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Microalgae identification: Future of image processing and digital algorithm

The identification of microalgae species is an important tool in scientific research and commercial application to prevent harmful algae blooms (HABs) and recognizing potential microalgae strains for the bioaccumulation of valuable bioactive ingredients. The aim of this study is to incorporate rapid...

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Main Authors:	Chong, Jun Wei Roy, Khoo, Kuan Shiong, Chew, Kit Wayne, Vo, Dai-Viet N, Balakrishnan, Deepanraj, Banat, Fawzi, Heli Siti Halimatul Munawaroh, Heli Siti Halimatul Munawaroh, Koji, Iwamoto, Show, Pau Loke
Format:	Article
Published:	Elsevier Ltd 2023
Subjects:	T Technology (General)
Online Access:	http://eprints.utm.my/105520/ http://dx.doi.org/10.1016/j.biortech.2022.128418
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id	my.utm.105520
record_format	eprints
spelling	my.utm.1055202024-04-30T08:12:21Z http://eprints.utm.my/105520/ Microalgae identification: Future of image processing and digital algorithm Chong, Jun Wei Roy Khoo, Kuan Shiong Chew, Kit Wayne Vo, Dai-Viet N Balakrishnan, Deepanraj Banat, Fawzi Heli Siti Halimatul Munawaroh, Heli Siti Halimatul Munawaroh Koji, Iwamoto Show, Pau Loke T Technology (General) The identification of microalgae species is an important tool in scientific research and commercial application to prevent harmful algae blooms (HABs) and recognizing potential microalgae strains for the bioaccumulation of valuable bioactive ingredients. The aim of this study is to incorporate rapid, high-accuracy, reliable, low-cost, simple, and state-of-the-art identification methods. Thus, increasing the possibility for the development of potential recognition applications, that could identify toxic-producing and valuable microalgae strains. Recently, deep learning (DL) has brought the study of microalgae species identification to a much higher depth of efficiency and accuracy. In doing so, this review paper emphasizes the significance of microalgae identification, and various forms of machine learning algorithms for image classification, followed by image pre-processing techniques, feature extraction, and selection for further classification accuracy. Future prospects over the challenges and improvements of potential DL classification model development, application in microalgae recognition, and image capturing technologies are discussed accordingly. Elsevier Ltd 2023 Article PeerReviewed Chong, Jun Wei Roy and Khoo, Kuan Shiong and Chew, Kit Wayne and Vo, Dai-Viet N and Balakrishnan, Deepanraj and Banat, Fawzi and Heli Siti Halimatul Munawaroh, Heli Siti Halimatul Munawaroh and Koji, Iwamoto and Show, Pau Loke (2023) Microalgae identification: Future of image processing and digital algorithm. Bioresource Technology, 369 (NA). NA-NA. ISSN 0960-8524 http://dx.doi.org/10.1016/j.biortech.2022.128418 DOI : 10.1016/j.biortech.2022.128418
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	T Technology (General)
spellingShingle	T Technology (General) Chong, Jun Wei Roy Khoo, Kuan Shiong Chew, Kit Wayne Vo, Dai-Viet N Balakrishnan, Deepanraj Banat, Fawzi Heli Siti Halimatul Munawaroh, Heli Siti Halimatul Munawaroh Koji, Iwamoto Show, Pau Loke Microalgae identification: Future of image processing and digital algorithm
description	The identification of microalgae species is an important tool in scientific research and commercial application to prevent harmful algae blooms (HABs) and recognizing potential microalgae strains for the bioaccumulation of valuable bioactive ingredients. The aim of this study is to incorporate rapid, high-accuracy, reliable, low-cost, simple, and state-of-the-art identification methods. Thus, increasing the possibility for the development of potential recognition applications, that could identify toxic-producing and valuable microalgae strains. Recently, deep learning (DL) has brought the study of microalgae species identification to a much higher depth of efficiency and accuracy. In doing so, this review paper emphasizes the significance of microalgae identification, and various forms of machine learning algorithms for image classification, followed by image pre-processing techniques, feature extraction, and selection for further classification accuracy. Future prospects over the challenges and improvements of potential DL classification model development, application in microalgae recognition, and image capturing technologies are discussed accordingly.
format	Article
author	Chong, Jun Wei Roy Khoo, Kuan Shiong Chew, Kit Wayne Vo, Dai-Viet N Balakrishnan, Deepanraj Banat, Fawzi Heli Siti Halimatul Munawaroh, Heli Siti Halimatul Munawaroh Koji, Iwamoto Show, Pau Loke
author_facet	Chong, Jun Wei Roy Khoo, Kuan Shiong Chew, Kit Wayne Vo, Dai-Viet N Balakrishnan, Deepanraj Banat, Fawzi Heli Siti Halimatul Munawaroh, Heli Siti Halimatul Munawaroh Koji, Iwamoto Show, Pau Loke
author_sort	Chong, Jun Wei Roy
title	Microalgae identification: Future of image processing and digital algorithm
title_short	Microalgae identification: Future of image processing and digital algorithm
title_full	Microalgae identification: Future of image processing and digital algorithm
title_fullStr	Microalgae identification: Future of image processing and digital algorithm
title_full_unstemmed	Microalgae identification: Future of image processing and digital algorithm
title_sort	microalgae identification: future of image processing and digital algorithm
publisher	Elsevier Ltd
publishDate	2023
url	http://eprints.utm.my/105520/ http://dx.doi.org/10.1016/j.biortech.2022.128418
_version_	1797906038214623232
score	13.226694

Microalgae identification: Future of image processing and digital algorithm

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