Research on Optimal Spraying Path of Agricultural Unmanned Aerial Vehicle Based on Particle Swarm and Genetic Algorithm

With the rapid development of intelligent agriculture, intelligent agricultural machinery is continuously applied to agricultural production. But for agricultural UAVs in the spraying operation process of the path planning research, this paper firstly analyzed the optimal spraying path planning for agricultural drones, and used particle swarm algorithm (PSO) to optimize it, and secondly used genetic algorithm (GA) to carry out planning research on the paths of agricultural drones, and finally used MATLAB to carry out simulation on the path planning under PSO and GA, and compared the differences between the two algorithms and given the final optimized paths. Through the simulation comparison and analysis, it was found that the path planning results under PSO were more accurate, and there were fewer computational steps under GA, which verified the feasibility and validity of the optimization algorithm model through simulation comparison and analysis and provided an effective basis for the subsequent design of agricultural UAVs.