Abstract: Path planning is one of the most important procedures in weeding machines at present. However, the small-scale intelligent machine can often be confined to crop damage and low efficiency caused by the imperfect path planning of the weeding components. In this study, adaptive path planning was proposed for the seedling-avoiding weeding operation using a heuristic search strategy. The soil-cultured lettuce was utilized as the experimental subject. A bidirectional parallel drive mechanism was concurrently designed to enhance the functionality of the weeding machine. A weeding platform was then constructed to integrate an image acquisition with the bidirectional parallel drive mechanism. The Denavit-Hartenberg (D-H) parameters were employed to perform the kinematic analysis. The precise control was realized for the movement of the weeding components. The crop damage was minimized for the effective removal of the weed. The YOLOv8n-seg model was applied to accurately identify and then extract the edge morphology of the lettuce and weeds from the acquired images. A minimum enclosing circle was delineated to define the lettuce protection zone. While the positions of the weeds were represented, according to their centroid coordinates. As such, clear and concise mapping was obtained for the weed locations in the field. According to the weed positions, adaptive recursive clustering was utilized to partition the weed locations into distinct clusters. A weighted undirected graph was then constructed for the operation path of the weeding component. The centers of the cluster also served as the key points in the graph. This graph-based approach was integrated with the A* (A-Star) heuristic search strategy. The shortest path planning was successfully achieved in the weeding components under seedling-avoiding conditions. The weeding machine was efficiently navigated around the protected lettuce areas to target weed clusters. The simulation was conducted to validate the effectiveness and adaptability of the path planning under various weed distributions. Additionally, a series of experiments were also carried out to compare with the conventional path planning under the identical weed distribution. The simulation results demonstrated that the optimal path planning was achieved in the coverage of the weed growth areas with the shorter paths across different weed distributions, indicating its strong adaptability and efficiency. The lengths of the operation path were then ranked in the ascending order of: the proposed, the Spiral, the Zigzag, and the BCD algorithm. Specifically, the path planning outperformed the rest under discrete weed distribution with grid coverage rates of 20%, 40%, and 60%. Furthermore, the path lengths were reduced by 54.6%, 53.3%, and 60.5%, respectively, compared with the BCD algorithm. The reduced length of the path enhanced the operational efficiency to minimize fuel consumption and wear and tear on the weeding machinery. Experimental results further verified the simulation findings. The adaptive seedling-avoiding weeding was achieved at a weeding rate of 93.91%, indicating a high-level removal of the weed. Importantly, the crop damage rate was maintained at 0, indicating the high precision to protect the cultivated lettuce from unintended harm. Additionally, the average weeding duration per unit (defined by the spatially adjacent lettuce plants in the row and column directions) was measured to be 14.2 s, indicating the high operational speed. The adaptive mode enhanced the operational efficiency by 42.28%, compared with the seedling-avoiding exhaustive weeding. There was promising potential for practical application in agricultural settings. The advanced image processing was integrated with precise kinematic control and path planning algorithms. A robust framework was then provided to improve the weeding performance of the small-scale intelligent machines. The heuristic search strategies were combined with adaptive clustering and precise motion control. The adaptive path planning of the seedling-avoiding weeding can be expected to reduce crop damage for high efficiency during mechanical weeding. In conclusion, the findings can offer a valuable technical reference for high-quality mechanical weeding in the field. The significant promise can greatly contribute to more sustainable and productive farming using intelligent weeding machinery.