Development and Application of a High-Throughput Microbial Clone Picking Workstation Based on Machine Vision Synthetic Biology Journal    DOI: 10.12211/2096-8280.2025-038

Fig. 9 Identification and labeling of Petri dishes

Extracts from the Article

为了保证机械运行系统在挑取菌落时处于安全状态，避免与培养皿边缘碰撞，软件系统首先对培养皿的内外边缘进行识别（图7），确保挑针在培养皿内边缘内运行，同时只识别培养皿内的菌类提升工作效率。培养皿的识别与菌类识别采用相同的ResUNet++语义分割网络模型，挑选圆形和方形培养皿训练样本量132张，图像resize为256×256大小，直接输入到模型中，对训练过程调整了训练参数，准确率和loss曲线如图8所示。测试培养皿52张，计算测试集语义分割的IOU，取大于0.70的阈值计算准确率达到100%。培养皿分割模型针对真实图像分割的效果图如图9所示。

Other Images/Table from this Article

• Fig. 1  Overall form of clone selection workstation
• Fig. 2  System composition of clone selection workstation
• Fig. 3  Electrical schematic diagram
• Fig. 4  Work area arrangement of clone selection workstation
• Fig. 5  Problems existing in colony images that affect recognition
• Fig. 6  Flowchart of colony identification and location
• Fig. 7  Petri dish
• Fig. 8  Dish trainning curve
• Table 1  Distribution of the dataset
• Fig. 10  Colony-training curve
• Fig. 11  Grayscale image generated by reasoning
• Fig. 12  Identification of qualified colonies
• Fig. 13  UI design
• Fig. 14  Control interface framework of the workstation
• Fig.15  Clone selection process
• Fig. 16  Image comparison before and after picking
• Fig. 17  Deep well plate containing colony culture medium
• Fig. 18  Verification of sterilization effect
• Table 2  A comparison of technical parameters of this device with other three cloning selection workstations