Microalgae identification: Future of image processing and digital algorithm

Jun Wei Roy Chong; Kuan Shiong Khoo; Kit Wayne Chew; Dai-Viet N Vo; Deepanraj Balakrishnan; Fawzi Banat; Heli Siti Halimatul Munawaroh; Koji Iwamoto; Pau Loke Show

doi:10.1016/j.biortech.2022.128418

Microalgae identification: Future of image processing and digital algorithm

Bioresour Technol. 2023 Feb:369:128418. doi: 10.1016/j.biortech.2022.128418. Epub 2022 Dec 5.

Authors

Jun Wei Roy Chong¹, Kuan Shiong Khoo², Kit Wayne Chew³, Dai-Viet N Vo⁴, Deepanraj Balakrishnan⁵, Fawzi Banat⁶, Heli Siti Halimatul Munawaroh⁷, Koji Iwamoto⁸, Pau Loke Show⁹

Affiliations

¹ Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia Campus, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia.
² Department of Chemical Engineering and Materials Science, Yuan Ze University, Taoyuan, Taiwan; Centre for Research and Graduate Studies, University of Cyberjaya, Persiaran Bestari, 63000 Cyberjaya, Selangor, Malaysia.
³ School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore, 637459, Singapore.
⁴ Institute of Applied Technology and Sustainable Development, Nguyen Tat Thanh University, Ho Chi Minh City 755414, Vietnam.
⁵ Department of Mechanical Engineering, College of Engineering, Prince Mohammad Bin Fahd University, Al Khobar, 31952, Saudi Arabia.
⁶ Department of Chemical Engineering, Khalifa University, P.O Box 127788, Abu Dhabi, United Arab Emirates.
⁷ Study Program of Chemistry, Department of Chemistry Education, Universitas Pendidikan Indonesia, Bandung 40154, West Java, Indonesia.
⁸ Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia.
⁹ Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia Campus, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia; Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Sustainable Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India. Electronic address: showpauloke@gmail.com.

PMID: 36470491
DOI: 10.1016/j.biortech.2022.128418

Abstract

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.

Keywords: Classification; Deep learning; Image pre-processing; Machine learning; Microalgae.

Publication types

Review

MeSH terms

Algorithms
Harmful Algal Bloom
Image Processing, Computer-Assisted / methods
Microalgae*
Technology