We developed the ZooScan with ZooProcess and Plankton Identifier (PkID) software as an integrated analysis system for acquisition and classification of digital zooplankton images from preserved zooplankton samples. Zooplankton samples are digitized by the ZooScan and processed by ZooProcess and PkID in order to detect, enumerate, measure and classify the digitized objects.

Here we present a semi-automatic approach that entails automated classification of images followed by manual vali- dation, which allows rapid and accurate classification of zooplankton and abiotic objects. We demonstrate this approach with a biweekly zooplankton time series from the Bay of Villefranche-sur-mer, France. The classification approach pro- posed here provides a practical compromise between a fully automatic method with varying degrees of bias and a manual but accurate classification of zooplank- ton. We also evaluate the appropriate number of images to include in digital learn- ing sets and compare the accuracy of six classification algorithms. We evaluate the accuracy of the ZooScan for automated measurements of body size and present relationships between machine measures of size and C and N content of selected zooplankton taxa.

We demonstrate that the ZooScan system can produce useful measures of zooplankton abundance, biomass and size spectra, for a variety of ecological studies.

see Gorsky et al., 2010 for more details

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