This Small Business Innovation Research Phase I project will implement a novel Computerized Algal Identification System (CAIS) to automatically identify, measure and enumerate algal taxa. Algae are sensitive indicators of stress in our national waters, yet algal data is difficult and expensive to obtain. Producing reliable data requires experienced technicians to spend hours per sample with results varying widely due to differences in methodology and training. The approach for this project unites an experienced algal ecologist with experts in image analysis to build a system initially capable of differentiating 11 genera. CAIS will incorporate automated microscopy and image analysis to analyze cell shape, color (pigment), texture (cell content) and size. Permanently mounted samples provide physical archives, solve orientation problems and retain characteristics necessary for identification and enumeration. Digital image and data archives enable process monitoring, strict classification accountability, and interactive expert evaluation.CAIS will significantly reduce processing time over current methods and ultimately increase laboratory capacities while decreasing measurement costs, and thus overcoming existing cost barriers to algal monitoring. CAIS is versatile and extensible to other taxonomic groups as well. Universities, government agencies, municipalities and consulting firms comprise a market conservatively estimated to generate over two million samples annually.
