High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network.

ABSTRACT: Image processing technologies are available for high-throughput acquisition and analysis of phenotypes for crop populations, which is of great significance for crop growth monitoring, evaluation of seedling condition, and cultivation management. However, existing methods rely on empirical segmentation thresholds, thus can have insufficient accuracy of extracted phenotypes. Taking maize as an example crop, we propose a phenotype extraction approach from top-view images at the seedling stage. An end-to-end segmentation network, named PlantU-net, which uses a small amount of training data, was explored to realize automatic segmentation of top-view images of a maize population at the seedling stage. Morphological and color related phenotypes were automatic extracted, including maize shoot cove

SUBMITTER: Li Y 

PROVIDER: S-EPMC7802938 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature