An Improved Hierarchical Leaf Density Model for Spatio-Temporal Distribution Characteristic Analysis of UAV Downwash Air-Flow in a Fruit Tree Canopy
Abstract
1. Introduction
2. Materials and Methods
2.1. Governing Equations
2.2. Construction of the Improved Hierarchical Leaf Density Model
Algorithm 1. Detailed calculation flow. |
Inputs: raw tree point cloud data, raw branch stem data, leaf season data, k, T1, T2, Se Outputs: lad, validation_result |
# Step 1: Point Cloud Data Acquisition and Processing 1:def acquire_point_cloud_data(): 2: leafy_tree_data = capture_leafy_season_tree_data("leafy-season") 3: branch_stem_data = capture_leafless_tree_data("leafless") 4: filtered_data = filter_point_cloud_data(leafy_tree_data, branch_stem_data) 5: return filtered_data # Step 2: Measured LAD using Specific Leaf Weight Method 6:def measured_lad_using_leaf_weight_method(filtered_data) 7: branches_by_layer = count_branches_by_layer(filtered_data) 8: selected_branches = random_select_branches(branches_by_layer) 9: leaf_count = count_leaves_on_selected_branches(selected_branches) 10: punched_leaves = randomly_select_and_punch_leaves(leaf_count) 11: leaf_area = calculate_leaf_area(punched_leaves) 12: lad_with_canopy_coverage = calculate_lad_with_canopy(leaf_area) 13: return lad_with_canopy_coverage # Step 3: Voxel Method for Calculating Leaf Area Density (LAD) 14:def voxel_method_for_calculating_lad(filtered_data): 15: voxel_grid = segment_point_cloud_into_voxels(filtered_data, delta_i, delta_j, delta_s) 16: lad = calculate_lad_using_hosoi_omasa(voxel_grid) 17: return lad # Step 4: Validation 18:def validate_lad(lad_from_leaf_weight, lad_from_voxel_method): 19: validation_result = validate_lad(lad_from_leaf_weight, lad_from_voxel_method) 20: return validation_result |
2.3. Rotor Model and Simulated Fruit Tree Canopy Structure
2.4. Boundary Conditions and Computational Methods
2.5. Computational Method
2.6. Field Test
3. Results and Discussion
3.1. Analysis of the Temporal Variation Characteristics of Downwash Airflow
3.2. Analysis of the Variation Scale of Turbulence
3.3. Experimental Verification and Analysis
4. Conclusions
- (1)
- This study systematically investigated the spatio-temporal characteristics of UAV-induced downwash airflow and established a refined numerical simulation model to describe airflow velocity distributions within fruit tree canopies. The simulation revealed that the downwash airflow initially forms a cylindrical column, then disperses in a branching pattern upon impacting the canopy, eventually producing a ground effect. Among the tested configurations, a flight height of 3 m was found to deliver the most effective canopy penetration. This height balances the airflow impact intensity and turbulence structure development, ensuring improved interaction with the internal canopy structure.
- (2)
- An analysis of turbulence dynamics under varying flight altitudes showed that lower operating heights generate stronger turbulence and more pronounced recirculation, with intensified shear interactions near the rotor. In contrast, increasing the flight altitude reduces the size of turbulent structures and weakens the flow disturbance. The Y-direction velocity profile further confirmed that alternating positive and negative gradients near the rotor serve as the key mechanism for turbulence generation.
- (3)
- Validation results demonstrated the superior performance of the improved hierarchical leaf density porous model compared to the conventional porous model. At t = 3.5 s and a 3 m flight height, the average error between the simulation and orchard measurements was only 8.4%, significantly lower than the 14.51% error observed with the conventional model. Moreover, the coefficients of variation (CVs) in the middle and lower canopy layers were 0.26 and 0.29, respectively, indicating good agreement between simulated and measured airflow distribution uniformity. These findings confirm the improved model’s effectiveness in capturing real-world canopy airflow dynamics and provide a reliable reference for UAV operation optimization in precision agriculture.
- (4)
- Future research should further explore the application of this model framework under strong wind conditions, focusing on simulating the interaction between UAV downwash airflow and external wind disturbances. Such studies could provide insights into airflow deflection, penetration attenuation, and overall airflow stability within canopies, thereby enhancing the robustness of UAV operation strategies in open-field environments.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Main Equipment | Specifications and Values |
---|---|
Infrared Photoelectric Sensor | E18-D80NK |
Response Time | <2 ms |
Detection Distance | 3–80 cm |
LoRa Module | L32-433 |
Transmission Distance | 6 km |
Communication Interface | Usart |
Peak Data Transmission Rate | 76.8 kbps |
testo 405i | 0–30 m/s 0.01 m/s (resolution) 1 S < (response Time) ± (0.1 m/s + 5%) |
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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Fu, S.; Ren, N.; Liu, S.; Shao, M.; Jiang, Y.; Du, Y.; Zhang, H.; Sun, L.; Zhang, W. An Improved Hierarchical Leaf Density Model for Spatio-Temporal Distribution Characteristic Analysis of UAV Downwash Air-Flow in a Fruit Tree Canopy. Agronomy 2025, 15, 1867. https://doi.org/10.3390/agronomy15081867
Fu S, Ren N, Liu S, Shao M, Jiang Y, Du Y, Zhang H, Sun L, Zhang W. An Improved Hierarchical Leaf Density Model for Spatio-Temporal Distribution Characteristic Analysis of UAV Downwash Air-Flow in a Fruit Tree Canopy. Agronomy. 2025; 15(8):1867. https://doi.org/10.3390/agronomy15081867
Chicago/Turabian StyleFu, Shenghui, Naixu Ren, Shuangxi Liu, Mingxi Shao, Yuanmao Jiang, Yuefeng Du, Hongjian Zhang, Linlin Sun, and Wen Zhang. 2025. "An Improved Hierarchical Leaf Density Model for Spatio-Temporal Distribution Characteristic Analysis of UAV Downwash Air-Flow in a Fruit Tree Canopy" Agronomy 15, no. 8: 1867. https://doi.org/10.3390/agronomy15081867
APA StyleFu, S., Ren, N., Liu, S., Shao, M., Jiang, Y., Du, Y., Zhang, H., Sun, L., & Zhang, W. (2025). An Improved Hierarchical Leaf Density Model for Spatio-Temporal Distribution Characteristic Analysis of UAV Downwash Air-Flow in a Fruit Tree Canopy. Agronomy, 15(8), 1867. https://doi.org/10.3390/agronomy15081867