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Full Length
Research Paper
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Weed/corn
seedling recognition by support vector machine using texture
features
Lanlan Wu*
and Youxian Wen
College of Engineering and Technology, Huazhong Agricultural
University, Wuhan, 430070, P. R. China.
*Corresponding author. E-mail:
skykk621@yahoo.com.cn.
Tel: +86 027 87281002.
Fax: +86 027 87282121.
Accepted 3 August, 2009 |
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Abstract |
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This study investigated the effect of a new approach, the
support vector machine, as a classifier tool to identify the
weeds in corn fields at early growth stage. Image segmentation
was done by transforming original color images to gray level
images according to the statistical values of red, green, blue
components. The Gray Level Co-occurrence Matrix (GLCM) and
statistical properties of the histogram from the gray level
images were further used to obtain the texture features of the
weeds and corn seedlings. These texture features were used in
the classification procedure. Principle component analysis was
used to select the texture features according to their better
contributions to reduce space dimensions. A Support Vector
Machine (SVM) classifier was employed to recognize the weeds and
the corn seedlings. The results indicated that the SVM
classifiers with different feature selections could identify
successfully weed-corn with a higher accuracy ranged from 92.31
to 100%. A comparison study of the recognition capabilities of
SVM and back-propagation (BP) neural-network classifier using
the same data set was conducted. It was found that the SVM
classifier provided the best recognition performance with an
accuracy of 100%, which exceeded the accuracy of 80% given by
the BP classifier.
Key words:
Corn seedling, weeds, texture, support vector machine,
recognize. |
