Microbiology laboratories are increasingly adopting automated imaging technologies as means to leverage their productivity and increase traceability and objectivity of test results. Specifically, colony enumeration and detection of color development in these stand as the two most common applications of machine vision in microbiology laboratories. In the advent of the age of computer vision, colony counting and analysis stands as yet another process that can be automated by means of image-driven artificial intelligence. The present work assesses the capacity of flatbed scanners to capture images for the detection and measurement of color development in colonies. Effects of different concentrations of chromogens and the differences in color development over time are evaluated. Affordable approaches to interpret derived data are suggested and insights related to the analysis of color development are supplied. Metrological aspects of the measurement technique are duly addressed. Thus, particular care is devoted to characterize the measurement technique employed, to highlight its limitations, and to assess the cross-device reproducibility of obtained results. First-in-class accounts of enumeration of colonies in alternative culture media, based on kinetic imaging of their growth, are also reported. Furthermore, time to the earliest detection of colonies is evaluated, along with colony recovery evaluation, as a means to assess stress induction related to the presence of potentially toxic matrices.