Publication Date

Fall 2023

Degree Type

Master's Project

Degree Name

Master of Science in Bioinformatics (MSBI)

Department

Computer Science

First Advisor

Philip Heller

Second Advisor

Wendy Lee

Third Advisor

Maya deVries

Keywords

Coral Reefs, Crustose Coralline Algae (CCA), Machine learning (ML), Computer vision, Image segmentation, Mask R-CNN

Abstract

Coral reefs, supporting 25% of marine biodiversity, confront challenges from local and global impacts like overfishing, runoff, acidification, and warming. Crustose Coralline Algae (CCA), pivotal for reef structure and coral settlement, are underrepresented in research. Current methods like Coral Point Count with Excel Extensions (CPCe) have limitations, relying on image quality and being time-consuming. This paper proposes computer vision and Mask R-CNN, a supervised machine learning model, for CCA analysis in reef images, considering color, texture, and shape. Results indicate promise in clustering and classifying organisms. The innovative technology reduces manual labor, enhancing image analysis, simplifying the understanding of CCA’s role in reef health. Future work involves advanced feature extraction and exploring different machine learning models for marine ecology research.

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