Abstract: Traditional particle swarm optimization(PSO) is prone to getting stuck in local optima and low search ability for energy consumption optimal solutions when planning plant protection operation paths. The study focuses on improving the PSO algorithm to improve the efficiency of crop protection drone operations and reduce drone losses. An adjacent competition (AC) algorithm for crop protection operation path planning based on a crop protection UAV model was proposed to solve the problem of poor effectiveness of traditional particle swarm optimization algorithms applied to UAV plant protection operations. The AC algorithm ensures that the total distance of crop protection UAV operations is constant, provides prior knowledge of the search direction, and ensures that special points are not missed, preventing the algorithm from getting stuck in local optimal solutions: Firstly, all particles initialize the homework distance range to prevent extreme situations where the single homework distance is too long or too short. Secondly, each drone is randomly assigned the operating distance for each particle within the operating range. Finally, adjacent particles compete with each other for the operating distance, indirectly changing the operating distance of each flight and searching for the optimal path. Matlab was used to conduct algorithm simulation verification on a simulated plant protection site of 420 m×200 m. Traditional PSO algorithm often gets stuck in local optima and the final planning results fluctuate greatly, while the AC algorithm obtains the optimal energy consumption solution 9 times out of 10 planning iterations, and the planning energy consumption of non-optimal solutions is similar to the optimal energy consumption, still better than all planning results of the traditional PSO algorithm, proving that the neighboring contention algorithm has stronger searchability. The results show that traditional PSO algorithms often got stuck in local optima and increased energy consumption by 16.16% to 38.14% compared to neighboring contention algorithms in 10 rounds of path planning. This indicated that the energy consumption of the AC algorithm proposed in this study for planning results is much lower than that of the traditional PSO algorithm, and the algorithm has stronger search capabilities. This study used the RflySim simulation platform to build a crop protection drone model and a 420 m×200 m work site in order to compare the simulated tracking results of the traditional PSO algorithm and AC algorithm in a virtual environment. The results show that the energy consumption of AC algorithm planning results is reduced by 25.15% compared to the traditional PSO algorithm. This study conducted actual flight tests using the drone M300 at a 420 m×200 m work site to validate the practicality of the algorithm. Actual flight experiments have shown that the energy consumption of AC algorithm planning results is reduced by 34.48% compared to traditional particle swarm optimization algorithms, making it more suitable for multiple crop protection operations. In actual experiments, the return points of multiple UAVs in the adjacent contention algorithm are closer to the supply point, the distance of departure and return is shorter, and the ineffective energy consumption is lower compared with the traditional PSO algorithm, indicating that its planning is more reasonable. Adjacent particles indirectly change the distance of each operation by competing for each other's operation distance, effectively improving the convergence speed of the algorithm, preventing the algorithm from getting stuck in local optima, and improving the efficiency of multiple crop protection operations with a single drone.