Identification and morphological analysis of microorganisms are of high interest in scientific research, especially in the medical field and food industry. Identification allows rapid functional characterization based on similarities with known related species enabling to confirm the identity of an isolate used, for example, in a trademarked industrial process. Monitoring of microorganisms within a given ecosystem and analysis of the morphological characteristics of the observed species enable quality control of the process under analysis. Such procedures are carried out manually in specialized laboratories by trained personnel using the appropriate optical equipment; therefore, it may be of great interest to use automatic measurement approaches that enable rapid and effective process analysis. Artificial intelligence techniques in computer vision and especially deep learning are well suited for this purpose. This article describes the realization of an automatic measurement system based on deep learning for the identification and measurement of morphological parameters of Saccharomyces cerevisiae microorganisms present in brewer's yeast by returning for each of the objects identified within the image the confidence score, the coordinates, and the dimensions of the corresponding ellipsoid-shaped cell. The metrological characteristics of the system have been defined through a calibration process by comparing measurements with a reference system.

19^th IMEKO TC10 Conference "Measurement for Diagnostics, Optimisation and Control to Support Sustainability and Resilience"