Portalatin, Andy

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    Approaches for the recognition and classification of marine sponges: Leveraging deep learning in underwater environments
    (2023-12-12) Portalatin, Andy; Cafaro, Matías J.; College of Arts and Sciences - Sciences; Alfaro, Mónica; Schizas, Nikolaos V.; Department of Biology; Arzuaga, Emmanuel
    Marine sponges (Phylum Porifera) are known to be an integral part of marine ecosystems, as they provide essential functions such as nutrient cycling, water filtration, and species-specific niches. These resilient organisms can thrive in various temperature zones and depths, even adapting to freshwater environments. Beyond their ecological significance, sponges possess valuable natural compounds with proven applications in medicine, evolutionary analysis, commercial resources, and many other fields. Nonetheless, marine sponges face escalating threats from climate change, overfishing, coastal development, land-based runoff, to different types of pollution. Accurate identification and classification are thus critical for a better understanding of their roles and implementing conservation strategies. Traditional classification methods alone are labor-intensive and depend on expertise, limiting their scalability. To address these challenges, we developed the “Porifera Classifier”, a deep learning, computer vision model trained on a curated dataset of 16,915 labelled images that allows it to detect and classify up to 126 species of marine sponges. Established through a YOLOv8 architecture, the model ensures state-of-the-art accuracy benchmarks based on the detection of amorphous and highly varying structural objects. It further evaluates the integration of algorithms that compensate for light scattering in underwater recordings. The annotated dataset also serves as a valuable resource for future studies. Moreover, this thesis elucidates the growing role of machine learning in oceanography and environmental research. Computer vision enhances research precision and efficiency, extending to critical areas like ecology, evolution, microbiology, and genetics, contributing to a comprehensive understanding of marine and terrestrial biology.