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Related Experiment Video

Updated: Jan 29, 2026

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
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Evaluating maize phenotype dynamics under drought stress using terrestrial lidar.

Yanjun Su1,2, Fangfang Wu1,2, Zurui Ao3

  • 11State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093 China.

Plant Methods
|February 12, 2019
PubMed
Summary

Terrestrial lidar effectively monitors maize (Zea mays L.) plant 3D phenotypes under drought. This technology aids in identifying key drought-influenced traits and growth stages for improved crop resilience.

Keywords:
Drought stressLidarMaizePhenotype

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Area of Science:

  • Agricultural Science
  • Plant Science
  • Remote Sensing Technology

Background:

  • Maize (Zea mays L.) is a vital global food crop, crucial for food security, especially facing climate change and drought.
  • Current phenotyping methods limit studies to specific growth stages, hindering comprehensive analysis of plant responses.
  • Light detection and ranging (lidar) offers potential for 3D vegetation data but is underutilized for individual maize plant phenotyping dynamics.

Purpose of the Study:

  • To assess the feasibility of using terrestrial lidar for monitoring 3D maize phenotypes at the individual plant level under drought stress.
  • To analyze drought-related phenotypic changes in maize during critical growth stages using lidar data.
  • To identify key phenotypes and growth stages affected by drought stress for potential crop improvement.

Main Methods:

  • Terrestrial laser scanner utilized to collect lidar data across six growth stages for 20 maize varieties under induced drought stress.
  • Calculation of three key phenotypes—plant height, plant area index (PAI), and projected leaf area (PLA)—from lidar point clouds at the individual plant level.
  • Comparative analysis of phenotypic dynamics between high and low drought-tolerant maize groups.

Main Results:

  • Terrestrial lidar accurately estimated plant height (96%), PAI (70%), and PLA (92%) in maize.
  • All three measured phenotypes exhibited a dynamic pattern of increase followed by decrease throughout the growth period.
  • High drought tolerance in maize was associated with maintained plant area index (PAI) and projected leaf area (PLA) with reduced plant height during tasseling, and lower upper canopy plant area density.

Conclusions:

  • Terrestrial lidar is a feasible technology for field-based 3D monitoring of maize phenotypes under drought stress.
  • Lidar-derived phenotypic data provides novel insights into drought impact on maize growth dynamics.
  • This approach can aid in identifying crucial phenotypes and growth stages for breeding drought-resilient maize varieties.