Manual or mechanized tree planting typically follows final felling to ensure satisfactorilyregrowth of the new forest stand. As part of quality assurance, inventories are conductedsome time after planting to verify properly planted seedlings and to gather forest standplanting statistics, which are both costly and time consuming. With the advent of AI andfaster image processing, several methods are now available that can automate theassessment of replanted tree seedlings and objectively quantify planting quality. In thisstudy, we used a dataset consisting of over 3000 stereo images and corresponding depthmaps, annotated with seedling length, position and tilt, as well as classifications of plantingspots, site preparation methods, and many other parameters relevant for qualityassessment. Using computer vision, we evaluated both rule-based approaches incombination with trained AI models to find suitable methods for automating plantingquality assessment. The annotated dataset was used to validate the different estimationmethods. First, we developed a model that automatically detected seedlings in image data.The outcome was used to evaluate the presence of vital, living seedlings. Next, we were ableto discern whether the planting spot were positioned favourably using depth data as input.These findings suggest that image and depth data can be utilized to assess planting qualityin a stand, either after manual planting or during mechanized planting, providing valuablefeedback to the system or operator. Drone overflights may also serve as a suitable methodfor this assessment. In recent years, research has indicated that autonomous forestregeneration may be achievable in the near term. The results of this study may acceleratethis development, as planting quality assessment is essential when such autonomoussystems operate independently of human oversight.