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Harmful algal bloom smart device application: using image analysis and machine learning techniques for early classification of harmful algal blooms
Citation:
Lazorchak, Jim, M. Waters, Joel Allen, AND M. Steinitz-Kannan. Harmful algal bloom smart device application: using image analysis and machine learning techniques for early classification of harmful algal blooms. SETAC Europe 2016, Nantes, FRANCE, May 22 - 26, 2016.
Impact/Purpose:
This poster covers a new application for use by Citizen Scientists or Monitoring personnel to detect harmful algal blooms real time. It is a app that we intend to use on a study lake as well as at several locations along the Ohio river.
Description:
The Ecological Stewardship Institute at Northern Kentucky University and the U.S. Environmental Protection Agency are collaborating to optimize a harmful algal bloom detection algorithm that estimates the presence and count of cyanobacteria in freshwater systems by image analysis. Green and blue-green algae exhibit different Hue-Saturation-Value color histograms in digital photographs. These differences are exploited by machine learning techniques to train a smart device (cellular phone, tablet, or similar) to detect the presence and amount of cyanobacteria in a small surface portion of a freshwater system. The Harmful Algal Bloom Classification Application (HAB APP) has been field tested and verified to classify both green and blue-green algae. Specifically, the APP has been tested on several small streams and ponds, correctly classifying green algal blooms and has been tested on the Ohio River, correctly classifying blue-green algae in the recent 636-mile cyanobacteria bloom. The application is planned to be tested and optimized in Lake Harsha, a 22,000-acre reservoir which supplies six million gallons per day of drinking water to the Ohio county in which it lies and is a source of many recreational activities, including swimming, boating, and fishing. The application will be used on images taken from a freshwater intake facility. The presence and amount (or lack thereof) will be verified by other detection instruments and in vitro by agency scientists and hysteresis techniques will be used to monitor the presence and amount of cyanobacteria on a periodic (e.g. daily, seasonally) basis.
