Drone phenotyping and machine learning enable discovery of loci regulating daily floral opening in lettuce

Rongkui Han, Andy J.Y. Wong, Zhehan Tang, Maria J. Truco, Dean O. Lavelle, Alexander Kozik, Yufang Jin, Richard W. Michelmore

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Flower opening and closure are traits of reproductive importance in all angiosperms because they determine the success of self-and cross-pollination. The temporal nature of this phenotype rendered it a difficult target for genetic studies. Cultivated and wild lettuce, Lactuca spp., have composite inflorescences that open only once. An L. serriola×L. sativa F6 recombinant inbred line (RIL) population differed markedly for daily floral opening time. This population was used to map the genetic determinants of this trait; the floral opening time of 236 RILs was scored using time-course image series obtained by drone-based phenotyping on two occasions. Floral pixels were identified from the images using a support vector machine with an accuracy >99%. A Bayesian inference method was developed to extract the peak floral opening time for individual genotypes from the time-stamped image data. Two independent quantitative trait loci (QTLs; Daily Floral Opening 2.1 and qDFO8.1) explaining >30% of the phenotypic variation in floral opening time were discovered. Candidate genes with non-synonymous polymorphisms in coding sequences were identified within the QTLs. This study demonstrates the power of combining remote sensing, machine learning, Bayesian statistics, and genome-wide marker data for studying the genetics of recalcitrant phenotypes.

Original languageEnglish (US)
Pages (from-to)2979-2994
Number of pages16
JournalJournal of Experimental Botany
Volume72
Issue number8
DOIs
StatePublished - Apr 2 2021

Keywords

  • Bayesian inference
  • flower opening
  • high-throughput phenotyping
  • image analysis
  • lettuce
  • machine learning
  • QTL mapping
  • remote sensing phenotyping
  • support vector machine (SVM)
  • unmanned aerial system (UAS)

ASJC Scopus subject areas

  • Physiology
  • Plant Science

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