A Survey of Deep Learning Approaches for the Monitoring and Classification of Seagrass

Abstract Seagrass meadows are essential to the health of coastal ecosystems. They support carbon storage, provide habitats for marine species, and help stabilize coastlines. Monitoring underwater seagrass is vital for understanding the conditions of the ecosystem. Researchers have been interested in identifying and classifying underwater seagrasses. However, traditional monitoring methods can be labor-intensive and costly, especially in complex underwater environments. Deep learning approaches have made significant progress in digital image processing, particularly in object recognition and classification, and are among the most popular computer vision tools. The collection of digital images for monitoring underwater habitats, such as seagrass meadows, has increased significantly as recent progress in imaging technology has made it easier to collect high-resolution data. The increase in imagery data has in turn created a demand for automated detection and classification using deep neural network-based techniques. This study reviews the current deep-learning techniques used for monitoring and classification of the seagrass. It discusses the key methodologies, datasets, and progress in this area. This study not only examines the well-known challenges such as limited availability of data but provides a novel, structured taxonomy of deep learning techniques tailored for the monitoring of seagrass, highlighting their unique advantages and limitations within diverse marine environments. By synthesizing findings across various data sources and model architectures, we offer critical insights into the selection of context-aware algorithms and identify key research gaps, an essential step for advancing the reliability and applicability of AI-driven seagrass conservation efforts.