<p>This paper investigates the problem of coverage path planning (CPP) for the spraying drone operating in complex two-dimensional regions with obstacles. The drone must depart from a designated start point, completely cover a region of interest, and land at a specified end point while avoiding collisions. A basic mathematical model for the CPP of the spraying drone is formulated first. Because CPP is a combinatorial optimization problem whose computational cost increases significantly with fine-grained discretization and obstacle density, achieving high-quality solutions within operational time limits requires efficient and scalable computation. To address this challenge, a hybrid heuristic method (HHM) is proposed, which integrates global path construction with local refinement to enable efficient exploration of the solution space while supporting high-performance and real-time execution. The method is evaluated on 44 instances, including convex polygons, concave polygons, and polygons with obstacles. Experimental results demonstrate that HHM consistently outperforms state-of-the-art approaches in solution quality while maintaining short computation times, highlighting its suitability for large-scale and time-sensitive applications.</p>