Seagrass Classification Using Differentiable Architecture Search

Abstract

Seagrasses are among the most ecologically significant and diverse ecosystems on Earth, playing a crucial role in maintaining the health and productivity of coastal environments. However, these important habitats are threatened by various human activities, including pollution, habitat destruction, and climate change. To address these challenges, it is essential to develop effective conservation and management strategies that protect seagrass ecosystems and the species that depend on them. Accurately identifying various seagrass species is essential to understanding their habitat and overall health. The researchers have developed a seagrass species identification model to address this challenge using a differentiable architecture search with an early stopping strategy. This model achieved an impressive overall accuracy of 93.3% within a relatively short training time of 4 hours and 11 minutes using a commercially-available Apple MacBook device. This model has the potential to greatly improve the efficiency and accuracy of seagrass species identification, providing valuable insights for conservation efforts and supporting the conservation of these vital ecosystems.