A machine learning approach to classifying algae concentrations
Document Type
Conference Proceeding
Publication Title
2017 IEEE MIT Undergraduate Research Technology Conference, URTC 2017
Publication Date
9-8-2016
Abstract
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.
Volume
2018-January
First Page
1
Last Page
4
DOI
10.1109/URTC.2017.8284201
Recommended Citation
Daniels, E., & McPheron, B. (2016). A machine learning approach to classifying algae concentrations. 2017 IEEE MIT Undergraduate Research Technology Conference, URTC 2017, 2018-January, 1-4. https://doi.org/10.1109/URTC.2017.8284201
ISBN
9781538625347
