  Obtaining in situ measurements of biological microparticles is crucial for
both scientific research and numerous industrial applications (e.g., early
detection of harmful algal blooms, monitoring yeast during fermentation).
However, existing methods are limited to offer timely diagnostics of these
particles with sufficient accuracy and information. Here, we introduce a novel
method for real-time, in situ analysis using machine learning assisted digital
inline holography (DIH). Our machine learning model uses a customized YOLO v5
architecture specialized for the detection and classification of small
biological particles. We demonstrate the effectiveness of our method in the
analysis of 10 plankton species with equivalent high accuracy and significantly
reduced processing time compared to previous methods. We also applied our
method to differentiate yeast cells under four metabolic states and from two
strains. Our results show that the proposed method can accurately detect and
differentiate cellular and subcellular features related to metabolic states and
strains. This study demonstrates the potential of machine learning driven DIH
approach as a sensitive and versatile diagnostic tool for real-time, in situ
analysis of both biotic and abiotic particles. This method can be readily
deployed in a distributive manner for scientific research and manufacturing on
an industrial scale.
