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

Research output: Contribution to journalReview articlepeer-review

19 Scopus citations

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.

Original languageBritish English
Article number128418
JournalBioresource Technology
Volume369
DOIs
StatePublished - Feb 2023

Keywords

  • Classification
  • Deep learning
  • Image pre-processing
  • Machine learning
  • Microalgae

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