| Citation: | ZHAO Xu-feng, JIA Zhi-qiang, CHEN Wei-xue, HU Peng-tao, SU Xin-ran, LI Jun-lin, GE Ming-feng, DONG Wen-fei. Artificial intelligence-enabled high-precision colony extraction and isolation system[J]. Chinese Optics. doi: 10.37188/CO.EN-2025-0025
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Standard bacterial suspensions play a crucial role in microbiological diagnosis. Traditional preparation methods, which rely heavily on manual operations, face challenges such as poor reproducibility, low efficiency, and biosafety concerns. In this study, we propose a high-precision automated colony extraction and separation system that combines large-field imaging and artificial intelligence (AI) to facilitate intelligent screening and localization of colonies. Firstly, a large-field imaging system was developed to capture high-resolution images of 90 mm Petri dishes, achieving a physical resolution of 13.2 μm and an imaging speed of 13 frames per second. Subsequently, AI technology was employed for the automatic recognition and localization of colonies, enabling the selection of target colonies with diameters ranging from 1.9 to 2.3 mm. Next, a three-axis motion control platform was designed, accompanied by a path planning algorithm for the efficient extraction of colonies. An electronic pipette was employed for accurate colony collection. Additionally, a bacterial suspension concentration measurement module was developed, incorporating a 650 nm laser diode as the light source, achieving a measurement accuracy of 0.01 McFarland concentration (MCF). Finally, the system’s performance was validated through the preparation of an E. coli suspension. After 17 hours of cultivation, E. coli was extracted four times, achieving the target concentration set by the system. This work is expected to enable rapid and accurate microbial sample preparation, significantly reducing detection cycles and alleviating the workload of healthcare personnel.
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