Dataset Information

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 time) traits, we demonstrate high heritability and very high accuracy against the "ground-truth" values from visual scoring. Using the traits predicted by the network, we tested genotype-to-phenotype association using the deep learning phenotypes and uncovered novel epistatic interactions for flowering time. Enabled by the time-series high-throughput phenotyping, we describe a new phenotype as the rate of flowering and show heritable genetic control for this trait. CONCLUSIONS:We demonstrated a field-based high-throughput phenotyping approach using deep learning that can directly measure morphological and developmental phenotypes in genetic populations from field-based imaging. The deep learning approach presented here gives a conceptual advancement in high-throughput plant phenotyping because it can potentially estimate any trait in any plant species for which the combination of breeder scores and high-resolution images can be obtained, capturing the expert knowledge from breeders, geneticists, pathologists, and physiologists to train the networks.

SUBMITTER: Wang X

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

REPOSITORIES: biostudies-literature

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

Wang Xu X Xuan Hong H Evers Byron B Shrestha Sandesh S Pless Robert R Poland Jesse J

GigaScience 20191101 11

<h4>Background</h4>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.<h4>Results</h4>Here we demonstrate the application of deep learning on proximal imaging from a mobile field vehicle to directly estimate plant morphology and developmental stages ...[more]

PMID: 31742599

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Dataset Information

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

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

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