High-throughput phenotyping of plant leaf morphological, physiological, and biochemical traits on multiple scales using optical sensing

Huichun Zhang, Lu Wang, Xiuliang Jin, Liming Bian, Yufeng Ge

Research output: Contribution to journalReview articlepeer-review

9 Scopus citations

Abstract

Acquisition of plant phenotypic information facilitates plant breeding, sheds light on gene action, and can be applied to optimize the quality of agricultural and forestry products. Because leaves often show the fastest responses to external environmental stimuli, leaf phenotypic traits are indicators of plant growth, health, and stress levels. Combination of new imaging sensors, image processing, and data analytics permits measurement over the full life span of plants at high temporal resolution and at several organizational levels from organs to individual plants to field populations of plants. We review the optical sensors and associated data analytics used for measuring morphological, physiological, and biochemical traits of plant leaves on multiple scales. We summarize the characteristics, advantages and limitations of optical sensing and data-processing methods applied in various plant phenotyping scenarios. Finally, we discuss the future prospects of plant leaf phenotyping research. This review aims to help researchers choose appropriate optical sensors and data processing methods to acquire plant leaf phenotypes rapidly, accurately, and cost-effectively.

Original languageEnglish (US)
Pages (from-to)1303-1318
Number of pages16
JournalCrop Journal
Volume11
Issue number5
DOIs
StatePublished - Oct 2023

Keywords

  • Artificial intelligence
  • Image processing
  • Leaf traits
  • Machine learning
  • Optical sensing

ASJC Scopus subject areas

  • Agronomy and Crop Science
  • Plant Science

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