  Harmful algae blooms (HABs), which produce lethal toxins, are a growing
global concern since they negatively affect the quality of drinking water and
have major negative impact on wildlife, the fishing industry, as well as
tourism and recreational water use. In this study, we investigate the
feasibility of leveraging machine learning and fluorescence-based
spectral-morphological features to enable the identification of six different
algae types in an automated fashion. More specifically, a custom multi-band
fluorescence imaging microscope is used to capture fluorescence imaging data of
a water sample at six different excitation wavelengths ranging from 405 nm -
530 nm. A number of morphological and spectral fluorescence features are then
extracted from the isolated micro-organism imaging data, and used to train
neural network classification models designed for the purpose of identification
of the six algae types given an isolated micro-organism. Experimental results
using three different neural network classification models showed that the use
of either fluorescence-based spectral features or fluorescence-based
spectral-morphological features to train neural network classification models
led to statistically significant improvements in identification accuracy when
compared to the use of morphological features (with average identification
accuracies of 95.7%+/-3.5% and 96.1%+/-1.5%, respectively). These preliminary
results are quite promising, given that the identification accuracy of human
taxonomists are typically between the range of 67% and 83%, and thus
illustrates the feasibility of leveraging machine learning and
fluorescence-based spectral-morphological features as a viable method for
automated identification of different algae types.
