Salli Pääkkönen

Doctoral Researcher

Unit

Faculty of Information Technology

Room number

Ag C411.4

Email

salli.k.paakkonen@jyu.fi

Postal address

Mattilanniemi 2

Publications

Publication

2025

Accurate non-invasive quantification of astaxanthin content using hyperspectral images and machine learning

Algal Research.

Calderini, Marco L.

Pääkkönen, Salli

Yli-Tuomola, Aliisa

Timilsina, Hemanta

Pulkkinen, Katja

Pölönen, Ilkka

Salmi, Pauliina

Publication

2025

Semi-supervised anomaly detection from Chlorella vulgaris cultivations using hyperspectral imaging

Smart Agricultural Technology.

Pääkkönen, Salli

Pölönen, Ilkka

Salmi, Pauliina

Publication

2024

Lipid monitoring of Chlorella vulgaris using non-invasive near-infrared spectral imaging

Journal of Applied Phycology.

Pääkkönen, Salli

Pölönen, Ilkka

Calderini, Marco

Yli-Tuomola, Aliisa

Ruokolainen, Visa

Vihinen-Ranta, Maija

Salmi, Pauliina

Publication

2024

Non-invasive monitoring of microalgae cultivations using hyperspectral imager

Journal of Applied Phycology.

Pääkkönen, Salli

Pölönen, Ilkka

Raita-Hakola, Anna-Maria

Carneiro, Mariana

Cardoso, Helena

Mauricio, Dinis

Rodrigues, Alexandre Miguel Cavaco

Salmi, Pauliina

Publication

2024

Resolving phytoplankton pigments from spectral images using convolutional neural networks

Limnology and Oceanography: Methods.

Salmi, Pauliina

Pölönen, Ilkka

Beckmann, Daniel Atton

Calderini, Marco L.

May, Linda

Olszewska, Justyna

Perozzi, Laura

Pääkkönen, Salli

Taipale, Sami

Hunter, Peter

Publication

2023

Bean goose migration shows a long-term temporal shift to earlier spring, but not to later autumn migration in Finland

Ornis Fennica.

Kortesalmi, Pihla

Pääkkönen, Salli

Valkonen, Janne

Nokelainen, Ossi

Publication

2023

Temperature, phosphorus and species composition will all influence phytoplankton production and content of polyunsaturated fatty acids

Journal of Plankton Research.

Calderini, Marco L.

Pääkkönen, Salli

Salmi, Pauliina

Peltomaa, Elina

Taipale, Sami J.

Publication

2022

Assessment of microalgae species, biomass, and distribution from spectral images using a convolution neural network

Journal of Applied Phycology.

Salmi, Pauliina

Calderini, Marco

Pääkkönen, Salli

Taipale, Sami

Pölönen, Ilkka

Projects

MIDAS - Microalgae Digital Applications

The MIDAS consortium develops the integration and concatenation of microalgae bioprocesses by means of sensorics and modeling.

ROBA - Robust Algae Systems

This project focuses on carbon capture using microalgae and on-site monitoring methods of microalgae cultivations.