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An advanced approach to the automated assessment of a microscopic slide containing spores is presented. The objective is to develop an intelligent system for the rapid and precise estimation of phytopathogenic spore concentration on microscopic slides, thereby enabling automated processing. The smart microscopy scanning system comprises an electronic microscope, a coordinate table, and software for the control of the coordinate table and image processing. The developed smart microscopy scanning system processes the entire microscope slide with multiple exposed strips, which are automatically determined based on the novel two-stage algorithm. The analysis of trained convolutional neural networks employed for the detection of spore phytopathogens demonstrates high precision and recall metrics. The system is capable of identifying and counting the number of spores of phytopathogenic fungi species Blumeria graminis, Puccinia striiformis, and Pyrenophora tritici-repentis on each exposed strip. A methodology for estimating the spore distribution on a microscopic slide is proposed, which involves calculating the average spore concentration density.
Doroshenko et al. (Wed,) studied this question.