCPP model improves the coverage efficiency by 17.6% and reduces the total energy consumption by 12.3% compared with the traditional method.