Image-based high-throughput field phenotyping of crop roots

Alexander Bucksch, James Burridge, Larry M. York, Abhiram Das, Eric Nord, Joshua S. Weitz, Jonathan Paul Lynch

Research output: Contribution to journalArticle

96 Citations (Scopus)

Abstract

Current plant phenotyping technologies to characterize agriculturally relevant traits have been primarily developed for use in laboratory and/or greenhouse conditions. In the case of root architectural traits, this limits phenotyping efforts, largely, to young plants grown in specialized containers and growth media. Hence, novel approaches are required to characterize mature root systems of older plants grown under actual soil conditions in the field. Imaging methods able to address the challenges associated with characterizing mature root systems are rare due, in part, to the greater complexity of mature root systems, including the larger size, overlap, and diversity of root components. Our imaging solution combines a field-imaging protocol and algorithmic approach to analyze mature root systems grown in the field. Via two case studies, we demonstrate how image analysis can be utilized to estimate localized root traits that reliably capture heritable architectural diversity as well as environmentally induced architectural variation of both monocot and dicot plants. In the first study, we show that our algorithms and traits (including 13 novel traits inaccessible to manual estimation) can differentiate nine maize (Zea mays) genotypes 8 weeks after planting. The second study focuses on a diversity panel of 188 cowpea (Vigna unguiculata) genotypes to identify which traits are sufficient to differentiate genotypes even when comparing plants whose harvesting date differs up to 14 d. Overall, we find that automatically derived traits can increase both the speed and reproducibility of the trait estimation pipeline under field conditions.

Original languageEnglish (US)
Pages (from-to)470-486
Number of pages17
JournalPlant physiology
Volume166
Issue number2
DOIs
StatePublished - Oct 1 2014

Fingerprint

root systems
image analysis
phenotype
crops
Genotype
genotype
Zea mays
container-grown plants
harvest date
Vigna unguiculata
Magnoliopsida
Liliopsida
cowpeas
reproducibility
soil quality
culture media
planting
case studies
greenhouses
Soil

All Science Journal Classification (ASJC) codes

  • Physiology
  • Genetics
  • Plant Science

Cite this

Bucksch, A., Burridge, J., York, L. M., Das, A., Nord, E., Weitz, J. S., & Lynch, J. P. (2014). Image-based high-throughput field phenotyping of crop roots. Plant physiology, 166(2), 470-486. https://doi.org/10.1104/pp.114.243519
Bucksch, Alexander ; Burridge, James ; York, Larry M. ; Das, Abhiram ; Nord, Eric ; Weitz, Joshua S. ; Lynch, Jonathan Paul. / Image-based high-throughput field phenotyping of crop roots. In: Plant physiology. 2014 ; Vol. 166, No. 2. pp. 470-486.
@article{f261ac08072249fb87c063283d7d48fc,
title = "Image-based high-throughput field phenotyping of crop roots",
abstract = "Current plant phenotyping technologies to characterize agriculturally relevant traits have been primarily developed for use in laboratory and/or greenhouse conditions. In the case of root architectural traits, this limits phenotyping efforts, largely, to young plants grown in specialized containers and growth media. Hence, novel approaches are required to characterize mature root systems of older plants grown under actual soil conditions in the field. Imaging methods able to address the challenges associated with characterizing mature root systems are rare due, in part, to the greater complexity of mature root systems, including the larger size, overlap, and diversity of root components. Our imaging solution combines a field-imaging protocol and algorithmic approach to analyze mature root systems grown in the field. Via two case studies, we demonstrate how image analysis can be utilized to estimate localized root traits that reliably capture heritable architectural diversity as well as environmentally induced architectural variation of both monocot and dicot plants. In the first study, we show that our algorithms and traits (including 13 novel traits inaccessible to manual estimation) can differentiate nine maize (Zea mays) genotypes 8 weeks after planting. The second study focuses on a diversity panel of 188 cowpea (Vigna unguiculata) genotypes to identify which traits are sufficient to differentiate genotypes even when comparing plants whose harvesting date differs up to 14 d. Overall, we find that automatically derived traits can increase both the speed and reproducibility of the trait estimation pipeline under field conditions.",
author = "Alexander Bucksch and James Burridge and York, {Larry M.} and Abhiram Das and Eric Nord and Weitz, {Joshua S.} and Lynch, {Jonathan Paul}",
year = "2014",
month = "10",
day = "1",
doi = "10.1104/pp.114.243519",
language = "English (US)",
volume = "166",
pages = "470--486",
journal = "Plant Physiology",
issn = "0032-0889",
publisher = "American Society of Plant Biologists",
number = "2",

