Deploying AI for Health Monitoring of Diadema Sea Urchins: Toward Sustainable Marine Ecosystems

This study examines the potential of artificial intelligence (AI) technologies in monitoring the health of Diadema sea urchins, a crucial species for marine biodiversity. Utilizing advanced image acquisition and preprocessing techniques, detailed photographic data of Diadema populations in the Gulf of Aqaba were captured. Through the application of four AI models: AdaBoost (Adaptive Boosting), Random Forest, Convolutional Neural Network (CNN), and You Only Look Once version 7 (YOLOv7), the research aimed to accurately assess the health status of these marine organisms. The models were evaluated based on precision, recall, false positive rate (FPR), and F-measure, with YOLOv7 demonstrating superior performance across all metrics. The findings highlight the urgent need for innovative approaches to marine ecosystem monitoring in response to significant mortality events among Diadema species. The integration of AI and image processing technologies offers a promising strategy for enhancing the accuracy and efficiency of ecological assessments. This study contributes to the advancement of ecological stewardship and emphasizes the importance of adopting AI technologies for the conservation of marine biodiversity, advocating for international collaboration and the continuous innovation of monitoring methodologies.