Planning and optimization of spraying missions for agricultural drones

Abstract

ABSTRACT: This study presents an optimized mission planning strategy for agricultural unmanned aerial vehicles (UAVs) that integrates battery endurance constraints into the coverage path planning (CPP) problem. The primary objective was to minimize total mission time by optimizing the sweep direction (θ) and automatically segmenting the path into executable sorties (m) based on battery capacity. The methodology was validated through simulations and a real-world case study of a 7.02 ha sugarcane field using parameters from a DJI AGRAS T10 drone (swath width of 5 m and flight speed of 5 m/s). The proposed approach was compared against classical methods, specifically those of TORRES et al. (2016) and CHOSET & PIGNON (1998). Results indicated reductions of 2.65% and 8.2% in total mission time, respectively, achieved by reducing unproductive turns and optimizing battery usage. This efficiency gain directly extends the operational window for spraying, enabling larger areas to be treated under ideal weather conditions. The findings demonstrated that integrating operational endurance into offline planning improves the reliability and logistics of drone-based precision agriculture applications.