Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Predicting plant biomass accumulation from image-derived parameters

View ORCID ProfileDijun Chen, Rongli Shi, Jean-Michel Pape, Christian Klukas
doi: https://doi.org/10.1101/046656
Dijun Chen
1Present address: Institute for Biochemistry and Biology, Potsdam University, 14476 Potsdam, Germany.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Dijun Chen
  • For correspondence: chendijun2012@gmail.com christian.klukas@gmail.com
Rongli Shi
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jean-Michel Pape
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Christian Klukas
2LemnaTec GmbH, 52076 Aachen, Germany.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: chendijun2012@gmail.com christian.klukas@gmail.com
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Image-based high-throughput phenotyping technologies have been rapidly developed in plant science recently and they provide a great potential to gain more valuable information than traditionally destructive methods. Predicting plant biomass is regarded as a key purpose for plant breeders and ecologist. However, it is a great challenge to find a suitable model to predict plant biomass in the context of high-throughput phenotyping. In the present study, we constructed several models to examine the quantitative relationship between image-based features and plant biomass accumulation. Our methodology has been applied to three consecutive barley experiments with control and stress treatments. The results proved that plant biomass can be accurately predicted from image-based parameters using a random forest model. The high prediction accuracy based on this model, in particular the cross-experiment performance, is promising to relieve the phenotyping bottleneck in biomass measurement in breeding applications. The relative contribution of individual features for predicting biomass was further quantified, revealing new insights into the phenotypic determinants of plant biomass outcome. What’s more, the methods could also be used to determine the most important image-based features related to plant biomass accumulation, which would be promising for subsequent genetic mapping to uncover the genetic basis of biomass.

One-sentence Summary We demonstrated that plant biomass can be accurately predicted from image-based parameters in the context of high-throughput phenotyping.

Footnotes This work was supported by the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), the Robert Bosch Stiftung (32.5.8003.0116.0) and the Federal Agency for Agriculture and Food (BEL, 15/12-13, 530-06.01-BiKo CHN) and the Federal Ministry of Education and Research (BMBF, 0315958A and 031A053B). This research was furthermore enabled with support of the European Plant Phenotyping Network (EPPN, grant agreement no. 284443) funded by the FP7 Research Infrastructures Programme of the European Union.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted March 31, 2016.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Predicting plant biomass accumulation from image-derived parameters
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Predicting plant biomass accumulation from image-derived parameters
Dijun Chen, Rongli Shi, Jean-Michel Pape, Christian Klukas
bioRxiv 046656; doi: https://doi.org/10.1101/046656
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Predicting plant biomass accumulation from image-derived parameters
Dijun Chen, Rongli Shi, Jean-Michel Pape, Christian Klukas
bioRxiv 046656; doi: https://doi.org/10.1101/046656

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Plant Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (3689)
  • Biochemistry (7789)
  • Bioengineering (5674)
  • Bioinformatics (21282)
  • Biophysics (10576)
  • Cancer Biology (8173)
  • Cell Biology (11937)
  • Clinical Trials (138)
  • Developmental Biology (6762)
  • Ecology (10401)
  • Epidemiology (2065)
  • Evolutionary Biology (13863)
  • Genetics (9708)
  • Genomics (13070)
  • Immunology (8139)
  • Microbiology (19983)
  • Molecular Biology (7842)
  • Neuroscience (43053)
  • Paleontology (319)
  • Pathology (1279)
  • Pharmacology and Toxicology (2258)
  • Physiology (3351)
  • Plant Biology (7232)
  • Scientific Communication and Education (1312)
  • Synthetic Biology (2004)
  • Systems Biology (5537)
  • Zoology (1128)