Image-based taxonomic classification of bulk biodiversity samples using deep learning and domain adaptation

Tomochika Fujisawa, VĂ­ctor Noguerales, Emmanouil Meramveliotakis, Anna Papadopoulou & Alfried Vogler
Complex bulk samples of invertebrates from biodiversity surveys present a great challenge for taxonomic identification, especially if obtained from unexplored ecosystems. High-throughput imaging combined with machine learning for rapid classification could overcome this bottleneck. Developing such procedures requires that taxonomic labels from an existing source data set are used for model training and prediction of an unknown target sample. Yet the feasibility of transfer learning for the classification of unknown samples remains to be tested....

Registration Year

  • 2022
    1

Resource Types

  • Dataset
    1

Affiliations

  • Shiga University
    1
  • Instituto de Productos Naturales y AgrobiologĂ­a
    1
  • University of Cyprus
    1
  • Natural History Museum
    1