Accurate image-based identification of macroinvertebrate specimens using deep learning-How much training data is needed?

ABSTRACT: Image-based methods for species identification offer cost-efficient solutions for biomonitoring. This is particularly relevant for invertebrate studies, where bulk samples often represent insurmountable workloads for sorting, identifying, and counting individual specimens. On the other hand, image-based classification using deep learning tools have strict requirements for the amount of training data, which is often a limiting factor. Here, we examine how classification accuracy increases with the amount of training data using the BIODISCOVER imaging system constructed for image-based classification and biomass estimation of invertebrate specimens. We use a balanced dataset of 60 specimens of each of 16 taxa of freshwater macroinvertebrates to systematically quantify how classification perf

SUBMITTER: Hoye TT 

PROVIDER: S-EPMC9415355 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature