By Topic

The application of support vector machine in veed classification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Weixing Zhu ; Modern Agric. Equip. & Technol. Key Lab. of Jiangsu Province, Jiangsu Univ., Zhenjiang, China ; Xiaofang Zhu

As accurate weed identification is the for precise herbicides spraying, this presents a weed recognition method based on support vector machine (SVM). At first, five kinds of weeds are segmented from the background images and their shape and texture parameters are extracted. Then, according to the distribution of the feature data, the most effective combination of feature data are selected and inputted into SVM classifier for classification training. As SVM has advantages of high-dimensional and nonlinear processing capabilities, in this paper, the effective character parameters are selected by analyzing the distribution of feature data, which reduced the complexity of the algorithm. At the same time, the reliability of classification are ensured by the cross-validation classification and training. The experimental results show that the accuracy of weed recognition in proposed method is 93.3% and the classification time is 1.18s. This is an effective classification method and will find wide application in order aspects.

Published in:

Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on  (Volume:4 )

Date of Conference:

20-22 Nov. 2009