Enhanced Black-Winged Kite Algorithm for Drone Coverage in Complex Fruit Farms
Abstract
:1. Introduction
- Utilizing the deep learning SAM model to segment the task area in fruit farms and creating an environmental map via gridding;
- Designing a new objective function by constructing the coverage task cost functions, the flight safety cost function, and the path length cost function, thereby restructuring the drone coverage issue as an optimizing problem with multi-constraints;
- Proposing a DWBKA that incorporates a Dynamic Position Balancing strategy and a modified Whale Random Walk mechanism to avoid local optima traps and augment its global search capability;
- Developing a drone coverage approach anchored in the DWBKA algorithm to plan near-optimal pesticide spraying flight paths in complex fruit farm environments.
2. Materials and Methodology
2.1. Study Area Description
2.2. Grid-Based Planning Area
2.3. Mathematical Model of Drone Coverage Path Planning
2.3.1. Representation of Flight Paths
2.3.2. Construction of the Objective Function
- Flight Safety Cost Function
- 2.
- Coverage Task Cost Function
- 3.
- Path Length Cost Function
- 4.
- Overall Function for CPP
2.4. Black-Winged Kite Algorithm
2.4.1. Population Initialization
2.4.2. Attack Behavior
2.4.3. Migration Behavior
2.5. Enhancement of Algorithm
2.5.1. Dynamic Position Balancing Strategy
2.5.2. Modified Whale Random Walk Strategy
2.6. Flow of the Proposed Path Planning Method
- Step 1: The algorithm commences by constructing an environmental map using the grid-based method. The starting point of the UAV, the set of obstacle grids , the set of free grids , the set of coverage tasks , and the total number of coverage task grids are initialized;
- Step 2: The fundamental parameters of BKA population are initialized. These include the population size , the iteration counter , the maximum iterations , and the initial positions of all individuals inside the solution space;
- Step 3: Iterations begin. The fitness values of all individuals in the population are calculated, and the best individual is identified;
- Step 4: The iteration counter is incremented (). If , advance to Step 5. If not, continue to Step 6;
- Step 5: Attack behavior. A number is produced randomly in the range of 0 to 1. If , the original attack strategy (Equation (12)) is employed to modify the individual’s position. Otherwise, the Dynamic Position Balancing mechanism (Equation (16)) is used to update the individual’s position;
- Step 6: Migration behavior. The current individual’s fitness value is computed. If is inferior to a randomly chosen individual’s fitness value , the original migration strategy (Equation (14)) is used to modify the individual’s position. Otherwise, the modified Whale Random Walk strategy (Equation (19)) is employed to update the individual’s position;
- Step 7: The best individual position (optimal solution) and its fitness value are calculated according to Equation (8);
- Step 8: The algorithm undergoes verification of its termination condition. If the iteration counter is more than , proceed to Step 9. Otherwise, return to Step 3;
- Step 9: The algorithm concludes by outputting the value of fitness and the solution of optimum (optimal path).
3. Results and Discussion
3.1. Settings for Experiments
3.2. Results
3.3. Discussion
- In planning the drone’s flight path, this study primarily focused on static threat factors and did not consider sudden situations or unknown local information, such as flocks of birds in the air or wind direction changes, which prevent real-time flight path planning. Adjusting the algorithm to accommodate different real-time factors will be a key focus in future work [73,74].
- Although this study has proposed a CPP algorithm for a single drone, the need for multi-agent collaborative work is becoming increasingly evident with the development of multi-drone systems. Future research can explore collaborative path planning algorithms for multiple drones to enhance overall system efficiency and collaborative performance [75,76].
4. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
BKA | Black-winged Kite Algorithm |
SAM | Segment Anything Model |
DWBKA | BKA with Dynamic Position Balancing and modified Whale Random Walk |
BL-DQN | Deep Q-Network with Bidirectional Long Short-Term Memory |
UAV | Unmanned Aerial Vehicle |
CPP | Coverage Path Planning |
DQN | Deep Q-Network |
WOA | Whale Optimization Algorithm |
HHO | Harris Hawk Optimization |
GWO | Grey Wolf Optimizer |
RDWOA | WOA with Random Spare and Double Adaptive Weight |
CTHHO | HHO with Cauchy Distribution Inverse Cumulative and Tangent Flight |
DGWO | GWO with Dimension Learning-based Hunting |
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Scenario | Algorithms | Coverage (%) | Repeated Coverage (%) | Path Length | Computation Time (s) |
---|---|---|---|---|---|
Scenario 1 | Ours | 100.00% | 0.00% | 66.83 | 75.33 |
BKA | 98.48% | 4.62% | 69.66 | 156.62 | |
BL-DQN | 98.48% | 1.54% | 68.49 | 1016.57 | |
RDWOA | 96.97% | 7.81% | 69.13 | 141.65 | |
CTHHO | 98.48% | 3.08% | 69.24 | 180.27 | |
DGWO | 98.48% | 1.54% | 67.66 | 150.03 | |
Scenario 2 | Ours | 100.00% | 0.00% | 71.41 | 81.31 |
BKA | 97.18% | 4.35% | 73.49 | 159.33 | |
BL-DQN | 97.18% | 2.90% | 73.90 | 1012.03 | |
RDWOA | 97.18% | 2.90% | 72.24 | 153.32 | |
CTHHO | 98.59% | 5.71% | 74.64 | 189.92 | |
DGWO | 98.59% | 4.29% | 72.89 | 162.02 | |
Scenario 3 | Ours | 100.00% | 0.00% | 67.07 | 72.63 |
BKA | 98.46% | 4.69% | 68.72 | 144.50 | |
BL-DQN | 96.92% | 10.17% | 73.97 | 977.55 | |
RDWOA | 96.92% | 6.35% | 68.90 | 141.01 | |
CTHHO | 98.46% | 3.13% | 68.90 | 177.08 | |
DGWO | 98.46% | 3.13% | 68.49 | 153.32 | |
Scenario 4 | Ours | 100.00% | 0.00% | 100.24 | 114.42 |
BKA | 97.98% | 7.22% | 106.02 | 228.01 | |
BL-DQN | 98.99% | 5.32% | 105.07 | 986.32 | |
RDWOA | 96.97% | 8.33% | 106.03 | 219.64 | |
CTHHO | 97.98% | 9.28% | 107.67 | 276.43 | |
DGWO | 97.98% | 5.15% | 104.80 | 228.37 | |
Scenario 5 | Ours | 100.00% | 0.00% | 50.83 | 54.64 |
BKA | 98.00% | 6.12% | 52.06 | 108.40 | |
BL-DQN | 96.00% | 8.70% | 56.56 | 1001.43 | |
RDWOA | 96.00% | 4.17% | 52.14 | 105.44 | |
CTHHO | 100.00% | 8.00% | 55.31 | 132.09 | |
DGWO | 100.00% | 2.00% | 52.66 | 111.31 | |
Scenario 6 | Ours | 100.00% | 0.00% | 61.41 | 66.79 |
BKA | 98.36% | 6.67% | 64.06 | 132.85 | |
BL-DQN | 96.72% | 3.39% | 63.49 | 981.86 | |
RDWOA | 96.72% | 6.78% | 64.37 | 129.66 | |
CTHHO | 98.36% | 8.33% | 65.54 | 165.32 | |
DGWO | 100.00% | 4.92% | 63.24 | 135.93 |
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Li, J.; Fu, S.; Zhang, W.; Fu, H.; Fang, X.; Li, Z. Enhanced Black-Winged Kite Algorithm for Drone Coverage in Complex Fruit Farms. Agriculture 2025, 15, 1044. https://doi.org/10.3390/agriculture15101044
Li J, Fu S, Zhang W, Fu H, Fang X, Li Z. Enhanced Black-Winged Kite Algorithm for Drone Coverage in Complex Fruit Farms. Agriculture. 2025; 15(10):1044. https://doi.org/10.3390/agriculture15101044
Chicago/Turabian StyleLi, Jian, Shengliang Fu, Weijian Zhang, Haitao Fu, Xu Fang, and Zheng Li. 2025. "Enhanced Black-Winged Kite Algorithm for Drone Coverage in Complex Fruit Farms" Agriculture 15, no. 10: 1044. https://doi.org/10.3390/agriculture15101044
APA StyleLi, J., Fu, S., Zhang, W., Fu, H., Fang, X., & Li, Z. (2025). Enhanced Black-Winged Kite Algorithm for Drone Coverage in Complex Fruit Farms. Agriculture, 15(10), 1044. https://doi.org/10.3390/agriculture15101044