The visual analysis of microscopic images is often used for soil bacteria recognition in microbiology. Such task can be automated with the aid of machine learning and digital image processing techniques. The best results for soil microorganism identification usually rely on extracting features based on color. However, accommodating in the latter an extra impact of lighting conditions or sample's preparation on classification accuracy is often omitted. In contrast, this research examines features which are insensitive to the above two factors by focusing rather on bacteria shape and their specific group dispersion. In doing so, the calculation of layout features resorts to k-means and mean shift methods. Additionally, the dependencies between specific distances determined from bacteria cells and the curvature of interpolated bacteria boundary are computed to extract vital geometric shape information. The proposed bacteria recognition tool involves testing four different classification methods for which the parameters are iteratively adjusted. The results obtained here for five selected soil bacteria genera: Enterobacter, Rhizobium, Pantoea, Bradyrhizobium and Pseudomonas reach 85.14% classification accuracy upon combining both geometric and dispersion features. The latter forms a promising result as a substitutive tool for color-based feature classification.
Copyright: © 2023 Konopka et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.