It is of great value to research the problem of forest pest and disease control. Currently, helicopters play an important role in dealing with this problem. However, the spraying route planning still depends on the pilot’s driving experience, which leads to low efficiency and less accurate coverage. For this reason, this paper attempts to use intelligent algorithms to plan the pesticide spraying route for helicopters. When the helicopter is conducting spraying operations in multiple forest areas, the routes are divided into two parts: pesticide spraying routes for individual forest areas and dispatch routes between multiple forest areas. First, the shorter spraying route with fewer turnarounds for individual forest areas was determined. Then a two-layer intelligent algorithm, a combination of a genetic algorithm (GA) and ant colony optimization algorithm (ACO), was designed to determine the dispatch route between multiple forest areas, which is referred to as GAACO-GA. The performance was evaluated in self-created multiple forest areas and compared with other two-layer intelligent algorithms. The results show that the GAACO-GA algorithm found the shortest dispatch route (5032.75 m), which was 5.60%, 5.45%, 6.54%, and 4.07% shorter than that of GA-GA algorithm, simulated annealing-GA (SA-GA) algorithm, ACO-GA algorithm, and particle swarm optimization-GA (PSO-GA) algorithm, respectively. A spraying experiment with a helicopter was conducted near Pigzui Mountain, Huai’an City, Jiangsu Province, China. It was found that the flight path obtained from the proposed algorithm was 5.43% shorter than that derived from a manual planning method. The dispatch route length was reduced by 16.93%, the number of turnarounds was reduced by 11 times, and the redundant coverage was reduced by 17.87%. Moreover, helicopter fuel consumption and pesticide consumption decreased by 10.56% and 5.43%, respectively. The proposed algorithm can shorten the application route, reduce the number of turnarounds and the cost of spraying operations, and has the potential for use in spraying operations in smart forestry and agriculture.

LiDAR technology has been widely used in forest survey and research, but the high-resolution point cloud data generated by LiDAR equipment also pose challenges in storage and computing. To address this problem, we propose a point cloud simplification method for trees, which considers both higher similarity to the original point cloud and the area of the tree point cloud. The method first determines the optimal search neighborhood using the standard deviation of FPFH information entropy. Based on FPFH information entropy and Poisson disc sampling theory, the point cloud is partitioned and sampled. By optimizing the separation thresholds of significant feature points and less significant feature points using a genetic algorithm with the Hausdorff distance and point cloud area as the objective function, the final simplified point cloud is obtained. Validation with two point cloud data sets shows that the proposed method achieves good retention of the area information of the original point cloud while ensuring point cloud quality. The research provides new approaches and techniques for processing large-scale forest LiDAR scan point clouds, reducing storage and computing requirements. This can improve the efficiency of forest surveys and monitoring.

Analysing the penetration and droplet deposition characteristics in the canopy of fruit trees is critical for optimising the operational parameters of air-assisted spraying equipment, achieving precise application of chemicals, and improving the effectiveness of fruit tree pest and disease control. We used a mobile LIDAR system to detect the tree canopy characteristics and optical porosity and conduct wind tunnel experiments to investigate the interaction between the tree canopy, the airflow field, and the droplet penetration ratio in the canopy of fruit trees. The results show that the relative wind velocity decreases rapidly during canopy penetration, and that the minimum value occurs at the back of the canopy. The smaller the optical porosity, the greater the reduction in wind velocity is. The quadratic exponential regression model had the highest coefficient of determination (R²) (0.9672) and the lowest root mean square error (RMSE) (5.56%). This paper provides information on optimising the spraying parameters, improving the pesticide utilisation rate, and selecting the optimum spraying conditions and application parameters.

Multi-rotor plant protection Unmanned Aerial Vehicles (UAVs) have suitable terrain adaptability and efficient ultra-low altitude spraying capacity, which is a significant development direction in efficient plant protection equipment. The interaction mechanisms of the wind field, droplet, and crop are unclear, and have become the bottleneck factor restricting the improvement of the deposition quality. This paper suggests a method to study the influence of the pesticide load on the detailed distribution law of downwash for a six-rotor UAV. Based on a hexahedral structured mesh, a 3D numerical calculation model was established. Analysis showed that the relative errors between the simulated and measured velocities in the z-axis were less than 11% when the downwash air flow was stable. Numerical simulations were carried out for downwash in hover under 0, 1, 2, 3, 4, and 5 kg loads. The effect of load on the airflow was evident, and the greater the load was, the higher the wind speed of downwash would be. Then, the influence of wing interference on the distribution of airflow would be more pronounced. Furthermore, under the rotation of the rotor and the extrusion of external atmospheric pressure, the “trumpet” phenomenon appeared in the downwash airflow area. As an extension, the phenomenon of the “shrinkage–expansion” was shown in the longitudinal section under heavy load, while the phenomenon of “shrinkage–expansion–shrinkage” was present under light load. After that, based on the detailed analysis of the downwash wind field, the spray height of this multi-rotor UAV was suggested to be 2.5 m or higher, and the nozzle was recommended to be mounted directly under the rotor and to have the same rotation direction as the rotor. The research in this paper lays a solid foundation for the proposal of the three-zone overlapping matching theory of wind field, droplet settlement, and canopy shaking.

Under complex climatic conditions, variable application parameters and a two-dimensional application route, it is difficult to ensure the accurate deposition of pesticide droplets during helicopter aerial applications. This is especially true when the deposition area is forested. The Agricultural Dispersal (AGDISP) model was used with an optimization procedure to study the influence of flight height, flight speed, and ambient wind speed. Optimization techniques were used to obtain the best fit between the simulation results. Three objectives were used to propose a new application strategy, namely (i) the average deposition in the forest area; (ii) the uniformity of droplet deposition; and (iii) the deposition at a distance of 50 m downwind outside the forest area. A new application strategy was proposed, where the forest area was divided into two subareas, namely the safe area (the area away from forest boundaries) and the edge area (the area close to forest boundaries). Flight height and speed were adjusted to ensure the desired average deposition and uniformity in the safety area and the desired deposition at 50 m downwind in the edge area. Six helicopter spraying experiments at different wind speeds were conducted at Longtan, Nanjing, China. The deposition effects of the new strategy were compared with those of the common manual empirical method (operating at the same height and speed over the whole forest). It was found that at wind speeds of 2 m/s, 1 m/s, and 2.5 m/s, the average deposition in the safe area was improved by 4.82%, 0.91%, and 8.24%, respectively, and that in the edge area, it was improved by 7.04%, 0.90%, and 0.77%, respectively. Conversely, the deposition at 50 m downwind was reduced by 25.00%, 16.58% and 22.90%, respectively. These experimental results demonstrated that the new strategy can effectively reduce the droplet drift. We achieved the synergistic optimization goal of moderate (and uniform) deposition in the forest area with low deposition outside the forest area. This study can provide important technical references for precision forestry.