A Computational Framework for Precise Aerial Agricultural Spray Delivery Processes

As the world’s population is expected to increase, so is the global demand for food. Sustainable intensification via precision agriculture of existing farms can increase crop production. Agricultural spray drones have recently taken a physical role within precision agriculture, such as aerial application of fluids, solids, and biological control agents but have difficulties spraying in uncontrolled environments caused by wind shifting spray material away from intended target areas. This work proposes an efficient physics-based framework to provide drone operators with trajectory and spray nozzle configuration for optimal target crop-dusting to mitigate spray drifts while providing quantitative approximations of spray particle trajectory and ground concentration. The framework is coupled with a machine-learning algorithm (MLA) to aid users in their search for optimal results and includes two decoupled models that simulate wind and spray particle trajectories. In the model problem, a genetic algorithm (GA) is used to optimize the system where the optimal trajectory and spray nozzle configuration resulted in 64% of crop targets hit while only losing minimal spray material from spray drifts.