Abstract: Tissue culture rapid propagation cannot be reduced the production that is caused by environmental factors, compared with traditional flower cultivation. Since phalaenopsis flowers are very popular for their unique appearance, tissue culture rapid propagation can be expected to cultivate phalaenopsis for the high cultivation speed in industrial production. Current phalaenopsis rapid propagation cannot fully meet the large-scale production in recent years, due to the high repetition and labor consumption. Therefore, it is in high demand for automated phalaenopsis tissue-cultured seedlings' rapid propagation. Among them, image recognition can be used to detect the picking points of seedlings in the process of the robot arms. However, current algorithms of image recognition are only applicable to the specific growth stages, due to the differences in the image characteristics of seedlings at different stages. This study aims to improve the adaptability and efficiency of seedling grasping point detection. A seedling grasping point localization was also proposed using the improved U²-Net salient detection network (MBU²-Net⁺). A seedling grasping end effector was then designed. The overall steps of seedling grasping included visual detection, grasping point localization, and a robotic arm control module. Firstly, industrial cameras were used to capture the images of seedlings. The salient map of the seedlings was obtained after the MBU²-Net⁺ salient detection network. Secondly, the salient map was processed by filtering, while the skeleton extraction was to extract the intersection points of the skeleton lines. Then, the intersection points were clustered using K Nearest Neighbours (KNN) to remove the salient outliers. The grasping angle was obtained to locate the seedling grasping point using morphological analysis after fitting the line using Random Sample Consensus (RANSAC). Finally, the grasping point location data was sent to the robotic arm for grasping. The salient detection experiment was carried out to compare MBU2-Net⁺ with Res2Net-PoolNet, U²-Net, and U²-Net⁺. The average absolute error of MBU²-Net⁺ was 0.002, the maximum F₁ score was 0.993, the FPS was 33.99, and the model weight size was 2.37MB. All of these metrics were optimal. The success rate of grasping point detection of 112 seedlings in four groups was 85.71% in the grasping point of seedling detection experiment using MBU²-Net⁺. The failure of grasping point extraction was attributed to the complex structure of certain seedlings, indicating a significant number of mature leaves and roots. Consequently, the intersections in the obtained skeleton image deviated significantly from the actual locations of roots or leaves, leading to grasping points out of the appropriate regions. Six seedlings were selected to verify the adaptability from the mother bottle to the intermediate bottle stage, three seedlings from the intermediate bottle to the daughter bottle stage, and two virtual seedlings in the rapid propagation process of phalaenopsis. The overall success rate was 81.82%. A high success rate was achieved to extract the grasping points for the orchid seedlings in the entire process of orchid tissue culture. Furthermore, a certain level of generalization was suitable for the other types of seedlings with similar structures. In summary, the capability of the improved model can be expected to effectively identify the grasping points for the orchid seedlings in the entire process of orchid tissue culture, even with the limited datasets. The adaptability and efficiency were significantly enhanced to detect the grasping points for new seedlings. The findings can serve as a technical reference in the development of automated and rapid propagation techniques for tissue-cultured seedlings.