To enhance the management of waterweed in crab farming ponds， this paper integrates and optimizes image processing methods and the path planning of waterweed clearing boat，aiming to develop a high-quality waterweed control strategy.The research employs unmanned aerial vehicle （UAV） photogrammetry to acquire multi-temporal imagery of crab ponds， and proposes a refined YOLO11n-seg network architecture. Specifically， the Neck stage is enhanced with a lightweight High-level Screening Path Aggregation Network-Dysample （HSPAN-D） module incorporating dynamic upsampling operators， the original C3k2 module is replaced with a lightweight feature extraction module C3k2_Faster_EMA， and an EfficientHead lightweight segmentation head is introduced. Based on model recognition and segmentation outputs， a raster map of the crab pond is constructed， and a target waterweed clearing area screening mechanism is designed. An improved A* algorithm is then developed through path optimization strategies to enable precise waterweed clearing path planning. Results demonstrate that the enhanced model achieves a 1.6% improvement in waterweed recognition precision and a 0.5% increase in mean Average Precision （mAP）， while reducing parameter count by 39.4%， computational overhead by 25.5%， and model size by 34.5%.The improved A* algorithm exhibits more precise control over waterweed area coverage compared with manual clearing paths. Relative to the conventional A* algorithm， it reduces total path length by 14.25 meters， decreases cleaning boat turning maneuversby 10 instances， and shortens average planning time by 1.97 seconds.The research confirms that the proposed lightweight enhancement strategy significantly reduces computational burden while improving recognition accuracy. Coupled with the improved path planning algorithm， it enables efficient and precise waterweed clearing in crab ponds. This study provides a valuable path planning reference for practical operations of waterweed clearing boat.