Maize root complexity analysis using a Support Vector Machine method

Authors:
D. Zhong;J. Novais;T. E. Grift;M. Bohn;J. Han
Affiliations:
Institute of Control & Automation, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China;Department of Crop Sciences, University of Illinois, Urbana, IL 61801, USA;Department of Agricultural & Biological Engineering, University of Illinois, 1304 West Pennsylvania Avenue, Urbana, IL 61801, USA;Department of Crop Sciences, University of Illinois, Urbana, IL 61801, USA;Institute of Control & Automation, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
Venue:
Computers and Electronics in Agriculture
Year:
2009

Citing 7
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Multicategory Classification by Support Vector Machines

Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
Least Squares Support Vector Machine Classifiers

Neural Processing Letters
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
Training Support Vector Machines: an Application to Face Detection

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Support vector machine classification on the web

Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Root complexity is an important factor in the growth and survivability of maize plants under biotic and abiotic stress conditions. To genetically improve root structure in the future, there is a need to identify the genes that govern root complexity. Root complexity itself is ill defined, but indicators derived from images of the root system such as Fractal Dimension can be used as proxies. A disadvantage of using Fractal Dimension as a complexity indicator is that the complexity of the root as seen in the images is captured into a single parameter. This paper describes an alternative method, which translates a root image into a set of parameters. The method consists of computing the intercepts of circles drawn around the centre of the root image with the root branches. This led to characteristic curves from which parameters can be extracted using curve fitting. In addition to the parameters obtained by curve fitting, the density of the root images was included. All parameters were evaluated on their ability to classify the roots among their original genotypes using a method from the realm of Artificial Intelligence, the Support Vector Machine (SVM). The results showed that whilst using merely three parameters originating from the characteristic curves, the SVM algorithm was capable of correctly classifying 99.95% of roots among 235 original genotypes.