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.

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.

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.

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.

Airflow regulation is essential for optimizing pesticide deposition in orchard spraying. This study developed an airflow attenuation model for a five-port air-assisted sprayer by integrating jet dynamics with crown resistance characteristics. The tree crown was modeled as a porous medium with calibrated resistance coefficients, and airflow attenuation was described using momentum conservation theorems, with initial conditions derived from jet decay profiles at the outlet. Validation included free-space airflow measurements and field trials in three fruit tree species. In free-space tests, the model showed a maximum relative error of 22.31% and a mean error of 12.26%. Field tests yielded a maximum error of 25.92%, with mean errors of 14.85% and 15.76% at 2300 and 2800 rpm, respectively. The model provides a theoretical basis for intelligent airflow control aimed at improving deposition and reducing drift.

Forestry helicopter spraying requires fuel-efficient allocation of coverage tasks to successive sorties. Coverage-path generation alone does not resolve this allocation under coupled pesticide depletion and load-dependent fuel consumption. We formulate a Multi-Sortie Routing Problem with Non-synchronous Dual Resources (MSRP-NDR) and develop Sortie-Consolidation Hybrid Adaptive Large Neighborhood Search (SC-HALNS) to minimize modeled fuel consumption under pesticide-capacity and fuel constraints. For fixed coverage paths generated by convex decomposition and the Vector Modeling Method, the framework combines adaptive destroy-and-repair search and randomized variable neighborhood descent, supplemented by whole-sortie consolidation. Only merges that preserve resource feasibility and reduce total modeled fuel consumption are accepted. Six GIS-derived forest instances with 10–102 tasks were evaluated using 20 runs per stochastic algorithm and a nominal 100 s search budget. Relative to the aviation-company practice method (ACPM), SC-HALNS reduced mean modeled fuel consumption by 1.31–7.87%. Exact paired sign tests with Holm correction favored SC-HALNS in 13 of 18 external comparisons. Accepted consolidations reduced returns to the base, potentially easing replenishment workload. These results support offline scheduling under the evaluated model assumptions; reported fuel savings are model-based estimates.