Classification of Soil Bacteria Based on Machine Learning and Image Processing

Abstract

Soil bacteria play a fundamental role in plant growth. This paper focuses on developing and testing some techniques designed to identify automatically such microorganisms. More specifically, the recognition performed here deals with the specific five genera of soil bacteria. Their microscopic images are classified with machine learning methods using shape and image texture descriptors. Feature determination based on shape relies on interpolation and curvature estimation whereas feature recognition based on image texture resorts to the spatial relationships between chrominance and luminance of pixels using co-occurrence matrices. From the variety of modelling methods applied here the best reported result amounts to 97% of accuracy. This outcome is obtained upon incorporating the set of features from both groups and subsequently merging classification and feature selection methods: Extreme Learning Machine - Radial Basis Function with Sparse Multinomial Logistic Regression with Bayesian Regularization and also k-Nearest Neighbors classifier with Fast Correlation Based Filter. The optimal parameters involved in merged classifiers are obtained upon computational testing and simulation.