A systematic review of deep learning microalgae classification and detection.
Algae represent the majority of the diversity on Earth and are a large group of organisms that have photosynthetic properties that are important to life. The species of algae are estimated to be more than 1 million, they play an important role in many fields such as agriculture, industry, food, and...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers Inc.
2023
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| Online Access: | http://eprints.utm.my/104887/1/DinaMMadkour2023_ASystematicReviewofDeepLearningMicroalgae.pdf |
| _version_ | 1796867746077605888 |
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| author | Madkour, Dina M. Shapiai, Mohd. Ibrahim Mohamad, Shaza Eva Aly, Hesham Hamdy Ismail, Zool Hilmi Ibrahim, Mohd. Zamri |
| author_facet | Madkour, Dina M. Shapiai, Mohd. Ibrahim Mohamad, Shaza Eva Aly, Hesham Hamdy Ismail, Zool Hilmi Ibrahim, Mohd. Zamri |
| author_sort | Madkour, Dina M. |
| collection | ePrints |
| description | Algae represent the majority of the diversity on Earth and are a large group of organisms that have photosynthetic properties that are important to life. The species of algae are estimated to be more than 1 million, they play an important role in many fields such as agriculture, industry, food, and medicine. It is important to determine the type of algae, to determine if it is harmful or useful, and to indicate the health of the ecosystem, water quality, health, and safety risks. The conventional process of classifying algae is difficult, tedious, and time-consuming. Recently various computer vision techniques have been used to classify algae to overcome challenges and automate the process of classification. This paper presents a review of research done on image classification for microorganism algae using machine learning and deep learning techniques. The paper focuses on three important research questions to highlight the challenges of classifying microalgae. A systematic literature review or SLR has been conducted to determine how deep learning and machine learning have improved and enhanced automatic microalgae classification rather than manual classification. 51 articles have been included from well-known databases. The outcome of this SLR is beneficial due to the detailed analysis and comprehensive overview of the algorithms and the architectures and information about the dataset used in each included article. The future work focuses on getting a large dataset with high resolution, trying different methods to manage imbalance problems, and giving more attention to the fusion of deep learning techniques and traditional machine learning techniques. |
| first_indexed | 2024-04-09T03:45:29Z |
| format | Article |
| id | utm.eprints-104887 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-04-09T03:45:29Z |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | utm.eprints-1048872024-03-25T09:18:57Z http://eprints.utm.my/104887/ A systematic review of deep learning microalgae classification and detection. Madkour, Dina M. Shapiai, Mohd. Ibrahim Mohamad, Shaza Eva Aly, Hesham Hamdy Ismail, Zool Hilmi Ibrahim, Mohd. Zamri TP Chemical technology Algae represent the majority of the diversity on Earth and are a large group of organisms that have photosynthetic properties that are important to life. The species of algae are estimated to be more than 1 million, they play an important role in many fields such as agriculture, industry, food, and medicine. It is important to determine the type of algae, to determine if it is harmful or useful, and to indicate the health of the ecosystem, water quality, health, and safety risks. The conventional process of classifying algae is difficult, tedious, and time-consuming. Recently various computer vision techniques have been used to classify algae to overcome challenges and automate the process of classification. This paper presents a review of research done on image classification for microorganism algae using machine learning and deep learning techniques. The paper focuses on three important research questions to highlight the challenges of classifying microalgae. A systematic literature review or SLR has been conducted to determine how deep learning and machine learning have improved and enhanced automatic microalgae classification rather than manual classification. 51 articles have been included from well-known databases. The outcome of this SLR is beneficial due to the detailed analysis and comprehensive overview of the algorithms and the architectures and information about the dataset used in each included article. The future work focuses on getting a large dataset with high resolution, trying different methods to manage imbalance problems, and giving more attention to the fusion of deep learning techniques and traditional machine learning techniques. Institute of Electrical and Electronics Engineers Inc. 2023-05-26 Article PeerReviewed application/pdf en http://eprints.utm.my/104887/1/DinaMMadkour2023_ASystematicReviewofDeepLearningMicroalgae.pdf Madkour, Dina M. and Shapiai, Mohd. Ibrahim and Mohamad, Shaza Eva and Aly, Hesham Hamdy and Ismail, Zool Hilmi and Ibrahim, Mohd. Zamri (2023) A systematic review of deep learning microalgae classification and detection. IEEE Access, 11 . pp. 57529-57555. ISSN 2169-3536 http://dx.doi.org/10.1109/ACCESS.2023.3280410 DOI: 10.1109/ACCESS.2023.3280410 |
| spellingShingle | TP Chemical technology Madkour, Dina M. Shapiai, Mohd. Ibrahim Mohamad, Shaza Eva Aly, Hesham Hamdy Ismail, Zool Hilmi Ibrahim, Mohd. Zamri A systematic review of deep learning microalgae classification and detection. |
| title | A systematic review of deep learning microalgae classification and detection. |
| title_full | A systematic review of deep learning microalgae classification and detection. |
| title_fullStr | A systematic review of deep learning microalgae classification and detection. |
| title_full_unstemmed | A systematic review of deep learning microalgae classification and detection. |
| title_short | A systematic review of deep learning microalgae classification and detection. |
| title_sort | systematic review of deep learning microalgae classification and detection |
| topic | TP Chemical technology |
| url | http://eprints.utm.my/104887/1/DinaMMadkour2023_ASystematicReviewofDeepLearningMicroalgae.pdf |
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