Academic Bibliography

 Search 200 years of publications by Ghent University researchers.
Advanced search
1 file | 1.13 MB
Add to list
  1. Search results
  2. Machine learning techniques to charac...

Machine learning techniques to characterize functional traits of plankton from image data

Eric C. Orenstein, Sakina‐Dorothée Ayata, Frédéric Maps, Érica C. Becker, Fabio Benedetti, Tristan Biard, Thibault de Garidel‐Thoron, Jeffrey S. Ellen, Filippo Ferrario, Sarah L. C. Giering, et al.
(2022) LIMNOLOGY AND OCEANOGRAPHY. 67(8). p.1647-1669
Author
Organization
Project
Abstract
Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.
Keywords
DIEL VERTICAL MIGRATION, LIFE-HISTORY TRAITS, PELAGIC COPEPODS, PHOTOSYNTHETIC CHARACTERISTICS, GONAD MORPHOLOGY, MARINE COPEPODS, CELL-VOLUME, SIZE, PHYTOPLANKTON, FLUORESCENCE

Downloads

  • Download
    KERMIT-A1-690.pdf
    • full text (Published version)
    • |
    • open access
    • |
    • PDF
    • |
    • 1.13 MB

Citation

Please use this url to cite or link to this publication:

MLA
Orenstein, Eric C., et al. “Machine Learning Techniques to Characterize Functional Traits of Plankton from Image Data.” LIMNOLOGY AND OCEANOGRAPHY, vol. 67, no. 8, 2022, pp. 1647–69, doi:10.1002/lno.12101.
APA
Orenstein, E. C., Ayata, S., Maps, F., Becker, É. C., Benedetti, F., Biard, T., … Irisson, J. (2022). Machine learning techniques to characterize functional traits of plankton from image data. LIMNOLOGY AND OCEANOGRAPHY, 67(8), 1647–1669. https://doi.org/10.1002/lno.12101
Chicago author-date
Orenstein, Eric C., Sakina‐Dorothée Ayata, Frédéric Maps, Érica C. Becker, Fabio Benedetti, Tristan Biard, Thibault de Garidel‐Thoron, et al. 2022. “Machine Learning Techniques to Characterize Functional Traits of Plankton from Image Data.” LIMNOLOGY AND OCEANOGRAPHY 67 (8): 1647–69. https://doi.org/10.1002/lno.12101.
Chicago author-date (all authors)
Orenstein, Eric C., Sakina‐Dorothée Ayata, Frédéric Maps, Érica C. Becker, Fabio Benedetti, Tristan Biard, Thibault de Garidel‐Thoron, Jeffrey S. Ellen, Filippo Ferrario, Sarah L. C. Giering, Tamar Guy‐Haim, Laura Hoebeke, Morten Hvitfeldt Iversen, Thomas Kiørboe, Jean‐François Lalonde, Arancha Lana, Martin Laviale, Fabien Lombard, Tom Lorimer, Séverine Martini, Albin Meyer, Klas Ove Möller, Barbara Niehoff, Mark D. Ohman, Cédric Pradalier, Jean‐Baptiste Romagnan, Simon‐Martin Schröder, Virginie Sonnet, Heidi M. Sosik, Lars S. Stemmann, Michiel Stock, Tuba Terbiyik‐Kurt, Nerea Valcárcel‐Pérez, Laure Vilgrain, Guillaume Wacquet, Anya M. Waite, and Jean‐Olivier Irisson. 2022. “Machine Learning Techniques to Characterize Functional Traits of Plankton from Image Data.” LIMNOLOGY AND OCEANOGRAPHY 67 (8): 1647–1669. doi:10.1002/lno.12101.
Vancouver
1.
Orenstein EC, Ayata S, Maps F, Becker ÉC, Benedetti F, Biard T, et al. Machine learning techniques to characterize functional traits of plankton from image data. LIMNOLOGY AND OCEANOGRAPHY. 2022;67(8):1647–69.
IEEE
[1]
E. C. Orenstein et al., “Machine learning techniques to characterize functional traits of plankton from image data,” LIMNOLOGY AND OCEANOGRAPHY, vol. 67, no. 8, pp. 1647–1669, 2022.
@article{8763955,
  abstract     = {{Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.}},
  author       = {{Orenstein, Eric C. and Ayata, Sakina‐Dorothée and Maps, Frédéric and Becker, Érica C. and Benedetti, Fabio and Biard, Tristan and de Garidel‐Thoron, Thibault and Ellen, Jeffrey S. and Ferrario, Filippo and Giering, Sarah L. C. and Guy‐Haim, Tamar and Hoebeke, Laura and Iversen, Morten Hvitfeldt and Kiørboe, Thomas and Lalonde, Jean‐François and Lana, Arancha and Laviale, Martin and Lombard, Fabien and Lorimer, Tom and Martini, Séverine and Meyer, Albin and Möller, Klas Ove and Niehoff, Barbara and Ohman, Mark D. and Pradalier, Cédric and Romagnan, Jean‐Baptiste and Schröder, Simon‐Martin and Sonnet, Virginie and Sosik, Heidi M. and Stemmann, Lars S. and Stock, Michiel and Terbiyik‐Kurt, Tuba and Valcárcel‐Pérez, Nerea and Vilgrain, Laure and Wacquet, Guillaume and Waite, Anya M. and Irisson, Jean‐Olivier}},
  issn         = {{0024-3590}},
  journal      = {{LIMNOLOGY AND OCEANOGRAPHY}},
  keywords     = {{DIEL VERTICAL MIGRATION,LIFE-HISTORY TRAITS,PELAGIC COPEPODS,PHOTOSYNTHETIC CHARACTERISTICS,GONAD MORPHOLOGY,MARINE COPEPODS,CELL-VOLUME,SIZE,PHYTOPLANKTON,FLUORESCENCE}},
  language     = {{eng}},
  number       = {{8}},
  pages        = {{1647--1669}},
  title        = {{Machine learning techniques to characterize functional traits of plankton from image data}},
  url          = {{http://doi.org/10.1002/lno.12101}},
  volume       = {{67}},
  year         = {{2022}},
}
Altmetric
View in Altmetric
Web of Science
Times cited: