Improving semiautomated zooplankton classification using an internal control and different imaging devices

The rapid development of image-based methods for counting and classifying zooplankton has made it possible to analyze large numbers of samples in a semiautomated way. However, using semiautomated methods to deal with hundreds of samples increases the risk of propagating errors during the procedure. Furthermore, classification methods based on training sets require constant validation to ensure that systematic errors do not affect the results. In this study, we propose using an internal control to check the quality of the procedure for counting and classifying zooplankton. We also evaluate the advantages and disadvantages of two different laboratory imaging devices (scanner and photographic camera) at two resolutions (4800 dpi and 8500 dpi).