Detecting Complex and Amorphous Features of Marine Sponges

Andy Portalatin's master's thesis at the University of Puerto Rico at Mayaguez led to the creation of the "Porifera Classifier," a computer vision model designed to detect and classify up to 126 marine sponge species using the YOLOv8 object detection algorithm on the Roboflow platform. Marine sponges play vital ecological roles, but their identification is traditionally challenging due to complex features and limited taxonomic expertise. The Porifera Classifier automates the identification process, supporting ecological research and conservation efforts by providing rapid, accurate analysis crucial for monitoring sponge populations against threats like climate change and pollution. The model's development involved using Roboflow for annotating and augmenting a dataset of 8,958 images, which was expanded to 27,188 images through various augmentations. Experimental results demonstrated improved model performance, with applications extending to environmental monitoring, citizen science initiatives, and integration with bio-inspired robotics. This innovative approach allows for deeper insights into marine sponge biodiversity and conservation strategies while fostering sustainable practices in marine science research.