The large data sets provided by in situ optical microscopes are allowing us to answer longstanding questions about the dynamics of planktonic ecosystems. To deal with the influx of information, while facilitating ecological insights, the design of these instruments increasingly must consider the data: storage standards, human annotation, and automated classification. In that context, we detail the design of the Scripps Plankton Camera (SPC) system, an in situ microscopic imaging system. Broadly speaking, the SPC consists of three units: (1) an underwater, free-space, dark-field imaging microscope; (2) a server-based management system for data storage and analysis; and (3) a web-based user interface for real-time data browsing and annotation. Combined, these components facilitate observations and insights into the diverse planktonic ecosystem. Here, we detail the basic design of the SPC and briefly present several preliminary, machine-learning-enabled studies illustrating its utility and efficacy.

To understand ocean health, it is crucial to monitor photosynthetic marine plankton – the microorganisms that form the base of the marine food web and are responsible for the uptake of atmospheric carbon. With the recent development of in situ microscopes that can acquire vast numbers of images of these organisms, the use of deep learning methods to taxonomically identify them has come to the forefront. Given this, two questions arise: 1) How well do deep learning methods such as Convolutional Neural Networks (CNNs) identify these marine organisms using data from in situ microscopes? 2) How well do CNN-derived estimates of abundance agree with established net and bottle-based sampling? Here, using images collected by the in situ Scripps Plankton Camera (SPC) system, we trained a CNN to recognize 9 species of phytoplankton, some of which are associated with Harmful Algal Blooms (HABs). The CNNs evaluated on 26 independent natural samples collected at Scripps Pier achieved an averaged accuracy of 92%, with 7 of 10 target categories above 85%. To compare abundance estimates, we fit a linear model between the number of organisms of each species counted in a known volume in the lab, with the number of organisms collected by the in situ microscope sampling at the same time. The linear fit between lab and in situ counts of several of the most abundant key HAB species suggests that, in the case of dinoflagellates, there is good correspondence between the two methods. As one advantage of our method, given the excellent correlation between lab counts and in situ microscope counts for key species, the methodology proposed here provides a way to estimate an equivalent volume in which the employed microscope can identify in-focus organisms and obtain statistically robust estimates of abundance.