Mask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy.

ABSTRACT: The recent developments in artificial intelligence have the potential to facilitate new research methods in ecology. Especially Deep Convolutional Neural Networks (DCNNs) have been shown to outperform other approaches in automatic image analyses. Here we apply a DCNN to facilitate quantitative wood anatomical (QWA) analyses, where the main challenges reside in the detection of a high number of cells, in the intrinsic variability of wood anatomical features, and in the sample quality. To properly classify and interpret features within the images, DCNNs need to undergo a training stage. We performed the training with images from transversal wood anatomical sections, together with manually created optimal outputs of the target cell areas. The target species included an example for the most co

SUBMITTER: Resente G 

PROVIDER: S-EPMC8601631 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature