Swift Automated System for Distinguishing Blue-White Colonies Post Bacterial Transformation on Agar Plates Using Computer Vision Techniques.

The manual selection of transformed bacterial colonies from non-transformed ones grown on agar plate post-blue-white screening -- despite chromogenic difference -- is cumbersome, owing to their small size and large cell number. The present study offers a lucrative solution to this problem by the design of an automated system that is not only fast and less laborious but also low priced and user-friendly. The image masking technique was used to distinguish plated transformed colonies. This method uses computer vision techniques to detect the number of blue and white colonies post bacterial transformation on agar plates and calculate the transformation efficiency. To assess the proposed model with the manual counting method, we have validated the model by comparing the manual counting of colonies with the automated system count. Hence, a model was developed that would be an added advantage to biotechnologists as it would minimize the time required for counting and help in productive research. [ABSTRACT FROM AUTHOR]