Age-based clustering of seagrass blades using AI models

Omer Sevinc; Mehrube Mehrubeoglu; Kirk Cammarata; Chi Huang; Lifford McLauchlan

doi:10.1117/12.3014915

7 June 2024 Age-based clustering of seagrass blades using AI models

Omer Sevinc, Mehrube Mehrubeoglu, Kirk Cammarata, Chi Huang, Lifford McLauchlan

Proceedings Volume 13034, Real-Time Image Processing and Deep Learning 2024; 130340F (2024) https://doi.org/10.1117/12.3014915
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Seagrass ecosystems play a vital role in maintaining marine biodiversity and ecological balance, making their monitoring and management essential. This study proposes a novel approach for clustering of seagrass images into three distinct age categories: young, medium, and old, using deep learning and unsupervised machine learning techniques. VGG-16 convolutional neural networks (CNN) are employed for feature extraction from the seagrass images, followed by K-means clustering to categorize the image samples into the specified age groups. The implemented methodology begins with the collection and annotation of a diverse seagrass image dataset, including samples from various locations and conditions. Images are first pre-processed to ensure consistent size and quality. To enable real-time capabilities, an optimized VGG-16 CNN is then fine-tuned on the annotated dataset to learn discriminative features that capture age-related characteristics of the seagrass leaves within the constraints of real-time image processing. After feature extraction, the Kmeans clustering algorithm is applied to group the images into young, medium, and old categories based on the learned features. The clustering results are evaluated using quantitative metrics such as the silhouette score and Davies-Bouldin index, demonstrating the effectiveness of the proposed method in capturing age-related patterns in seagrass imagery. This research contributes to the field of seagrass monitoring by providing an automated and real-time approach to classifying seagrass images into age categories which can facilitate more accurate assessments of seagrass health and growth dynamics. A real-time capability would equip decision-makers with a valuable tool for immediate responses and support the sustainable management of seagrass ecosystems in various marine environments.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Omer Sevinc, Mehrube Mehrubeoglu, Kirk Cammarata, Chi Huang, and Lifford McLauchlan "Age-based clustering of seagrass blades using AI models", Proc. SPIE 13034, Real-Time Image Processing and Deep Learning 2024, 130340F (7 June 2024); https://doi.org/10.1117/12.3014915

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