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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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Related Experiment Video

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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
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A robust, high-throughput method for computing maize ear, cob, and kernel attributes automatically from images.

Nathan D Miller1, Nicholas J Haase2, Jonghyun Lee1

  • 1Department of Botany, University of Wisconsin-Madison, 430 Lincoln Drive, Madison, WI, 53706, USA.

The Plant Journal : for Cell and Molecular Biology
|September 3, 2016
PubMed
Summary
This summary is machine-generated.

Researchers developed an automated system using custom algorithms to measure maize yield components from digital images. This technology enhances objectivity and speed in analyzing ear and kernel traits for improved crop understanding.

Keywords:
Zea maysFourier transformear sizehigh-throughput phenotypingimage analysiskernel countingkernel shapekernel spacingtechnical advance

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Area of Science:

  • Agricultural Science
  • Plant Biology
  • Computational Biology

Background:

  • Maize (Zea mays) grain yield is determined by ear and kernel size, shape, and number.
  • Accurate measurement of these yield components is crucial for advancing maize research.
  • Manual methods for trait measurement are time-consuming and subjective.

Purpose of the Study:

  • To develop and validate an automated system for quantifying maize yield components from digital images.
  • To provide an objective and efficient alternative to manual measurement techniques.
  • To facilitate large-scale analysis of maize genetic diversity and breeding.

Main Methods:

  • Development of three custom algorithms for automated image analysis.
  • Sliding-window Fourier transform for kernel spacing along the cob.
  • Bayesian analysis and principal components analysis for kernel and ear shape traits.

Main Results:

  • High correlation with ground truth and simulated data, demonstrating accuracy.
  • Accurate measurement of millimeter-scale differences in ear, cob, and kernel traits.
  • Successful application across diverse inbred maize lines with significant trait variation.

Conclusions:

  • The automated system provides objective and rapid measurement of maize yield components.
  • The developed algorithms and platform offer a valuable tool for maize breeding and genetics research.
  • The system is available as a web service with downloadable source code for wider accessibility.