Full TGIF Record # 162863
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Web URL(s):http://elibrary.asabe.org/azdez.asp?JID=3&AID=13944&CID=t2003&v=46&i=4&T=1&redirType=
    Last checked: 07/09/2013
http://elibrary.asabe.org/azdez.asp?JID=3&AID=13944&ConfID=t2003&v=46&i=4&T=2&redirType=
    Last checked: 07/09/2013
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Publication Type:
i
Report
Author(s):Tang, L.; Tian, L.; Steward, B. L.
Author Affiliation:Tang: Assistant Professor, Agro Technology Group, Department of Agricultural Sciences, The Royal Veterinary and Agriculture University, Taastrup, Denmark; Tian: ASAE Member Engineer and Associate Professor, Department of Agricultural Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois; Steward: ASAE Member Engineer and Assistant Professor, Agricultural and Biosystems Engineering Department, Iowa State University, Ames, Iowa
Title:Classification of broadleaf and grass weeds using gabor wavelets and an artificial neural network
Section:Information and electrical technologies
Other records with the "Information and electrical technologies" Section
Source:Transactions of the ASAE. Vol. 46, No. 4, July/August 2003, p. 1247-1254.
# of Pages:8
Publishing Information:St. Joseph, MI: American Society of Agricultural and Biological Engineers
Related Web URL:http://elibrary.asabe.org/abstract.asp?aid=13944&t=3&dabs=Y&redir=&redirType=
    Last checked: 07/09/2013
    Notes: Abstract only
Keywords:TIC Keywords: Broadleaf weeds; Xanthium strumarium; Convolvulus; Digitaria; Image analysis; Selective weed control; Setaria faberii; Abutilon theophrasti; Weed identification
Abstract/Contents:"A texture-based weed classification method was developed. The method consisted of a low-level Gabor wavelets-based feature extraction algorithm and a high-level neural network-based pattern recognition algorithm. This classification method was specifically developed to explore the feasibility of classifying weed images into broadleaf and grass categories for spatially selective weed control. In this research, three species of broadleaf weeds (common cocklebur, velvetleaf, and ivyleaf morning glory) and two grasses (giant foxtail and crabgrass) that are common in Illinois were studied. After processing 40 sample images with 20 samples from each class, the results showed that the method was capable of classifying all the samples correctly with high computational efficiency, demonstrating its potential for practical implementation under real-time constraints."
Language:English
References:26
Note:Pictures, b/w
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ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Tang, L., L. Tian, and B. L. Steward. 2003. Classification of broadleaf and grass weeds using gabor wavelets and an artificial neural network. Trans. ASABE. 46(4):p. 1247-1254.
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Web URL(s):
http://elibrary.asabe.org/azdez.asp?JID=3&AID=13944&CID=t2003&v=46&i=4&T=1&redirType=
    Last checked: 07/09/2013
http://elibrary.asabe.org/azdez.asp?JID=3&AID=13944&ConfID=t2003&v=46&i=4&T=2&redirType=
    Last checked: 07/09/2013
    Requires: PDF Reader
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MSU catalog number: b5149809~S1a
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