Investigating the Automatic Classification of Algae Using Fusion of Spectral and Morphological Characteristics of Algae via Deep Residual Learning

TL;DR

This paper presents SAMSON, a deep learning-based system that combines spectral and morphological features from multi-wavelength microscopy images to automatically classify algae types with high accuracy, aiding water management.

Contribution

The study introduces a novel system integrating spectral and morphological data with deep residual learning for algae classification, achieving 96% accuracy.

Findings

Achieved 96% classification accuracy among six algae types.

Demonstrated the effectiveness of combining spectral and morphological features.

Showed potential for cost-effective algae monitoring using machine learning.

Abstract

Under the impact of global climate changes and human activities, harmful algae blooms in surface waters have become a growing concern due to negative impacts on water related industries. Therefore, reliable and cost effective methods of quantifying the type and concentration of threshold levels of algae cells has become critical for ensuring successful water management. In this work, we present SAMSON, an innovative system to automatically classify multiple types of algae from different phyla groups by combining standard morphological features with their multi-wavelength signals. Two phyla with focused investigation in this study are the Cyanophyta phylum (blue-green algae), and the Chlorophyta phylum (green algae). We use a custom-designed microscopy imaging system which is configured to image water samples at two fluorescent wavelengths and seven absorption wavelengths using discrete-wavelength high-powered light emitting diodes (LEDs). Powered by computer vision and machine learning, we investigate the possibility and effectiveness of automatic classification using a deep residual convolutional neural network. More specifically, a classification accuracy of 96% was achieved in an experiment conducted with six different algae types. This high level of accuracy was achieved using a deep residual convolutional neural network that learns the optimal combination of spectral and morphological features. These findings elude to the possibility of leveraging a unique fingerprint of algae cell (i.e. spectral wavelengths and morphological features) to automatically distinguish different algae types. Our work herein demonstrates that, when coupled with multi-band fluorescence microscopy, machine learning algorithms can potentially be used as a robust and cost-effective tool for identifying and enumerating algae cells.