  Plankton provide the foundation for life on earth. To advance our
understanding of the marine ecosystem, for scientific, commercial and survival
purposes, more in situ continuous monitoring and analysis of plankton is
required. Cost, complexity, power and data communication demands are barriers
to widespread deployment of in situ plankton microscopes. We address these
barriers by building and characterizing a lensless microscope with a data
pipeline optimized for the Raspberry Pi 3. The pipeline records 1080p video of
multiple plankton swimming in a sample well while simultaneously detecting,
tracking and selecting salient cropped images for classification @ 5.1 frames
per second. Thirteen machine learning classifiers and combinations of nine sets
of features are evaluated on nine plankton classes, optimized for speed
(F1=0.74 @ 1 msec. per image prediction) and accuracy (F1=0.81 @ .80 sec.).
System performance results confirm that performing the entire data pipeline
from image capture to classification is possible on a low-cost open-source
embedded computer.
