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Deep Image Analysis for Microalgae Identification

  • Conference paper
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Information Integration and Web Intelligence (iiWAS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14416))

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Abstract

The current management of microalgae cultivation requires manual microscopic examination in order to identify desired and competing species, as well as predators. In this study, we trained and tested a transfer learning model modified from EfficientNetV2 B3 model on 434 and 161 prospectively acquired images of the preferred Nanno-chloropsis sp microalgae and competitor Spirulina, respectively, and achieved >98% classification for both species on tenfold cross-validation. The model was further enhanced with gradient-weighted class activation mapping, which allowed visualisation of regions of the input images that were relevant to the classification, thereby improving its explainability. In this paper, we demonstrate that a simple deep transfer learning model can help microalgae farmers to identify and manage microalgae species. The application could enable the widespread adoption of microalgae by more farmers as an enviroment-friendly, drought-proof, and high-productive crop that can be grown on non-arable land and use waste water.

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Data Availability Statement:

Data are available on request to the Corresponding Author.

Conflicts of Interest:

The authors declare no conflict of interest.

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Acknowledgments

The authors acknowledge AlgaePharm, Goondiwindi, Australia for the supply of all images that were used in the research.

Funding

This research received no external funding.

Institutional Review Board Statement:

Not applicable.

Author information

Authors and Affiliations

  1. School of Business, University of Southern Queensland, Springfield, Australia

    Jeffrey Soar & Prabal Datta Barua

  2. Ngee Ann Polytechnic, Singapore, Singapore

    Oh Shu Lih

  3. School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore

    Loh Hui Wen

  4. School of Nursing and Midwifery, University of Southern Queensland, Ipswich, Australia

    Aletha Ward

  5. School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia

    Ekta Sharma, Ravinesh C. Deo & U Rajendra Acharya

  6. Singapore National Heart Centre Singapore, and Duke-NUS Medical School, Singapore, Singapore

    Ru-San Tan

  7. AlgaePharm, Goondiwindi, 4390, Australia

    Eliezer Rinen

Authors
  1. Jeffrey Soar
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Contributions

Conceptualisation, J.S., R.A., A.W.; methodology, R.A.; software, O.D.L, L.H.W.; validation, J.S., R.A., E.S, R.C.D.; formal analysis, R.A., E.S., R.C.D., O.S.L, L.H.W; investigation, J.S., R.A, O.S.L, E.S, R.C.D., A.W., L.H.W.; resources, E.R., L.H.W.; data curation, E.R., E.S, R.C.D.; writing—original draft preparation, J.S., R.A., E.S., R.C.D.; writing—review and editing, J.S., R.A., E.S, R.C.D., A.W.; visualisation, J.S., R.A.; supervision and project administration, J.S., R.A.; data acquisition, E.R. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Jeffrey Soar .

Editor information

Editors and Affiliations

  1. Monash University, Melbourne, VIC, Australia

    Pari Delir Haghighi

  2. La Trobe University, Melbourne, VIC, Australia

    Eric Pardede

  3. University of Auckland, Auckland, New Zealand

    Gillian Dobbie

  4. University of Auckland, Auckland, New Zealand

    Vithya Yogarajan

  5. Udayana University, Denpasar, Indonesia

    Ngurah Agus Sanjaya ER

  6. Johannes Kepler University Linz, Linz, Austria

    Gabriele Kotsis

  7. Johannes Kepler University Linz, Linz, Austria

    Ismail Khalil

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Soar, J. et al. (2023). Deep Image Analysis for Microalgae Identification. In: Delir Haghighi, P., et al. Information Integration and Web Intelligence. iiWAS 2023. Lecture Notes in Computer Science, vol 14416. Springer, Cham. https://doi.org/10.1007/978-3-031-48316-5_28

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  • DOI: https://doi.org/10.1007/978-3-031-48316-5_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48315-8

  • Online ISBN: 978-3-031-48316-5

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