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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
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High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging.

R Makanza1, M Zaman-Allah1, J E Cairns1

  • 1International Maize and Wheat Improvement Center (CIMMYT), PO Box MP163, Harare, Zimbabwe.

Plant Methods
|June 28, 2018
PubMed
Summary
This summary is machine-generated.

A new, low-cost digital imaging technique estimates maize ear and kernel traits, reducing costs for crop improvement. This method offers a precise and efficient alternative to traditional laborious measurements.

Keywords:
EarImage analysisKernelMaizePhenotyping

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Area of Science:

  • Agricultural Science
  • Plant Breeding
  • Image Analysis

Background:

  • Maize (Zea mays) grain yield, ear, and kernel attributes are crucial for understanding plant performance and variety development.
  • Traditional measurement of these traits is labor-intensive and costly, hindering efficient breeding programs.

Purpose of the Study:

  • To develop a low-cost digital imaging method for estimating maize ear and kernel attributes.
  • To provide a more accessible tool for crop improvement programs, especially those with limited resources.

Main Methods:

  • Utilized a digital imaging technique with ImageJ open-source software for batch processing of ear images.
  • Estimated kernel weight using total kernel number (derived from visible kernels) and average kernel size.

Main Results:

  • The developed method accurately estimates ear number, size, kernel number, size, and weight from digital images.
  • Image-derived data showed good agreement with ground truth measurements in accuracy and precision.
  • Estimated parameters exhibited broad-sense heritability comparable to or higher than measured grain weight.

Conclusions:

  • The digital imaging method significantly reduces selection costs in maize breeding.
  • This approach facilitates a deeper understanding of the genetic basis of grain yield determinants.
  • It offers a valuable tool for resource-constrained crop improvement initiatives.