A machine learning approach to classifying algae concentrations

Algal concentrations in marine environments are monitored regularly, as higher concentrations may lead to harmful algal blooms, which negatively impact coastal ecosystems. To identify algae concentration in the field, researchers have developed a handheld, low-cost in-situ device employing spectrophotometry and optical filtering. In an effort to better understand and evaluate the data collected, a pattern recognition method for automatic concentration detection was created. This method employs binary classification to differentiate low and high concentrations. Features for classification were defined by the spectral peaks evaluated, these include: RMS value, distance between edges, variance, and energy.