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

Creative Commons License

Orenstein E. C., Ayata S., Maps F., Becker E. C., Benedetti F., Biard T., ...Daha Fazla

LIMNOLOGY AND OCEANOGRAPHY, cilt.67, sa.8, ss.1647-1669, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 67 Sayı: 8
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1002/lno.12101
  • Dergi Adı: LIMNOLOGY AND OCEANOGRAPHY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Animal Behavior Abstracts, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, BIOSIS, CAB Abstracts, Environment Index, Geobase, Pollution Abstracts, Veterinary Science Database
  • Sayfa Sayıları: ss.1647-1669
  • Çukurova Üniversitesi Adresli: Evet

Özet

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.