Towards cleaner waters: Advancing pollutant detection with artificial intelligence-assisted digital in-line holographic microscopy
<p>Rapid urbanization and population growth have raised significant concerns about water quality in the environment. This highlights the need for an efficient and user-friendly technique for real-time pollutant monitoring in aquatic environments. Digital In-line Holographic Microscopy (DIHM) e...
محفوظ في:
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| مؤلفون آخرون: | |
| منشور في: |
2025
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| _version_ | 1864513531540406272 |
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| author | R. Rarima (22927237) |
| author2 | S. Veerasingam (9648980) |
| author2_role | author |
| author_facet | R. Rarima (22927237) S. Veerasingam (9648980) |
| author_role | author |
| dc.creator.none.fl_str_mv | R. Rarima (22927237) S. Veerasingam (9648980) |
| dc.date.none.fl_str_mv | 2025-06-21T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.optlastec.2025.113402 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Towards_cleaner_waters_Advancing_pollutant_detection_with_artificial_intelligence-assisted_digital_in-line_holographic_microscopy/30970531 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Environmental sciences Environmental management Information and computing sciences Artificial intelligence Holography Microplastics Oil spill Pathogens Suspended particles Algal blooms |
| dc.title.none.fl_str_mv | Towards cleaner waters: Advancing pollutant detection with artificial intelligence-assisted digital in-line holographic microscopy |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Rapid urbanization and population growth have raised significant concerns about water quality in the environment. This highlights the need for an efficient and user-friendly technique for real-time pollutant monitoring in aquatic environments. Digital In-line Holographic Microscopy (DIHM) enables the development of portable systems capable of selectively detecting and classifying pollutants in real-time. Although, artificial intelligence (AI) requires large datasets for optimal performance, it has revolutionized data analysis, as reported in various studies. AI-assisted DIHM has the potential to reduce costs while enhancing the accurate detection and classification of organic, inorganic, and biological contaminants in water. This review compiles various AI methodologies used for processing holograms of different pollutants in aqueous environments. Additionally, it highlights a critical research gap: the need for robust software packages or computational models to improve the image quality of detected targets.</p><h2>Other Information</h2> <p> Published in: Optics & Laser Technology<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.optlastec.2025.113402" target="_blank">https://dx.doi.org/10.1016/j.optlastec.2025.113402</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_4b1bb21ff04bbc2f26f7d7c21a70396c |
| identifier_str_mv | 10.1016/j.optlastec.2025.113402 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30970531 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Towards cleaner waters: Advancing pollutant detection with artificial intelligence-assisted digital in-line holographic microscopyR. Rarima (22927237)S. Veerasingam (9648980)Environmental sciencesEnvironmental managementInformation and computing sciencesArtificial intelligenceHolographyMicroplasticsOil spillPathogensSuspended particlesAlgal blooms<p>Rapid urbanization and population growth have raised significant concerns about water quality in the environment. This highlights the need for an efficient and user-friendly technique for real-time pollutant monitoring in aquatic environments. Digital In-line Holographic Microscopy (DIHM) enables the development of portable systems capable of selectively detecting and classifying pollutants in real-time. Although, artificial intelligence (AI) requires large datasets for optimal performance, it has revolutionized data analysis, as reported in various studies. AI-assisted DIHM has the potential to reduce costs while enhancing the accurate detection and classification of organic, inorganic, and biological contaminants in water. This review compiles various AI methodologies used for processing holograms of different pollutants in aqueous environments. Additionally, it highlights a critical research gap: the need for robust software packages or computational models to improve the image quality of detected targets.</p><h2>Other Information</h2> <p> Published in: Optics & Laser Technology<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.optlastec.2025.113402" target="_blank">https://dx.doi.org/10.1016/j.optlastec.2025.113402</a></p>2025-06-21T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.optlastec.2025.113402https://figshare.com/articles/journal_contribution/Towards_cleaner_waters_Advancing_pollutant_detection_with_artificial_intelligence-assisted_digital_in-line_holographic_microscopy/30970531CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/309705312025-06-21T12:00:00Z |
| spellingShingle | Towards cleaner waters: Advancing pollutant detection with artificial intelligence-assisted digital in-line holographic microscopy R. Rarima (22927237) Environmental sciences Environmental management Information and computing sciences Artificial intelligence Holography Microplastics Oil spill Pathogens Suspended particles Algal blooms |
| status_str | publishedVersion |
| title | Towards cleaner waters: Advancing pollutant detection with artificial intelligence-assisted digital in-line holographic microscopy |
| title_full | Towards cleaner waters: Advancing pollutant detection with artificial intelligence-assisted digital in-line holographic microscopy |
| title_fullStr | Towards cleaner waters: Advancing pollutant detection with artificial intelligence-assisted digital in-line holographic microscopy |
| title_full_unstemmed | Towards cleaner waters: Advancing pollutant detection with artificial intelligence-assisted digital in-line holographic microscopy |
| title_short | Towards cleaner waters: Advancing pollutant detection with artificial intelligence-assisted digital in-line holographic microscopy |
| title_sort | Towards cleaner waters: Advancing pollutant detection with artificial intelligence-assisted digital in-line holographic microscopy |
| topic | Environmental sciences Environmental management Information and computing sciences Artificial intelligence Holography Microplastics Oil spill Pathogens Suspended particles Algal blooms |