The use of unmanned aerial vehicle (UAV) pesticide spraying technology in precision agriculture is becoming increasingly important. However, traditional spraying methods struggle to address the precision application need caused by the canopy differences of fruit trees in orchards. This study proposes a UAV orchard variable-rate spraying method based on canopy volume. A DJI M300 drone equipped with LiDAR was used to capture high-precision 3D point cloud data of tree canopies. An improved progressive TIN densification (IPTD) filtering algorithm and a region-growing algorithm were applied to segment the point cloud of fruit trees, construct a canopy volume-based classification model, and generate a differentiated prescription map for spraying. A distributed multi-point spraying strategy was employed to optimize droplet deposition performance. Field experiments were conducted in a citrus (Citrus reticulata Blanco) orchard (73 trees) and a litchi (Litchi chinensis Sonn.) orchard (82 trees). Data analysis showed that variable-rate treatment in the litchi area achieved a maximum canopy coverage of 14.47% for large canopies, reducing ground deposition by 90.4% compared to the continuous spraying treatment; variable-rate treatment in the citrus area reached a maximum coverage of 9.68%, with ground deposition reduced by approximately 64.1% compared to the continuous spraying treatment. By matching spray volume to canopy demand, variable-rate spraying significantly improved droplet deposition targeting, validating the feasibility of the proposed method in reducing pesticide waste and environmental pollution and providing a scalable technical path for precision plant protection in orchards.

The rotor airflow intensity and distribution characteristics of plant protection UAVs vary significantly with spatial positions below the rotor. Consequently, changes in the rotor–nozzle distance directly affect droplet motion and deposition patterns. To optimize the spraying effect of UAVs, this study combined a numerical simulation of rotor airflow and droplet deposition at different vertical distances between rotor and nozzle with field validation tests. The simulation results revealed that airflow intensity initially increases and then decreases with greater rotor–nozzle distance, peaking at 300–400 mm below the rotor with a maximum airflow velocity of 8.1 m/s. At 360 mm, the droplet swarm achieved its highest average velocity, corresponding to optimal deposition effect. Field tests confirmed a non-linear relationship between rotor–nozzle distance and droplet deposition. Droplet deposition first increased but declined sharply beyond the optimal range. When the distance was 360 mm, the target area exhibited the highest droplet deposition of 0.766 μL·cm⁻² and the lowest drift rate of 23.31%. Although a certain deviation existed between numerical simulation results and field test values, both methods consistently identified 360 mm as the ideal distance for balancing deposition efficiency and drift control. These findings provide actionable insights for field trial design and advance precision spraying strategies for plant protection UAVs.