English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

The most commonly used biomass estimate for microalgae is obtained from cell biovolume, usually calculated from microscopically measured linear dimensions. Although reliable, this is a highly time-consuming and specialized technique. Automated sampling devices that acquire images of cells and use pattern recognition techniques to identify the images have been developed as an alternative to microscopy-based methods. There are some aspects of automatic sampling and classification methods, however, which can be improved for the analysis of field samples including living and non-living particles. In this work, we demonstrate how the accuracy of a state-of the-art technique for plankton classification (Support Vector Machine) can be improved up to 86% if a previous automated step designed to remove non-living images is included. There is a tendency with the currently applied automatic methods to misestimate cell biovolume due to the two-dimensionality of the images. Here, we present a data set of more than 500 samples to show that the greatest effect is caused by the incorrect estimation of biovolume of the chain-forming diatoms. This results in an overestimate of biomass of between 20 and 100% where chain-forming diatoms represent more than the 20% of the biomass of the sample. We show how the classification method can be adapted to provide not only taxonomic but also the morphological classification of cells in order to obtain a more reliable estimate of biovolume according to the predicted cell shape, in a way comparable with microscopy-based estimates.

Improvement of plankton biovolume estimates derived from image-based automatic sampling devices: application to FlowCAM