Assessment of ZooImage as a tool for the classification of zooplankton

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Abstract

ZooImage, image analysis software, was evaluated to determine its ability to differentiate between zooplankton groups in preserved zooplankton samples collected in Prince William Sound, Alaska. A training set of 53 categories were established to train the software for automatic recognition. Using the Random forest algorithm, ZooImage identified particles in the training set with less than 13% error. Despite reasonable results with the training set, however, ZooImage was less effective when this training set was used to identify particles from field-collected zooplankton samples. When all particles were examined, ZooImage had an accuracy of 81.7% but this dropped to 63.3% when discard particles (e.g. marine snow and fibers) were removed from total particles. Copepods, the numerically dominant organisms in most samples, were examined separately and were correctly identified 67.8% of the time. Further investigation suggested size was effective in determining identifications; medium size copepods (e.g. Pseudocalanus sp., Acartia sp.) were accurately identified 73.3% of the time. ZooImage can provide a coarse level of taxonomic classification and we anticipate continued improvement to this software should further enhance automatic identification of preserved zooplankton samples.

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