Neural Network Recognition of Marine Benthos and Corals
We present thorough this review the developments in the field, point out their current limitations, and outline its timelines and unique potential. In order to do so we introduce the methods used in each of the advances in the application of deep learning (DL) to coral research that took place betwe...
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ftdoajarticles:oai:doaj.org/article:b2b34fe3086746eea3552960231238b6 2023-05-15T17:52:03+02:00 Neural Network Recognition of Marine Benthos and Corals Alina Raphael Zvy Dubinsky David Iluz Nathan S. Netanyahu 2020-01-01T00:00:00Z https://doi.org/10.3390/d12010029 https://doaj.org/article/b2b34fe3086746eea3552960231238b6 EN eng MDPI AG https://www.mdpi.com/1424-2818/12/1/29 https://doaj.org/toc/1424-2818 1424-2818 doi:10.3390/d12010029 https://doaj.org/article/b2b34fe3086746eea3552960231238b6 Diversity, Vol 12, Iss 1, p 29 (2020) coral reef marine ecosystem deep learning coral species classification Biology (General) QH301-705.5 article 2020 ftdoajarticles https://doi.org/10.3390/d12010029 2022-12-30T23:46:13Z We present thorough this review the developments in the field, point out their current limitations, and outline its timelines and unique potential. In order to do so we introduce the methods used in each of the advances in the application of deep learning (DL) to coral research that took place between the years: 2016−2018. DL has unique capability of streamlining the description, analysis, and monitoring of coral reefs, saving time, and obtaining higher reliability and accuracy compared with error-prone human performance. Coral reefs are the most diverse and complex of marine ecosystems, undergoing a severe decline worldwide resulting from the adverse synergistic influences of global climate change, ocean acidification, and seawater warming, exacerbated by anthropogenic eutrophication and pollution. DL is an extension of some of the concepts originating from machine learning that join several multilayered neural networks. Machine learning refers to algorithms that automatically detect patterns in data. In the case of corals these data are underwater photographic images. Based on “learned” patterns, such programs can recognize new images. The novelty of DL is in the use of state-of-art computerized image analyses technologies, and its fully automated methodology of dealing with large data sets of images. Automated Image recognition refers to technologies that identify and detect objects or attributes in a digital video or image automatically. Image recognition classifies data into selected categories out of many. We show that Neural Network methods are already reliable in distinguishing corals from other benthos and non-coral organisms. Automated recognition of live coral cover is a powerful indicator of reef response to slow and transient changes in the environment. Improving automated recognition of coral species, DL methods already recognize decline of coral diversity due to natural and anthropogenic stressors. Diversity indicators can document the effectiveness of reef bioremediation initiatives. We explored ... Article in Journal/Newspaper Ocean acidification Directory of Open Access Journals: DOAJ Articles Diversity 12 1 29 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
coral reef marine ecosystem deep learning coral species classification Biology (General) QH301-705.5 |
spellingShingle |
coral reef marine ecosystem deep learning coral species classification Biology (General) QH301-705.5 Alina Raphael Zvy Dubinsky David Iluz Nathan S. Netanyahu Neural Network Recognition of Marine Benthos and Corals |
topic_facet |
coral reef marine ecosystem deep learning coral species classification Biology (General) QH301-705.5 |
description |
We present thorough this review the developments in the field, point out their current limitations, and outline its timelines and unique potential. In order to do so we introduce the methods used in each of the advances in the application of deep learning (DL) to coral research that took place between the years: 2016−2018. DL has unique capability of streamlining the description, analysis, and monitoring of coral reefs, saving time, and obtaining higher reliability and accuracy compared with error-prone human performance. Coral reefs are the most diverse and complex of marine ecosystems, undergoing a severe decline worldwide resulting from the adverse synergistic influences of global climate change, ocean acidification, and seawater warming, exacerbated by anthropogenic eutrophication and pollution. DL is an extension of some of the concepts originating from machine learning that join several multilayered neural networks. Machine learning refers to algorithms that automatically detect patterns in data. In the case of corals these data are underwater photographic images. Based on “learned” patterns, such programs can recognize new images. The novelty of DL is in the use of state-of-art computerized image analyses technologies, and its fully automated methodology of dealing with large data sets of images. Automated Image recognition refers to technologies that identify and detect objects or attributes in a digital video or image automatically. Image recognition classifies data into selected categories out of many. We show that Neural Network methods are already reliable in distinguishing corals from other benthos and non-coral organisms. Automated recognition of live coral cover is a powerful indicator of reef response to slow and transient changes in the environment. Improving automated recognition of coral species, DL methods already recognize decline of coral diversity due to natural and anthropogenic stressors. Diversity indicators can document the effectiveness of reef bioremediation initiatives. We explored ... |
format |
Article in Journal/Newspaper |
author |
Alina Raphael Zvy Dubinsky David Iluz Nathan S. Netanyahu |
author_facet |
Alina Raphael Zvy Dubinsky David Iluz Nathan S. Netanyahu |
author_sort |
Alina Raphael |
title |
Neural Network Recognition of Marine Benthos and Corals |
title_short |
Neural Network Recognition of Marine Benthos and Corals |
title_full |
Neural Network Recognition of Marine Benthos and Corals |
title_fullStr |
Neural Network Recognition of Marine Benthos and Corals |
title_full_unstemmed |
Neural Network Recognition of Marine Benthos and Corals |
title_sort |
neural network recognition of marine benthos and corals |
publisher |
MDPI AG |
publishDate |
2020 |
url |
https://doi.org/10.3390/d12010029 https://doaj.org/article/b2b34fe3086746eea3552960231238b6 |
genre |
Ocean acidification |
genre_facet |
Ocean acidification |
op_source |
Diversity, Vol 12, Iss 1, p 29 (2020) |
op_relation |
https://www.mdpi.com/1424-2818/12/1/29 https://doaj.org/toc/1424-2818 1424-2818 doi:10.3390/d12010029 https://doaj.org/article/b2b34fe3086746eea3552960231238b6 |
op_doi |
https://doi.org/10.3390/d12010029 |
container_title |
Diversity |
container_volume |
12 |
container_issue |
1 |
container_start_page |
29 |
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1766159362360868864 |