Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies.

ABSTRACT: Association mapping studies have enabled researchers to identify candidate loci for many important environmental tolerance factors, including agronomically relevant tolerance traits in plants. However, traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately measuring stress responses, typically in an automated high-throughput context using image processing. In this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response-to-treatment directly from images. We demonstrate example applications using data from an interspecific cross of the model C₄ grass Setaria, a diversity panel of sorghum (S. bicolor), and the founder panel for

SUBMITTER: Ubbens J 

PROVIDER: S-EPMC7706325 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature