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| Main Authors: | , , |
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| Format: | Artículo científico |
| Language: | en |
| Published: |
PloS one
2025
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| Subjects: | |
| Online Access: | https://pubmed.ncbi.nlm.nih.gov/40720494/ |
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| _version_ | 1868266172008366083 |
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| author | Harlow, Leander Ovchinnikova, Katja James, Mark |
| author_facet | Harlow, Leander Ovchinnikova, Katja James, Mark Harlow, Leander Ovchinnikova, Katja James, Mark |
| collection | PubMed - marine biology |
| contents | Neural network-based identification for scallops (Pecten maximus) in natural marine habitats. Harlow, Leander Ovchinnikova, Katja James, Mark Animals Ecosystem Neural Networks, Computer Pectinidae Fisheries Conservation of Natural Resources Video Recording The Great Atlantic scallop, or King scallop (Pecten maximus), ranks third in value after mackerel and Nephrops in UK fisheries. Its landings have surged over recent decades, making it the UK's fastest-growing fishery. Scallop stock assessments, crucial for sustainable fisheries management, traditionally rely on fisheries surveys, including underwater imaging and dredge sampling. Data on areas that contain scallops but not fishable using dredges is lacking. Dredge sampling is also potentially destructive. Remote data collection using drop down cameras and towed video are used, but there are few tools available to analyse these data automatically. P. maximus are usually recessed in fine sand and gravel habitats making image identification challenging. This study explores the potential of Artificial Intelligence (AI), specifically the NetHarn model from the VIAME toolkit, to identify and count scallops from underwater video transects. The research utilises diverse video footage from NatureScot, captured with custom camera systems (DDV and miniDDV), providing varied habitat, image quality, and camera specifications. Previous AI studies of this species artificially placed scallops on the seabed and are not representative of natural presentation. This research applies the same AI model to survey images featuring scallops in their natural habitat. Results showed moderate performance of the NetHarn model, achieving an F1 score of 0.44 and a mean Average Precision (mAP) of 0.41 when classifying scallops into three categories: king, queen, and dead. Model performance varied across geographic locations, camera platforms, and habitat types, with challenges including blurred images and mislabelling. The study emphasises the need for improved data acquisition, standardised camera systems, and larger annotated datasets to enhance AI model performance. Despite moderate results, this research highlights AI's potential for automating estimation of scallop stock abundance and marine habitat monitoring. Future efforts should focus on addressing image quality issues, increasing sample sizes, and optimising data collection for enhanced marine conservation and fisheries management. |
| format | Artículo científico |
| id | pubmed_40720494 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | PloS one |
| record_format | pubmed |
| spellingShingle | Neural network-based identification for scallops (Pecten maximus) in natural marine habitats. Harlow, Leander Ovchinnikova, Katja James, Mark Animals Ecosystem Neural Networks, Computer Pectinidae Fisheries Conservation of Natural Resources Video Recording Neural network-based identification for scallops (Pecten maximus) in natural marine habitats. Harlow, Leander Ovchinnikova, Katja James, Mark Animals Ecosystem Neural Networks, Computer Pectinidae Fisheries Conservation of Natural Resources Video Recording The Great Atlantic scallop, or King scallop (Pecten maximus), ranks third in value after mackerel and Nephrops in UK fisheries. Its landings have surged over recent decades, making it the UK's fastest-growing fishery. Scallop stock assessments, crucial for sustainable fisheries management, traditionally rely on fisheries surveys, including underwater imaging and dredge sampling. Data on areas that contain scallops but not fishable using dredges is lacking. Dredge sampling is also potentially destructive. Remote data collection using drop down cameras and towed video are used, but there are few tools available to analyse these data automatically. P. maximus are usually recessed in fine sand and gravel habitats making image identification challenging. This study explores the potential of Artificial Intelligence (AI), specifically the NetHarn model from the VIAME toolkit, to identify and count scallops from underwater video transects. The research utilises diverse video footage from NatureScot, captured with custom camera systems (DDV and miniDDV), providing varied habitat, image quality, and camera specifications. Previous AI studies of this species artificially placed scallops on the seabed and are not representative of natural presentation. This research applies the same AI model to survey images featuring scallops in their natural habitat. Results showed moderate performance of the NetHarn model, achieving an F1 score of 0.44 and a mean Average Precision (mAP) of 0.41 when classifying scallops into three categories: king, queen, and dead. Model performance varied across geographic locations, camera platforms, and habitat types, with challenges including blurred images and mislabelling. The study emphasises the need for improved data acquisition, standardised camera systems, and larger annotated datasets to enhance AI model performance. Despite moderate results, this research highlights AI's potential for automating estimation of scallop stock abundance and marine habitat monitoring. Future efforts should focus on addressing image quality issues, increasing sample sizes, and optimising data collection for enhanced marine conservation and fisheries management. |
| title | Neural network-based identification for scallops (Pecten maximus) in natural marine habitats. |
| topic | Animals Ecosystem Neural Networks, Computer Pectinidae Fisheries Conservation of Natural Resources Video Recording |
| url | https://pubmed.ncbi.nlm.nih.gov/40720494/ |