Emerging Technologies

Planta

, Volume 236, Issue 6, pp 1943-1954

Extraction of quantitative characteristics describing wheat leaf pubescence with a novel image-processing technique

  • Mikhail A. GenaevAffiliated withLaboratory of Evolutionary Bioinformatics and Theoretical Genetics, Department of Systems Biology, Institute of Cytology and Genetics SB RAS
  • , Alexey V. DoroshkovAffiliated withLaboratory of Evolutionary Bioinformatics and Theoretical Genetics, Department of Systems Biology, Institute of Cytology and Genetics SB RAS
  • , Tatyana A. PshenichnikovaAffiliated withDepartment of the Genetic Resources of Experimental Plants, Sector of Genetics of Grain Quality, Institute of Cytology and Genetics SB RAS
  • , Nikolay A. KolchanovAffiliated withLaboratory of Evolutionary Bioinformatics and Theoretical Genetics, Department of Systems Biology, Institute of Cytology and Genetics SB RASChair of Informational Biology, Novosibirsk State University
  • , Dmitry A. AfonnikovAffiliated withLaboratory of Evolutionary Bioinformatics and Theoretical Genetics, Department of Systems Biology, Institute of Cytology and Genetics SB RASChair of Informational Biology, Novosibirsk State University Email author 

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Abstract

Leaf pubescence (hairiness) in wheat plays an important biological role in adaptation to the environment. However, this trait has always been methodologically difficult to phenotype. An important step forward has been taken with the use of computer technologies. Computer analysis of a photomicrograph of a transverse fold line of a leaf is proposed for quantitative evaluation of wheat leaf pubescence. The image-processing algorithm is implemented in the LHDetect2 software program accessible as a Web service at http://​wheatdb.​org/​lhdetect2. The results demonstrate that the proposed method is rapid, adequately assesses leaf pubescence density and the length distribution of trichomes and the data obtained using this method are significantly correlated with the density of trichomes on the leaf surface. Thus, the proposed method is efficient for high-throughput analysis of leaf pubescence morphology in cereal genetic collections and mapping populations.

Keywords

Common wheat Computer image analysis High-throughput phenotyping Leaf pubescence Trichomes