Integration of Morphometrics and Machine Learning Enables Accurate Distinction between Wild and Farmed Common Carp
14 pages, 6 figures, 5 tables, supplementary materials https://doi.org/10.3390/life12070957.-- Data Availability Statement: All datasets generated for this study are included in the manuscript and in the Supplementary Materials
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| Format: | artículo biblioteca |
| Language: | English |
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Multidisciplinary Digital Publishing Institute
2022-06
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| Subjects: | Morphometrics, Machine learning, Fish morphology, Domestication, Fisheries management, Conserve and sustainably use the oceans, seas and marine resources for sustainable development, |
| Online Access: | http://hdl.handle.net/10261/357716 |
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dig-icm-es-10261-3577162024-05-21T09:43:37Z Integration of Morphometrics and Machine Learning Enables Accurate Distinction between Wild and Farmed Common Carp Jafari, Omid Ebrahimi, Mansour Hedayati, Seyed Ali-Akbar Zeinalabedini, Mehrshad Poorbagher, Hadi Nasrolahpourmoghadam, Maryam Fernandes, Jorge M. O. European Commission Morphometrics Machine learning Fish morphology Domestication Fisheries management Conserve and sustainably use the oceans, seas and marine resources for sustainable development 14 pages, 6 figures, 5 tables, supplementary materials https://doi.org/10.3390/life12070957.-- Data Availability Statement: All datasets generated for this study are included in the manuscript and in the Supplementary Materials Morphology and feature selection are key approaches to address several issues in fisheries science and stock management, such as the hypothesis of admixture of Caspian common carp (Cyprinus carpio) and farmed carp stocks in Iran. The present study was performed to investigate the population classification of common carp in the southern Caspian basin using data mining algorithms to find the most important characteristic(s) differing between Iranian and farmed common carp. A total of 74 individuals were collected from three locations within the southern Caspian basin and from one farm between November 2015 and April 2016. A dataset of 26 traditional morphometric (TMM) attributes and a dataset of 14 geometric landmark points were constructed and then subjected to various machine learning methods. In general, the machine learning methods had a higher prediction rate with TMM datasets. The highest decision tree accuracy of 77% was obtained by rule and decision tree parallel algorithms, and “head height on eye area” was selected as the best marker to distinguish between wild and farmed common carp. Various machine learning algorithms were evaluated, and we found that the linear discriminant was the best method, with 81.1% accuracy. The results obtained from this novel approach indicate that Darwin’s domestication syndrome is observed in common carp. Moreover, they pave the way for automated detection of farmed fish, which will be most beneficial to detect escapees and improve restocking programs This study received additional support from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program [Grant agreement no. 683210] and from the Research Council of Norway under the Toppforsk program [Grant agreement no. 250548/F20] Peer reviewed 2024-05-21T09:43:37Z 2024-05-21T09:43:37Z 2022-06 artículo Life 12(7): 957 (2022) http://hdl.handle.net/10261/357716 10.3390/life12070957 2075-1729 en #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/EC/H2020/683210 Publisher's version https://doi.org/10.3390/life12070957 Sí open Multidisciplinary Digital Publishing Institute |
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Morphometrics Machine learning Fish morphology Domestication Fisheries management Conserve and sustainably use the oceans, seas and marine resources for sustainable development Morphometrics Machine learning Fish morphology Domestication Fisheries management Conserve and sustainably use the oceans, seas and marine resources for sustainable development |
| spellingShingle |
Morphometrics Machine learning Fish morphology Domestication Fisheries management Conserve and sustainably use the oceans, seas and marine resources for sustainable development Morphometrics Machine learning Fish morphology Domestication Fisheries management Conserve and sustainably use the oceans, seas and marine resources for sustainable development Jafari, Omid Ebrahimi, Mansour Hedayati, Seyed Ali-Akbar Zeinalabedini, Mehrshad Poorbagher, Hadi Nasrolahpourmoghadam, Maryam Fernandes, Jorge M. O. Integration of Morphometrics and Machine Learning Enables Accurate Distinction between Wild and Farmed Common Carp |
| description |
14 pages, 6 figures, 5 tables, supplementary materials https://doi.org/10.3390/life12070957.-- Data Availability Statement: All datasets generated for this study are included in the manuscript and in the Supplementary Materials |
| author2 |
European Commission |
| author_facet |
European Commission Jafari, Omid Ebrahimi, Mansour Hedayati, Seyed Ali-Akbar Zeinalabedini, Mehrshad Poorbagher, Hadi Nasrolahpourmoghadam, Maryam Fernandes, Jorge M. O. |
| format |
artículo |
| topic_facet |
Morphometrics Machine learning Fish morphology Domestication Fisheries management Conserve and sustainably use the oceans, seas and marine resources for sustainable development |
| author |
Jafari, Omid Ebrahimi, Mansour Hedayati, Seyed Ali-Akbar Zeinalabedini, Mehrshad Poorbagher, Hadi Nasrolahpourmoghadam, Maryam Fernandes, Jorge M. O. |
| author_sort |
Jafari, Omid |
| title |
Integration of Morphometrics and Machine Learning Enables Accurate Distinction between Wild and Farmed Common Carp |
| title_short |
Integration of Morphometrics and Machine Learning Enables Accurate Distinction between Wild and Farmed Common Carp |
| title_full |
Integration of Morphometrics and Machine Learning Enables Accurate Distinction between Wild and Farmed Common Carp |
| title_fullStr |
Integration of Morphometrics and Machine Learning Enables Accurate Distinction between Wild and Farmed Common Carp |
| title_full_unstemmed |
Integration of Morphometrics and Machine Learning Enables Accurate Distinction between Wild and Farmed Common Carp |
| title_sort |
integration of morphometrics and machine learning enables accurate distinction between wild and farmed common carp |
| publisher |
Multidisciplinary Digital Publishing Institute |
| publishDate |
2022-06 |
| url |
http://hdl.handle.net/10261/357716 |
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