Automatic kernel counting on maize ear using RGB images

Plant Methods
Di WuHuawei Qin

Abstract

The number of kernels per ear is one of the major agronomic yield indicators for maize. Manual assessment of kernel traits can be time consuming and laborious. Moreover, manually acquired data can be influenced by subjective bias of the observer. Existing methods for counting of kernel number are often unstable and costly. Machine vision technology allows objective extraction of features from image sensor data, offering high-throughput and low-cost advantages. Here, we propose an automatic kernel recognition method which has been applied to count the kernel number based on digital colour photos of the maize ears. Images were acquired under both LED diffuse (indoors) and natural light (outdoor) conditions. Field trials were carried out at two sites in China using 8 maize varieties. This method comprises five steps: (1) a Gaussian Pyramid for image compression to improve the processing efficiency, (2) separating the maize fruit from the background by Mean Shift Filtering algorithm, (3) a Colour Deconvolution (CD) algorithm to enhance the kernel edges, (4) segmentation of kernel zones using a local adaptive threshold, (5) an improved Find-Local-Maxima to recognize the local grayscale peaks and determine the maize kernel number wit...Continue Reading

References

Aug 29, 2012·Journal of Integrative Plant Biology·Jill E CairnsJose Luis Araus
Sep 3, 2016·The Plant Journal : for Cell and Molecular Biology·Nathan D MillerEdgar P Spalding
Mar 11, 2017·Journal of Imaging·Thomas Atta-FosuRui Sousa-Neves
May 16, 2018·Plant Methods·Sruti Das ChoudhuryTala Awada

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Citations

Jun 11, 2021·Frontiers in Robotics and AI·Jennifer HobbsDavid Wilson

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Software Mentioned

ImageJ
Fiji
methylgreen
mathop
Visual Studio

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