Maize Kernel-Ear-Cob Analysis


Phytomorph lab @ University of Wisconsin

Overview

Measured variables length, width, shape, count
Operating system mac, linux, windows
Licence open-source, freeware
Automation level automated
Plant requirements maize
Export formats csv
Other information Run on Cyverse (formerly iPlant)

Scientific article(s)

A robust, high-throughput method for computing maize ear, cob, and kernel attributes automatically from images
Nathan D. Miller, Nicholas J. Haase, Jonghyun Lee, Shawn M. Kaeppler, Natalia de Leon, Edgar P. Spalding
Plant J, 2017 View paper

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Description

Grain yield of the maize plant depends on the sizes, shapes, and numbers of ears and the kernels they bear. An automated pipeline that can measure these components of yield from easily-obtained digital images is needed to advance our understanding of this globally important crop. Here we present three custom algorithms designed to compute such yield components automatically from digital images acquired by a low-cost platform. One algorithm determines the average space each kernel occupies along the cob axis using a sliding-window Fourier transform analysis of image intensity features. A second counts individual kernels removed from ears, including those in clusters. A third measures each kernel’s major and minor axis after a Bayesian analysis of contour points identifies the kernel tip. Dimensionless ear and kernel shape traits that may interrelate yield components are measured by principal components analysis of contour point sets. Increased objectivity and speed compared to typical manual methods are achieved without loss of accuracy as evidenced by high correlations with ground truth measurements and simulated data.


Source: The Plant Journal

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