Closing the gap between phenotyping and genotyping: review of advanced, image-based phenotyping technologies in forestry

Liming Bian, Huichun Zhang, Yufeng Ge, Jaroslav Čepl, Jan Stejskal, Yousry A. EL-Kassaby

Research output: Contribution to journalReview articlepeer-review

3 Scopus citations

Abstract

Key message: The lack of efficient phenotyping capacities has been recognized as a bottleneck in forestry phenotyping and breeding. Modern phenotyping technologies use systems equipped with various imaging sensors to automatically collect high volume phenotypic data that can be used to assess trees' various attributes. Context: Efficient phenotyping has the potential to spark a new Green Revolution, and it would provide an opportunity to acquire growth parameters and dissect the genetic bases of quantitative traits. Phenotyping platforms aim to link information from several sources to derive knowledge about trees' attributes. Aims: Various tree phenotyping techniques were reviewed and analyzed along with their different applications. Methods: This article presents the definition and characteristics of forest tree phenotyping and reviews newly developed imaging-based practices in forest tree phenotyping. Results: This review addressed a wide range of forest trees phenotyping applications, including a survey of actual inter- and intra-specific variability, evaluating genotypes and species response to biotic and abiotic stresses, and phenological measurements. Conclusion: With the support of advanced phenotyping platforms, the efficiency of traits phenotyping in forest tree breeding programs is accelerated.

Original languageEnglish (US)
Article number22
JournalAnnals of Forest Science
Volume79
Issue number1
DOIs
StatePublished - Dec 2022

Keywords

  • Deep learning
  • Forest tree
  • Phenological measurement
  • Phenotyping
  • Platforms
  • Sensor
  • Stress tolerance

ASJC Scopus subject areas

  • Forestry
  • Ecology

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