Deep Learning-Based Fish Health Monitoring and Diagnosis: A Review Article Swipe
Lazhar Khriji
,
Abdelmalek Kouadri
,
Majdi Mansouri
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3621487
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3621487
Fish in aquaculture systems face health challenges influenced by aging, water quality, and environmental conditions. These issues affect critical components like feeding and filtration, potentially reducing efficiency and causing system failure. Effective Health Monitoring and Diagnosis (HMD) relies on high-quality features such as behavior, physical condition, feeding habits, and water parameters. However, traditional hand-crafted approaches often fail to capture the complex and nonlinear interactions between biological and environmental factors, limiting their adaptability to sudden changes in water conditions or disease outbreaks. This gap motivates the use of intelligent, multimodal learning strategies that integrate diverse data sources for more robust and reliable analysis. Advances in computing power, large datasets, and sophisticated algorithms have made deep learning (DL) a transformative tool in this field. By combining DL with multimodal data integration, it becomes possible to learn high-level representations directly from heterogeneous inputs such as water quality measures, behavioral signals, and visual observations, thereby overcoming the limitations of conventional feature-based methods. This paper reviews DL-based multimodal approaches in aquaculture HMD, comparing recent techniques, their strengths, and limitations. We also discuss future directions, emphasizing multimodal data fusion to enhance DL-driven health monitoring. This review provides a concise resource for researchers and practitioners aiming to advance aquaculture health monitoring.
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- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2025.3621487
- OA Status
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- 83
- OpenAlex ID
- https://openalex.org/W4415179063
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https://doi.org/10.1109/access.2025.3621487Digital Object Identifier
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Deep Learning-Based Fish Health Monitoring and Diagnosis: A ReviewWork title
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reviewOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
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Lazhar Khriji, Abdelmalek Kouadri, Majdi MansouriList of authors in order
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https://doi.org/10.1109/access.2025.3621487Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2025.3621487Direct OA link when available
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0Total citation count in OpenAlex
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83Number of works referenced by this work
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| abstract_inverted_index.hand-crafted | 53 |
| abstract_inverted_index.high-quality | 39 |
| abstract_inverted_index.integration, | 128 |
| abstract_inverted_index.intelligent, | 87 |
| abstract_inverted_index.interactions | 63 |
| abstract_inverted_index.limitations. | 173 |
| abstract_inverted_index.environmental | 13, 67 |
| abstract_inverted_index.feature-based | 156 |
| abstract_inverted_index.heterogeneous | 138 |
| abstract_inverted_index.observations, | 149 |
| abstract_inverted_index.practitioners | 197 |
| abstract_inverted_index.sophisticated | 109 |
| abstract_inverted_index.transformative | 117 |
| abstract_inverted_index.representations | 135 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.46478805 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |