For many years, a FlowCam flow imaging microscope (Yokogawa Fluid Imaging Inc., USA) has been used to study communities of marine organisms – their species composition, structure, and dynamics, as well as morphological characteristics of individual organisms. However, the problem of standardizing protocols for using these types of instruments in the field of visualization cytometry for processing plankton samples remains unsolved. The aim of the study was to develop a methodology for zooplankton image classification using VisualSpreadsheet tools – the software that controls the system operation. For the first time, an algorithm for automatic search of value filters was developed, and the ordering of class tabs in the VisualSpreadsheet classification window was formalized. The proposed method was applied to native samples of Black Sea zooplankton and, under certain conditions, enabled the correct classification of 100% of bivalve larvae (class mollusca) and the dinoflagellate Noctiluca scintillans (class noctiluca), 82% of the appendicularian Oikopleura dioica (class oikopleura), 71.2% of naupliar (larval) stages (class nauplius), and up to 70.2% of calanoid and cyclopod (class copepoda). Overall, the described approach can be used for any type of object studied with FlowCam, especially those with a complex morphology.
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