Images of phytoplankton used in the development of a convolutional neural network to automate phytoplankton identification

We collected less than 160,000 images of particles using an imaging flow cytometer (the FlowCam). Images were then identified manually using morphology to the lowest taxonomic resolution possible. Greater than half of those images were either zooplankton, detritus or could not be identified (unknown). These identified images were then used to train and test a convolutional neural network. This convolutional neural network model can then be used to make predictions on new images collected with the FlowCam, with an apparent accuracy of 86% overall.

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