}

Bucksch, A, Burridge, J, York, LM, Das, A, Nord, E, Weitz, JS & Lynch, JP 2014, 'Image-based high-throughput field phenotyping of crop roots', Plant physiology, vol. 166, no. 2, pp. 470-486. https://doi.org/10.1104/pp.114.243519

Image-based high-throughput field phenotyping of crop roots. / Bucksch, Alexander; Burridge, James; York, Larry M.; Das, Abhiram; Nord, Eric; Weitz, Joshua S.; Lynch, Jonathan Paul.

In: Plant physiology, Vol. 166, No. 2, 01.10.2014, p. 470-486.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Image-based high-throughput field phenotyping of crop roots

AU - Bucksch, Alexander

AU - Burridge, James

AU - York, Larry M.

AU - Das, Abhiram

AU - Nord, Eric

AU - Weitz, Joshua S.

AU - Lynch, Jonathan Paul

PY - 2014/10/1

Y1 - 2014/10/1

N2 - Current plant phenotyping technologies to characterize agriculturally relevant traits have been primarily developed for use in laboratory and/or greenhouse conditions. In the case of root architectural traits, this limits phenotyping efforts, largely, to young plants grown in specialized containers and growth media. Hence, novel approaches are required to characterize mature root systems of older plants grown under actual soil conditions in the field. Imaging methods able to address the challenges associated with characterizing mature root systems are rare due, in part, to the greater complexity of mature root systems, including the larger size, overlap, and diversity of root components. Our imaging solution combines a field-imaging protocol and algorithmic approach to analyze mature root systems grown in the field. Via two case studies, we demonstrate how image analysis can be utilized to estimate localized root traits that reliably capture heritable architectural diversity as well as environmentally induced architectural variation of both monocot and dicot plants. In the first study, we show that our algorithms and traits (including 13 novel traits inaccessible to manual estimation) can differentiate nine maize (Zea mays) genotypes 8 weeks after planting. The second study focuses on a diversity panel of 188 cowpea (Vigna unguiculata) genotypes to identify which traits are sufficient to differentiate genotypes even when comparing plants whose harvesting date differs up to 14 d. Overall, we find that automatically derived traits can increase both the speed and reproducibility of the trait estimation pipeline under field conditions.

AB - Current plant phenotyping technologies to characterize agriculturally relevant traits have been primarily developed for use in laboratory and/or greenhouse conditions. In the case of root architectural traits, this limits phenotyping efforts, largely, to young plants grown in specialized containers and growth media. Hence, novel approaches are required to characterize mature root systems of older plants grown under actual soil conditions in the field. Imaging methods able to address the challenges associated with characterizing mature root systems are rare due, in part, to the greater complexity of mature root systems, including the larger size, overlap, and diversity of root components. Our imaging solution combines a field-imaging protocol and algorithmic approach to analyze mature root systems grown in the field. Via two case studies, we demonstrate how image analysis can be utilized to estimate localized root traits that reliably capture heritable architectural diversity as well as environmentally induced architectural variation of both monocot and dicot plants. In the first study, we show that our algorithms and traits (including 13 novel traits inaccessible to manual estimation) can differentiate nine maize (Zea mays) genotypes 8 weeks after planting. The second study focuses on a diversity panel of 188 cowpea (Vigna unguiculata) genotypes to identify which traits are sufficient to differentiate genotypes even when comparing plants whose harvesting date differs up to 14 d. Overall, we find that automatically derived traits can increase both the speed and reproducibility of the trait estimation pipeline under field conditions.

UR - http://www.scopus.com/inward/record.url?scp=84907729433&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84907729433&partnerID=8YFLogxK

U2 - 10.1104/pp.114.243519

DO - 10.1104/pp.114.243519

M3 - Article

C2 - 25187526

AN - SCOPUS:84907729433

VL - 166

SP - 470

EP - 486

JO - Plant Physiology

JF - Plant Physiology

SN - 0032-0889

IS - 2

ER -

Bucksch A, Burridge J, York LM, Das A, Nord E, Weitz JS et al. Image-based high-throughput field phenotyping of crop roots. Plant physiology. 2014 Oct 1;166(2):470-486. https://doi.org/10.1104/pp.114.243519