High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat.

ABSTRACT:

Background

Measurement of plant traits with precision and speed on large populations has emerged as a critical bottleneck in connecting genotype to phenotype in genetics and breeding. This bottleneck limits advancements in understanding plant genomes and the development of improved, high-yielding crop varieties.

Results

Here we demonstrate the application of deep learning on proximal imaging from a mobile field vehicle to directly estimate plant morphology and developmental stages in wheat under field conditions. We developed and trained a convolutional neural network with image datasets labeled from expert visual scores and used this "breeder-trained" network to classify wheat morphology and developmental stages. For both morphological (awned) and phenological (flowering ti

SUBMITTER: Wang X 

PROVIDER: S-EPMC6862935 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature