#DeOlhoNosCorais: a polygonal annotated dataset to optimize coral monitoring
Corals are colonial animals within the Phylum Cnidaria that form coral reefs, playing a significant role in marine environments by providing habitat for fish, mollusks, crustaceans, sponges, algae, and other organisms. Global climate changes are causing more intense and frequent thermal stress event...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2023-11-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/16219.pdf |
| _version_ | 1797416451449028608 |
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| author | Daniel P. Furtado Edson A. Vieira Wildna Fernandes Nascimento Kelly Y. Inagaki Jessica Bleuel Marco Antonio Zanata Alves Guilherme O. Longo Luiz S. Oliveira |
| author_facet | Daniel P. Furtado Edson A. Vieira Wildna Fernandes Nascimento Kelly Y. Inagaki Jessica Bleuel Marco Antonio Zanata Alves Guilherme O. Longo Luiz S. Oliveira |
| author_sort | Daniel P. Furtado |
| collection | DOAJ |
| description | Corals are colonial animals within the Phylum Cnidaria that form coral reefs, playing a significant role in marine environments by providing habitat for fish, mollusks, crustaceans, sponges, algae, and other organisms. Global climate changes are causing more intense and frequent thermal stress events, leading to corals losing their color due to the disruption of a symbiotic relationship with photosynthetic endosymbionts. Given the importance of corals to the marine environment, monitoring coral reefs is critical to understanding their response to anthropogenic impacts. Most coral monitoring activities involve underwater photographs, which can be costly to generate on large spatial scales and require processing and analysis that may be time-consuming. The Marine Ecology Laboratory (LECOM) at the Federal University of Rio Grande do Norte (UFRN) developed the project “#DeOlhoNosCorais” which encourages users to post photos of coral reefs on their social media (Instagram) using this hashtag, enabling people without previous scientific training to contribute to coral monitoring. The laboratory team identifies the species and gathers information on coral health along the Brazilian coast by analyzing each picture posted on social media. To optimize this process, we conducted baseline experiments for image classification and semantic segmentation. We analyzed the classification results of three different machine learning models using the Local Interpretable Model-agnostic Explanations (LIME) algorithm. The best results were achieved by combining EfficientNet for feature extraction and Logistic Regression for classification. Regarding semantic segmentation, the U-Net Pix2Pix model produced a pixel-level accuracy of 86%. Our results indicate that this tool can enhance image selection for coral monitoring purposes and open several perspectives for improving classification performance. Furthermore, our findings can be expanded by incorporating other datasets to create a tool that streamlines the time and cost associated with analyzing coral reef images across various regions. |
| first_indexed | 2024-03-09T06:04:28Z |
| format | Article |
| id | doaj.art-8b8d2563e6de4311b88f606e1188e4c1 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:04:28Z |
| publishDate | 2023-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-8b8d2563e6de4311b88f606e1188e4c12023-12-03T12:05:49ZengPeerJ Inc.PeerJ2167-83592023-11-0111e1621910.7717/peerj.16219#DeOlhoNosCorais: a polygonal annotated dataset to optimize coral monitoringDaniel P. Furtado0Edson A. Vieira1Wildna Fernandes Nascimento2Kelly Y. Inagaki3Jessica Bleuel4Marco Antonio Zanata Alves5Guilherme O. Longo6Luiz S. Oliveira7Department of Informatics, Federal University of Paraná, Curitiba, PR, BrazilDepartment of Oceanography and Limnology, Federal University of Rio Grande do Norte, Natal, RN, BrazilDepartment of Oceanography and Limnology, Federal University of Rio Grande do Norte, Natal, RN, BrazilDepartment of Oceanography and Limnology, Federal University of Rio Grande do Norte, Natal, RN, BrazilDepartment of Oceanography and Limnology, Federal University of Rio Grande do Norte, Natal, RN, BrazilDepartment of Informatics, Federal University of Paraná, Curitiba, PR, BrazilDepartment of Oceanography and Limnology, Federal University of Rio Grande do Norte, Natal, RN, BrazilDepartment of Informatics, Federal University of Paraná, Curitiba, PR, BrazilCorals are colonial animals within the Phylum Cnidaria that form coral reefs, playing a significant role in marine environments by providing habitat for fish, mollusks, crustaceans, sponges, algae, and other organisms. Global climate changes are causing more intense and frequent thermal stress events, leading to corals losing their color due to the disruption of a symbiotic relationship with photosynthetic endosymbionts. Given the importance of corals to the marine environment, monitoring coral reefs is critical to understanding their response to anthropogenic impacts. Most coral monitoring activities involve underwater photographs, which can be costly to generate on large spatial scales and require processing and analysis that may be time-consuming. The Marine Ecology Laboratory (LECOM) at the Federal University of Rio Grande do Norte (UFRN) developed the project “#DeOlhoNosCorais” which encourages users to post photos of coral reefs on their social media (Instagram) using this hashtag, enabling people without previous scientific training to contribute to coral monitoring. The laboratory team identifies the species and gathers information on coral health along the Brazilian coast by analyzing each picture posted on social media. To optimize this process, we conducted baseline experiments for image classification and semantic segmentation. We analyzed the classification results of three different machine learning models using the Local Interpretable Model-agnostic Explanations (LIME) algorithm. The best results were achieved by combining EfficientNet for feature extraction and Logistic Regression for classification. Regarding semantic segmentation, the U-Net Pix2Pix model produced a pixel-level accuracy of 86%. Our results indicate that this tool can enhance image selection for coral monitoring purposes and open several perspectives for improving classification performance. Furthermore, our findings can be expanded by incorporating other datasets to create a tool that streamlines the time and cost associated with analyzing coral reef images across various regions.https://peerj.com/articles/16219.pdfConvolutional neural networkMachine learningComputer visionMarine ecology |
| spellingShingle | Daniel P. Furtado Edson A. Vieira Wildna Fernandes Nascimento Kelly Y. Inagaki Jessica Bleuel Marco Antonio Zanata Alves Guilherme O. Longo Luiz S. Oliveira #DeOlhoNosCorais: a polygonal annotated dataset to optimize coral monitoring PeerJ Convolutional neural network Machine learning Computer vision Marine ecology |
| title | #DeOlhoNosCorais: a polygonal annotated dataset to optimize coral monitoring |
| title_full | #DeOlhoNosCorais: a polygonal annotated dataset to optimize coral monitoring |
| title_fullStr | #DeOlhoNosCorais: a polygonal annotated dataset to optimize coral monitoring |
| title_full_unstemmed | #DeOlhoNosCorais: a polygonal annotated dataset to optimize coral monitoring |
| title_short | #DeOlhoNosCorais: a polygonal annotated dataset to optimize coral monitoring |
| title_sort | deolhonoscorais a polygonal annotated dataset to optimize coral monitoring |
| topic | Convolutional neural network Machine learning Computer vision Marine ecology |
| url | https://peerj.com/articles/16219.pdf |
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