A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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-tekijät tai -toimittajat

Salmi, Pauliina
Calderini, Marco
Pääkkönen, Salli
Taipale, Sami
Pölönen, Ilkka

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Salmi, Pauliina; Calderini, Marco; Pääkkönen, Salli; Taipale, Sami; Pölönen, Ilkka

Lehti tai sarja: Journal of Applied Phycology

ISSN: 0921-8971

eISSN: 1573-5176

Julkaisuvuosi: 2022

Ilmestymispäivä: 13.04.2022

Volyymi: 34

Lehden numero: 3

Artikkelin sivunumerot: 1565-1575

Kustantaja: Springer Science and Business Media LLC

Julkaisumaa: Alankomaat

Julkaisun kieli: englanti

DOI: https://doi.org/10.1007/s10811-022-02735-w

Linkki tutkimusaineistoon: https://doi.org/10.5281/zenodo.5061719

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/80780

Lisätietoja: Transmittance 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.

Tiivistelmä

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.

YSO-asiasanat: mikrolevät; hyperspektrikuvantaminen; koneoppiminen; neuroverkot; optiset ominaisuudet; biomassa (ekologia); viljely; ympäristötutkimus

Vapaat asiasanat: microalgae monitoring; hyperspectral imaging; machine learning

Liittyvät organisaatiot

Hankkeet, joissa julkaisu on tehty

Liittyvät tutkimusaineistot

OKM-raportointi: Kyllä

VIRTA-lähetysvuosi: 2022

JUFO-taso: 1