Path to Automating Ocean Health Monitoring

Authors

  • Mak Ahmad UC Davis
  • J. Scott Penberthy Google
  • Abigail Powell Lynker Technologies under contract to NOAA Northwest Fisheries Science Center

Keywords:

Applied Ai, Transfer Learning, Convolutional Neural Network, Multi-label Classification, Image Classification, Species Monitoring, Coral

Abstract

Marine ecosystems directly and indirectly impact human health, providing benefits such as essential food sources, coastal protection and biomedical compounds. Monitoring changes in marine species is important because impacts such as overfishing, ocean acidification and hypoxic zones can negatively affect both human and ocean health. The US west coast supports a diverse assemblage of deep-sea corals that provide habitats for fish and numerous other invertebrates. Currently, National Oceanic Atmospheric Administration (NOAA) scientists manually track the health of coral species using extractive methods. In this paper, we test the viability of using a machine learning algorithm Convolutional Neural Network (CNN) to automatically classify coral species, using field-collected coral images in collaboration with NOAA. We fine tune the hyperparameters of our model to surpass the human F-score. We also highlight a scalable opportunity to monitor ocean health automatically while preserving corals.

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Published

2021-05-18

How to Cite

Ahmad, M., Penberthy, J. S., & Powell, A. (2021). Path to Automating Ocean Health Monitoring. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15240-15246. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17788

Issue

Section

IAAI Technical Track on Emerging Applications of AI