The future of fish age estimation: deep machine learning coupled with Fourier transform near-infrared spectroscopy of otoliths

Details

• Journal Title:
  Canadian Journal of Fisheries and Aquatic Sciences
• Personal Author:
  Benson, Irina M. ; Helser, Thomas E. ; Marchetti, Giovanni ; Barnett, Beverly K.
• NOAA Program & Office:
  NMFS (National Marine Fisheries Service) ; AFSC (Alaska Fisheries Science Center) ; SEFSC (Southeast Fisheries Science Center)
• Description:
  Our novel approach for fish age prediction uses quantitative analysis of Fourier transform near-infrared (FT-NIR) spectra of otoliths by means of multimodal convolutional neural networks (MMCNN). We integrate two key data modalities that are related to fish ages: the entire range of wavenumbers of FT-NIR spectra and corresponding biological and geospatial data for nearly 9000 walleye pollock ( Gadus chalcogrammus) specimens. The proposed model extracts informative spectral features automatically and elucidates hidden structural relationships associated with fish growth to improve age predictions. Absorbance associated with 7000 to 4000 cm⁻¹ wavenumbers had the highest influence on model predictions followed by fish length, latitude, depth, and temperature. The optimal model resulted in good overall performance with an R² of 0.93 and RMSE of 0.83 for training data set and R² of 0.92 and RMSE of 0.91 for test data set. MMCNN's age predictions were comparable to microscope-based ages yielding as good or slightly better precision. Moreover, the model outperformed classical partial least squares analysis of otolith spectra and remedied prediction bias at older ages of fish.
• Keywords:
  Aquatic Science Ecology, Evolution, Behavior And Systematics Ecology, Evolution, Behavior And Systematics Ecology, Evolution, Behavior And Systematics
• Source:
  Canadian Journal of Fisheries and Aquatic Sciences (2023)
• DOI:
  https://doi.org/10.1139/cjfas-2023-0045
• ISSN:
  0706-652X ; 1205-7533
• Format:
  PDF
• Publisher:
  Canadian Science Publishing
• Document Type:
  Journal Article
• License:
  https://creativecommons.org/licenses/by/4.0/
• Rights Information:
  CC BY
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha256:bf127100b02a9a8bfd8bf233119113b5843a0c3e565ddeb4fede0fbecf774c19
• Download URL:
  https://repository.library.noaa.gov/view/noaa/50998/noaa_50998_DS1.pdf
• File Type:
  [PDF - 1.67 MB ]