基于显著性特征的蝴蝶兰组培苗夹取点检测方法

为了提高蝴蝶兰自动化快速繁育过程中组培苗夹取点视觉检测的适应性和效率，该研究提出了一种基于改进U²-Net显著性检测网络（MBU²-Net⁺）的组培苗夹取点定位方法。首先，通过显著性检测网络得到蝴蝶兰组培苗的显著性图像；然后，对显著性图像进行骨架提取，并经过形态学分析计算定位组培苗夹取点；最后，将夹取点位置数据发送给机械臂进行夹取。在图像显著性检测试验中，MBU²-Net⁺的平均绝对误差为0.002，最大F₁分数为0.993，FPS（frames per second，每秒帧率）为33.99 帧/s，模型权重大小为2.37 MB；在组培苗夹取试验中，4组共112颗苗的夹取点提取成功率为85.71%。为验证该研究的适应性，将其应用于各阶段组培苗以及部分虚拟两叶苗共11株种苗的夹取点提取，成功率为81.82%，使用该方法对不同时期的蝴蝶兰组培苗进行夹取点检测，具有较高的成功率。研究结果可为发展组培苗自动化快速繁育技术提供参考。

Pinch point extraction method for phalaenopsis tissue-cultured seedlings based on salient features

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.