Orchard variable rate spraying method and experimental study based on multidimensional prescription maps

The existing prescription map spraying methods only integrate the spraying needs of target crops in a two-dimensional space, neglecting the variations in spraying requirements in depth information, and ignoring the coupling issues between prescription map design and mechanical parameters. This study concentrates on orchard environments and proposes a multi-dimensional prescription map design method based on point cloud data, aimed at guiding variable-rate spraying operations for sprayers. This method includes three main aspects: tree point cloud leaf separation, nozzle position topology and spraying unit division, and multi-dimensional prescription map design. First, the features of the point cloud are enhanced, and the tree leaf point cloud is extracted using a Long Short Term Memory (LSTM) recurrent neural network. Next, the nozzle positions are designed based on canopy structure characteristics, and the canopy area is divided to obtain spraying units. Then, combining the spraying units with wind speed and dosage information, a multi-dimensional prescription map is generated. Finally, the prescription map’s throttle and solenoid valve control strategies are tested and optimized using Hardware In Loop (HIL) methods. The prescription map was applied to orchard spraying, and the experimental results demonstrated that in the deposition monitoring area, the droplet deposition on the front part of the canopy achieved excellent results,. In the rear part of the canopy, 85.7 % of the areas had droplet deposition amounts exceeding 1.2 μL cm⁻². The final experimental results verified the feasibility of the multi-dimensional prescription map, providing theoretical support for the development of intelligent air-assisted spraying equipment for orchards.