The feasibility of automated identification of six algae types using neural networks and fluorescence-based spectral-morphological features

3 May 2018Jason L. DeglintChao JinAngela ChaoAlexander Wong

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... (read more)

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