Abstract

Effective exercise techniques aim at maintaining coastal biodiversity, carbon accumulation zones, and overall marine health, focusing on increasingly important seagrass ecosystems. Conventional species identification and counting methods require extensive fieldwork, are labor-intensive, and are highly error-prone. This paper proposes an automated method based on deep learning techniques for species identification and counting from underwater photos in seagrass multihabitats. In this paper, Convolutional Neural Networks (CNN), will automatically identify and categorize several seagrass species through underwater imaging. The approach includes a vast annotated dataset of seagrass photos containing images of various species from various habitats. The model constructed addresses issues related to water and light conditions and numerous species environments by using advanced image processing, multi-stage feature extraction, and custom-designed CNNs. The results reveal that these species are being identified and counted more accurately than the conventional methods, irrespective of the reporting and operational timelines claimed by those methods. Reliability and scalability are also surpassed in these latter-day techniques. Results also support more efficient monitoring in broader areas with reduced resources, proving advantageous for environmental assessments, thanks to the CNN framework. In addition, the innovation is ideal for marine biologists and conservators as it provides opportunities to integrate automated systems for continuous surveillance and real-time ecosystem monitoring of seagrass for ecosystem monitoring.

Keywords: Convolutional neural networks, Coastal biodiversity, Seagrass, ecosystems