Weather-Adaptive Multi-UAV Trajectory Planning with Swarm Intelligence for Drift-Aware Pesticide Spraying in Complex Farmlands
Keywords:
weather-adaptive; multi-UAV; swarm intelligence; drift-aware; pesticide spraying; precision agriculture; trajectory planning; complex farmlands; socio-technical systems; sustainability; policyAbstract
This paper presents a comprehensive analysis of weather-adaptive multi-UAV trajectory planning using swarm intelligence for drift-aware pesticide spraying in complex farmlands. The study addresses the critical intersection of autonomous aerial systems, environmental sensing, and agricultural precision. We propose a system architecture that integrates real-time meteorological data with particle swarm optimization and ant colony algorithms to dynamically adjust flight paths, minimizing spray drift and ensuring uniform coverage. The paper examines structural trade-offs between computational overhead and real-time adaptability, as well as governance frameworks for deploying such systems across diverse regulatory landscapes. Infrastructure requirements including communication networks, ground control stations, and fail-safe mechanisms are discussed. Robustness is analyzed through the lens of system redundancy and fault tolerance, while fairness considerations involve equitable access to precision spraying technologies for smallholder farmers. Sustainability implications are evaluated with respect to reduced chemical usage and energy consumption. Policy recommendations are provided for adaptive regulation that accommodates technological evolution. The paper concludes that weather-adaptive swarm-based UAV systems can significantly enhance the efficiency and environmental safety of pesticide application, provided that socio-technical challenges are addressed through interdisciplinary collaboration.
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