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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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Automatic kernel counting on maize ear using RGB images.

Di Wu1, Zhen Cai2, Jiwan Han3

  • 1Institute of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang People's Republic of China.

Plant Methods
|June 11, 2020
PubMed
Summary
This summary is machine-generated.

An automated method accurately counts maize kernels from digital images, overcoming limitations of manual methods. This high-throughput, low-cost approach aids in agricultural breeding programs.

Keywords:
Adaptive thresholdComputer visionCountingKernel recognitionLocal Maxima

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Image Processing

Background:

  • Maize kernel number is a key indicator of crop yield.
  • Manual kernel counting is labor-intensive, time-consuming, and prone to bias.
  • Existing automated methods are often unstable and expensive.

Purpose of the Study:

  • To develop an efficient and accurate automated method for counting maize kernels using digital images.
  • To address the limitations of manual assessment and costly existing techniques.

Main Methods:

  • Image acquisition under diverse lighting conditions (LED diffuse and natural light).
  • A five-step algorithm including Gaussian Pyramid, Mean Shift Filtering, Colour Deconvolution, local adaptive thresholding, and Find-Local-Maxima.
  • Application to digital color photos of maize ears from field trials.

Main Results:

  • The automated method achieved over 93% accuracy and precision compared to manual counting.
  • The algorithm demonstrated robust performance across different lighting conditions.
  • Successful application in field trials involving 8 maize varieties.

Conclusions:

  • The proposed algorithm offers a robust, efficient, and low-cost solution for maize ear kernel counting.
  • This method is suitable for practical application in real-world plant breeding programs.
  • The technology provides objective and high-throughput data for agronomic research.