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2012 (Vol. 4, Issue: 18)
Article Information:

Performance Evaluation of Discriminant Analysis and Decision Tree, for Weed Classification of Potato Fields

Farshad Vesali, Masoud Gharibkhani and Mohmmad Hasan Komarizadeh
Corresponding Author:  Farshad Vesali 

Key words:  Color components, potato plant, three composition of classification, weed, , ,
Vol. 4 , (18): 3215-3221
Submitted Accepted Published
December 23, 2011 February 22, 2012 September 15, 2012
Abstract:

In present study we tried to recognizing weeds in potato fields to effective use from herbicides. As we know potato is one of the crops which is cultivated vastly all over the world and it is a major world food crop that is consumed by over one billion people world over, but it is threated by weed invade, because of row cropping system applied in potato tillage. Machine vision is used in this research for effective application of herbicides in field. About 300 color images from 3 potato farms of Qorveh city and 2 farms of Urmia University-Iran, was acquired. Images were acquired in different illumination condition from morning to evening in sunny and cloudy days. Because of overlap and shading of plants in farm condition it is hard to use morphologic parameters. In method used for classifying weeds and potato plants, primary color components of each plant were extracted and the relation between them was estimated for determining discriminant function and classifying plants using discrimination analysis. In addition the decision tree method was used to compare results with discriminant analysis. Three different classifications were applied: first, Classification was applied to discriminate potato plant from all other weeds (two groups), the rate of correct classification was 76.67% for discriminant analysis and 83.82% for decision tree; second classification was applied to discriminate potato plant from separate groups of each weed (6 groups), the rate of correct classification was 87%. And the third, Classification of potato plant versus weed species one by one. As the weeds were different, the results of classification were different in this composition. The decision tree in all conditions showed the better result than discriminant analysis.
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  Cite this Reference:
Farshad Vesali, Masoud Gharibkhani and Mohmmad Hasan Komarizadeh, 2012. Performance Evaluation of Discriminant Analysis and Decision Tree, for Weed Classification of Potato Fields.  Research Journal of Applied Sciences, Engineering and Technology, 4(18): 3215-3221.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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