A1 Journal article (refereed)
Assessment of microalgae species, biomass, and distribution from spectral images using a convolution neural network (2022)

Salmi, P., Calderini, M., Pääkkönen, S., Taipale, S., & Pölönen, I. (2022). Assessment of microalgae species, biomass, and distribution from spectral images using a convolution neural network. Journal of Applied Phycology, 34(3), 1565-1575. https://doi.org/10.1007/s10811-022-02735-w

JYU authors or editors

Publication details

All authors or editorsSalmi, Pauliina; Calderini, Marco; Pääkkönen, Salli; Taipale, Sami; Pölönen, Ilkka

Journal or seriesJournal of Applied Phycology



Publication year2022

Publication date13/04/2022


Issue number3

Pages range1565-1575

PublisherSpringer Science and Business Media LLC

Publication countryNetherlands

Publication languageEnglish


Research data linkhttps://doi.org/10.5281/zenodo.5061719

Publication open accessOpenly available

Publication channel open accessPartially open access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/80780

Additional informationTransmittance hyperspectral images of microalgae on the well plates are available via Zenodo repository with URL: https://doi.org/10.5281/zenodo.5061719 and https://doi.org/10.5281/zenodo.5061719. Other data, data descriptor, Python code, and the models constructed here, are available via JYX repository at the URI: http://urn.fi/URN:NBN:fi:jyu-202111085543 and https://doi.org/10.17011/jyx/dataset/78519.


Effective monitoring of microalgae growth is crucial for environmental observation, while the applications of this monitoring could also be expanded to commercial and research-focused microalgae cultivation. Currently, the distinctive optical properties of different microalgae groups are targeted for monitoring. Since different microalgae can grow together, their spectral signals are mixed with ambient properties, making estimations of species biomasses a challenging task. In this study, we cultured five different microalgae and monitored their growth with a mobile spectral imager in three separate experiments. We trained and validated a one-dimensional convolution neural network by introducing absorbance spectra of the cultured microalgae and simulated pairwise mixtures of them. We then tested the model with samples of microalgae (monocultures and their pairwise mixtures) that were not part of the training or validation data. The convolution neural network classified microalgae accurately in the monocultures (test accuracy = 95%, SD = 4) and in the pairwise mixtures (test accuracy = 100%, SD = 0). Median prediction errors for biomasses were 17% (mean = 22%, SD = 18) for the monocultures and 17% (mean 24%, SD = 28) for the pairwise mixtures. As the spectral camera produced spatial information of the imaged target, we also demonstrated here the spatial distribution of microalgae biomass by applying the model across 5 × 5 pixel areas of the spectral images. The results of this study encourage the application of a one-dimensional convolution neural network to solve classification, regression, and distribution problems related to microalgae observation, simultaneously.

Keywordsmicroalgaehyperspectral imagingmachine learningneural networks (information technology)optical propertiesbiomass (ecology)cultivationenvironmental research

Free keywordsmicroalgae monitoring; hyperspectral imaging; machine learning

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Ministry reportingYes

Reporting Year2022

JUFO rating1

Last updated on 2024-30-04 at 19:36