Title

Autonomous Lightweight Green Algae Evaluation

Lead Author Major

Electrical & Computer Engineering

Lead Author Status

Senior

Second Author Major

Electrical & Computer Engineering

Second Author Status

Senior

Third Author Major

Electrical & Computer Engineering

Third Author Status

Senior

Fourth Author Major

Electrical & Computer Engineering

Fourth Author Status

Senior

Fifth Author Major

Electrical & Computer Engineering

Fifth Author Status

Senior

Format

SOECS Senior Project Demonstration

Faculty Mentor Name

Rahim Khoie

Faculty Mentor Email

rkhoie@PACIFIC.EDU

Faculty Mentor Department

Electrical & Computer Engineering

Additional Faculty Mentor Name

Elizabeth Basha

Additional Faculty Mentor Email

ebasha@u.pacific.edu

Additional Faculty Mentor Department

Electrical & Computer Engineering

Abstract/Artist Statement

The growth of algae is a vital issue for watersheds, and monitoring these algal blooms can provide useful data on the health of the local environment. However, most equipment that is used to measure water conditions and algae concentration is bulky and expensive, which is prohibitive to field testing. Additionally, many bodies of water are in remote locations that are difficult to access by car or on foot. Our autonomous algae drone addresses these issues with a low-cost sensor package that can attach to an autonomous drone. The sensor array will measure five key parameters: pH, salinity, depth, temperature, and algae concentration. For the crucial measurement of algae concentration, we intend to use a combination of fluorescence and image processing. Preliminary testing has shown that at least partial submersion will be necessary for our device, and final testing will be completed next semester on the Calaveras River. This project will result in improved methods for autonomous field-testing of water and algae in critical watersheds.

Location

School of Engineering & Computer Science

Start Date

4-5-2019 2:30 PM

End Date

4-5-2019 4:00 PM

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May 4th, 2:30 PM May 4th, 4:00 PM

Autonomous Lightweight Green Algae Evaluation

School of Engineering & Computer Science

The growth of algae is a vital issue for watersheds, and monitoring these algal blooms can provide useful data on the health of the local environment. However, most equipment that is used to measure water conditions and algae concentration is bulky and expensive, which is prohibitive to field testing. Additionally, many bodies of water are in remote locations that are difficult to access by car or on foot. Our autonomous algae drone addresses these issues with a low-cost sensor package that can attach to an autonomous drone. The sensor array will measure five key parameters: pH, salinity, depth, temperature, and algae concentration. For the crucial measurement of algae concentration, we intend to use a combination of fluorescence and image processing. Preliminary testing has shown that at least partial submersion will be necessary for our device, and final testing will be completed next semester on the Calaveras River. This project will result in improved methods for autonomous field-testing of water and algae in critical watersheds.