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

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

Description

These data include hyperspectral images of microalgae cultivations, files for data preprocessing, principal component analysis (PCA), and semi-supervised anomaly detection models.
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Year of publication

2025

Authors

Norwegian University of Science and Technology

Salmi, Pauliina Orcid -palvelun logo - Creator

Informaatioteknologian tiedekunta

Pääkkönen, Salli Orcid -palvelun logo - Creator, Rights holder

Pölönen, Ilkka Orcid -palvelun logo - Creator

Other information

Fields of science

Computer and information sciences; Plant biology, microbiology, virology; Other engineering and technologies

Language

English

Open access

Open

License

Creative Commons Attribution 4.0 International (CC BY 4.0)

Keywords

machine learning, computational science, cyanobacteria, monitoring, Hyperspectral imaging, microalgae, green algae, contamination, model comparison, Microcystis aeruginosa
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