The metachromatic coloration of volutinous granules of the yeast Saccharomyces cerevisiae is one of the indicators of the influence of sharp geomagnetic field (GMF) perturbations. The metachromasia reaction is based on the aggregation of dye molecules in interaction with inorganic polyphosphates, which are components of volutinous granules. To determine the characteristics of the geomagnetic field that cause the appearance of different colors of the metachromasia reaction, it is necessary to simultaneously monitor this reaction and changes in the GMF. High-quality monitoring is possible with rapid automated counting of cells with all possible color changes during the metachromasia reaction. The aim of the work was to develop a neural network architecture for recognizing and quantifying color changes and heterogeneity in real time during monitoring of the metachromasia reaction of volutinous granules of the yeast S. cerevisiae, which is necessary for further determining their correlations with changes in the geomagnetic field of different intensities. A program based on a nonrecursive labeling algorithm was created to count the number of cells in the study groups. In the course of the work, the software of two neural network architectures was compared to determine the best results in recognizing and quantifying yeast cells with different colors during the volutinous granule metachromasia reaction. It was determined that the Unet architecture type coped with the tasks of cell classification and segmentation much more efficiently than the Inception v3 architecture. The average relative error for automatic recognition of all cell groups was 3.85%, and the maximum relative error was 4.56%. The performance of the neural network was 89.9% when detecting cell segmentation and 86.4% when detecting color differences in the metachromasia reaction.
