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. 12 Identification of qualified colonies

Extracts from the Article

根据菌落语义分割模型生成的灰度图象，计算图像中每个菌落的形态参数，主要参数包括：菌落大小、圆形度、邻近度、轴比等参数。依据系统的需求建立菌落辨识参数阈值。根据形态参数阈值辨识出合格菌落、不合格菌落（半径不合格、圆形度不合格、邻近度不合格等）用不同颜色标示，图12中绿色小圈表示合格菌落。此外，挑针对不同大小菌落的挑取精度和准确度与挑针模组X、Y、Z的运动精度有关，关于精度已在文中运动机构部分给出数值，可满足挑针在邻近度参数阈值内精确挑取。

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
• Fig. 9  Identification and labeling of Petri dishes
• Table 1  Distribution of the dataset
• Fig. 10  Colony-training curve
• Fig. 11  Grayscale image generated by reasoning
• 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