AI-based coral species discrimination: A case study of the Siderastrea Atlantic Complex.

ABSTRACT: Species delimitation in hard corals remains controversial even after 250+ years of taxonomy. Confusing taxonomy in Scleractinia is not the result of sloppy work: clear boundaries are hard to draw because most diagnostic characters are quantitative and subjected to considerable morphological plasticity. In this study, we argue that taxonomists may actually be able to visually discriminate among morphospecies, but fail to translate their visual perception into accurate species descriptions. In this article, we introduce automated quantification of morphological traits using computer vision (Completed Local Binary Patterns-CLBP) and test its efficiency on the problematic genus Siderastrea. An artificial neural network employing fuzzy logic (Θ-FAM), intrinsically formulated to deal with soft a

SUBMITTER: Barbeitos MS 

PROVIDER: S-EPMC11634003 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature