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Related Experiment Video

Updated: Aug 7, 2025

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

Published on: June 16, 2018

Corn360: a method for quantification of corn kernels.

Samantha Gillette¹, Lu Yin¹, Penny M A Kianian²

¹School of Life Sciences, Arizona State University, Tempe, AZ, 85287, USA.

|March 9, 2023

Summary

This summary is machine-generated.

A new Corn360 system offers a portable, affordable way to phenotype corn kernels for yield estimation and genetic studies. This high-throughput method uses AI-powered software, reducing manual counting time and enabling detailed kernel trait analysis.

Keywords:

Corn High-throughput phenotyping Image analysis Kernel color Kernel texture Low-cost

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Last Updated: Aug 7, 2025

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Published on: June 16, 2018

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Quantifying Plant Soluble Protein and Digestible Carbohydrate Content, Using Corn Zea mays As an Exemplar

Quantifying Plant Soluble Protein and Digestible Carbohydrate Content, Using Corn Zea mays As an Exemplar

Published on: August 6, 2018

Area of Science:

Agricultural Science
Plant Breeding
Genetics

Background:

High-throughput phenotyping of corn kernel traits is crucial for yield estimation and genetic studies.
Existing methods often require complex setups, statistical expertise, and programming skills.

Purpose of the Study:

To develop a portable, accessible, and affordable system for high-throughput corn kernel phenotyping.
To enable accurate quantification of total and patterned corn kernel counts.
To facilitate the study of genetic inheritance of kernel traits.

Main Methods:

Developed Corn360, a panoramic imaging system for capturing corn ear images.
Utilized freely available software with Artificial Intelligence for image analysis and kernel segmentation.
Applied the system to homogeneously and mixed-patterned corn ears, including a sweet x sticky corn cross.

Main Results:

Corn360 achieved 93.7% accuracy for total kernel count on homogeneous ears, saving an average of 3 min 40 s per image.
Segmented kernel counts for mixed-patterned ears showed accuracies of 84.8% and 61.8%.
Demonstrated segregation of starch:sweet:sticky kernels in a 9:4:3 ratio in an F2 population, indicating control by two genes with epistatic effects.

Conclusions:

The Corn360 system provides a portable, low-cost, high-throughput solution for corn kernel quantification.
Enables rapid yield component estimation and categorization of patterned kernels for genetic studies.
Corn360 is effective, accessible, and cost-efficient for corn kernel analysis, regardless of programming skills.