Description:
Summary: Phytoplankton form the foundation of most aquatic ecosystems and therefore quantitative information about their species composition is critical for many fields of research and applications. For example, in coastal and inland waters, species-specific cell counts data can be useful for eutrophication assessment and detection of harmful algal blooms. Satellite water color remote sensing can potentially be a powerful tool for rapidly and cost-effectively retrieving information associated with phytoplankton composition, provided that large amount of field data obtained in diverse natural waters are available to develop this tool. Extant techniques to measure phytoplankton cell counts are inadequate to satisfy this need. The classical method requires a phytoplankton taxonomist, or a non-professional aided by books, to look through a microscope, recognize, and count manually. The process is tedious and time-consuming, and lacks consistency because it depends on who is counting. Some instruments use an alternative approach that replaces the microscope with an imaging instrument, but still require manual species identification. Some achieved automatic identification and quantification of phytoplankton cells down to individual species. However, they lack portability, speediness of data acquisition, and accuracy of species identification.
Project Goals: The objective of this subtopic is to develop a portable, fast, and intelligent instrument that can be used to automatically and accurately measure the cell number concentration of each individual phytoplankton species present in a given natural water sample. This is a challenging task considering that 1) in natural waters phytoplankton cells are mixed with non-living particles of similar size and abundance; 2) different phytoplankton species can vary greatly in size and morphology; and 3) phytoplankton cells can form chains or arbitrarily shaped colonies. We envision that the new instrument capable of addressing these challenges would require two essential components, image acquisition and artificial taxonomist. The image acquisition component is used to capture and record information about individual cells such as size, color, morphology, excitation-emission spectra, and etc., basically any information that can be used to extract unique traits to characterize a species. This component must have a sufficiently large throughput to provide statistically representative cell counts for at least dominant species in the sample. The artificial taxonomist component is used to replace the human taxonomist to identify the taxon of each unknown cell based on the information captured by the image acquisition component. Advanced image processing algorithms play a key role in this component. Expectations for Phase I include a detailed proof-of-concept report describing research results and technology development completed for the instrument, and a description of where the principal investigator expects the project to be at the end of Phase II, including a description of how this instrument will be commercialized.
