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Abstract:
Background: Phytoplankton species identification and counting is a crucial step of water quality assessment.
Especially drinking water reservoirs, bathing and ballast water need to be regularly monitored for harmful species. In
times of multiple environmental threats like eutrophication, climate warming and introduction of invasive species
more intensive monitoring would be helpful to develop adequate measures. However, traditional methods such as
microscopic counting by experts or high throughput flow cytometry based on scattering and fluorescence signals are
either too time-consuming or inaccurate for species identification tasks. The combination of high qualitative microscopy
with high throughput and latest development in machine learning techniques can overcome this hurdle.
Results: In this study, image based cytometry was used to collect ~ 47,000 images for brightfield and Chl a fluorescence
at 60× magnification for nine common freshwater species of nano- and micro-phytoplankton. A deep
neuronal network trained on these images was applied to identify the species and the corresponding life cycle stage
during the batch cultivation. The results show the high potential of this approach, where species identity and their
respective life cycle stage could be predicted with a high accuracy of 97%.
Conclusions: These findings could pave the way for reliable and fast phytoplankton species determination of indicator
species as a crucial step in water quality assessment.
