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Grosjean, P., Picheral, M., Warembourg, C., and Gorsky, G. 2004. Enumeration,measurement, and identication of net zooplankton samples using the ZOOSCAN digitalimaging system. e ICES Journal of Marine Science, 61: 518e525.Identifying and counting zooplankton are labour-intensive and time-consuming processesthat are still performed manually. However, a new system, known as ZOOSCAN, has beendesigned for counting zooplankton net samples. We describe image-processing and theresults of (semi)-automatic identication of taxa with various machine-learning methods.Each scan contains between 1500 and 2000 individuals !0.5 mm. We used two trainingsets of about 1000 objects each divided into 8 (simplied) and 29 groups (detailed),respectively. The new discriminant vector forest algorithm, which is one of the mostecient methods, discriminates between the organisms in the detailed training set with anaccuracy of 75% at a speed of 2000 items per second. A supplementary algorithm tagsobjects that the method classied with low accuracy (suspect items), such that they could bechecked by taxonomists. This complementary and interactive semi-automatic processcombines both computer speed and the ability to detect variations in proportions and greylevels with the human skills to discriminate animals on the basis of small details, such aspresence/absence or number of appendages. After this checking process, total accuracyincreases to between 80% and 85%. We discuss the potential of the system as a standard foridentication, enumeration, and size frequency distribution of net-collected zooplankton.

Enumeration, measurement, and identification of net zooplankton samples using the ZOOSCAN digital imaging system