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Under the impact of global climate changes and human activities, harmfulalgae blooms in surface waters have become a growing concern due to negativeimpacts on water related industries. Therefore, reliable and cost effectivemethods of quantifying the type and concentration of threshold levels of algaecells has become critical for ensuring successful water management. In thiswork, we present SAMSON, an innovative system to automatically classifymultiple types of algae from different phyla groups by combining standardmorphological features with their multi-wavelength signals. Two phyla withfocused investigation in this study are the Cyanophyta phylum (blue-greenalgae), and the Chlorophyta phylum (green algae). We use a custom-designedmicroscopy imaging system which is configured to image water samples at twofluorescent wavelengths and seven absorption wavelengths usingdiscrete-wavelength high-powered light emitting diodes (LEDs). Powered bycomputer vision and machine learning, we investigate the possibility andeffectiveness of automatic classification using a deep residual convolutionalneural network. More specifically, a classification accuracy of 96% wasachieved in an experiment conducted with six different algae types. This highlevel of accuracy was achieved using a deep residual convolutional neuralnetwork that learns the optimal combination of spectral and morphologicalfeatures. These findings elude to the possibility of leveraging a uniquefingerprint of algae cell (i.e. spectral wavelengths and morphologicalfeatures) to automatically distinguish different algae types. Our work hereindemonstrates that, when coupled with multi-band fluorescence microscopy,machine learning algorithms can potentially be used as a robust andcost-effective tool for identifying and enumerating algae cells.

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