Leveraging image analysis for high-throughput plant phenotyping

Sruti Das Choudhury, Ashok Samal, Tala Awada

Research output: Contribution to journalShort survey

7 Scopus citations

Abstract

The complex interaction between a genotype and its environment controls the biophysical properties of a plant, manifested in observable traits, i.e., plant's phenome, which influences resources acquisition, performance, and yield. High-throughput automated image-based plant phenotyping refers to the sensing and quantifying plant traits non-destructively by analyzing images captured at regular intervals and with precision. While phenomic research has drawn significant attention in the last decade, extracting meaningful and reliable numerical phenotypes from plant images especially by considering its individual components, e.g., leaves, stem, fruit, and flower, remains a critical bottleneck to the translation of advances of phenotyping technology into genetic insights due to various challenges including lighting variations, plant rotations, and self-occlusions. The paper provides (1) a framework for plant phenotyping in a multimodal, multi-view, time-lapsed, high-throughput imaging system; (2) a taxonomy of phenotypes that may be derived by image analysis for better understanding of morphological structure and functional processes in plants; (3) a brief discussion on publicly available datasets to encourage algorithm development and uniform comparison with the state-of-the-art methods; (4) an overview of the state-of-the-art image-based high-throughput plant phenotyping methods; and (5) open problems for the advancement of this research field.

Original languageEnglish (US)
Article number508
JournalFrontiers in Plant Science
Volume10
DOIs
StatePublished - Apr 16 2019

Keywords

  • High-throughput plant phenotyping
  • Image analysis
  • Multimodal image sequence
  • Phenotype taxonomy
  • Physiological phenotype
  • Structural phenotype
  • Temporal phenotype

ASJC Scopus subject areas

  • Plant Science

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