Publication Date

Spring 2025

Degree Type

Master's Project

Degree Name

Master of Science in Bioinformatics (MSBI)

Department

Computer Science

First Advisor

Dr. Philip Heller

Keywords

Coral reefs, Sponge, Instance segmentation, Convolutional Neural Networks (CNNs), Mask R-CNN

Abstract

Climate change-driven ocean warming and acidification are disrupting the ecological balance of coral reefs. Notably, these oceanic conditions are undermining coral health and accelerating their decline which is favoring some sponge species in outcompeting them for dominance. Although functional, these altered reefs destabilize the reef architecture, hinder nutrient cycling, and support fewer marine species. Thus, monitoring the growth and abundance of various sponges and understanding their roles at different stages of ecological succession in coral reefs is vital. Autonomous reef monitoring structures (ARMS) are often used for this purpose, but manual taxonomic analysis using their images is time-consuming, inconsistent and not scalable. Here, we developed preliminary Mask R-CNN models using transfer learning approach for automating the process of identifying sponge Clathrinidae in ARMS plate images. The models trained were effective in identifying the target sponge across both seen and unseen images, showcasing its learning potential and generalizability even with a small training dataset. They also show how different training focus can lead to differences in their recall and precision performances. Overall, this work highlights the potential of Mask R-CNN based models to facilitate automated coral reef monitoring and guide their conservation and restoration strategies.

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