Enhanced Black-Winged Kite Algorithm for Drone Coverage in Complex Fruit Farms

When investigating precision pest management strategies for fruit farmlands with complex geometries and restrictive boundaries, this study proposes an enhanced coverage optimization methodology for agricultural drones based on an enhanced Black-winged Kite Algorithm (BKA). Initially, the task area is segmented using the Segment Anything Model (SAM) based on deep learning, and an environmental map is created through gridding. Subsequently, by proposing coverage task cost functions, flight safety cost functions, and path length cost functions, the coverage challenge in complex-shaped areas is redefined as a challenge involving multiple constraints. To optimize this problem, we introduce a DWBKA that incorporates a Dynamic Position Balancing strategy and a modified Whale Random Walk strategy, thereby enhancing its global search capability and avoiding local optima traps. Finally, comparative experiments are conducted in six distinct scenarios of fruit farms, juxtaposing the DWBKA with the initially developed version and the BL-DQN. The results of this comparative analysis unequivocally demonstrate that the DWBKA achieves superior performance metrics, excelling in coverage rate, repeated coverage rate, path length, and computational time. When compared with extant coverage methodologies for complex shapes, the proposed DWBKA method exhibits marked performance enhancements in coverage tasks. This underscores its potential to significantly elevate the efficiency and precision of drone coverage in complex farm settings.