Artificial Intelligence-Based Shoreline Monitoring and Hatchling Behavioral Analysis for Sea Turtle Conservation

Abstract

Coastal erosion, climate change, and changes in shorelines are serious threats to sea turtle populations. Abnormal behavior in hatchlings raised in hatcheries also affects their survival. Traditional monitoring methods are mainly based on manual observation, which limits continuous and scalable assessment in various conservation areas. Although recent advances in artificial intelligence (AI) show potential in wildlife monitoring and environmental analysis, these issues are often tackled separately. This study introduces an AI-based monitoring framework that combines two related conservation efforts: assessing environmental risks at natural nesting sites and monitoring the behavior of hatchlings in hatcheries. The proposed system merges deep learning for shoreline segmentation with YOLOv8 for hatchling detection. It also includes trajectory based behavioral analysis and spatial risk modeling. This integrated approach allows simultaneous evaluation of environmental changes and hatchling behaviors within a unified analytical framework. Experimental results from video data gathered in coastal nesting areas and hatchery environments show reliable performance. The shoreline segmentation model achieved an Intersection over Union (IoU) of 0.71, with a segmentation mAP@0.5 of 0.71. The hatchling detection model reached a mAP@0.5 of 0.977 and can provide real-time results. By combining environmental and behavioral information, the system supports better risk assessments, including detecting shoreline encroachment and unusual hatchling behavior. In general, this framework offers an efficient and practical solution to automatically monitor sea turtle conservation.