Skip to main content
Classification of Broadleaf and Grass Weeds Using Gabor Wavelets and an Artificial Neural Network
Transactions of the ASAE
  • Lie Tang, The Royal Veterinary and Agricultural University
  • Lei F. Tian, University of Illinois at Urbana-Champaign
  • Brian L. Steward, Iowa State University
Document Type
Publication Version
Published Version
Publication Date
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.

This article is from Transactions of the ASAE, 46, no. 4 (2003): 1247–1254.

Copyright Owner
American Society of Agricultural Engineers
File Format
Citation Information
Lie Tang, Lei F. Tian and Brian L. Steward. "Classification of Broadleaf and Grass Weeds Using Gabor Wavelets and an Artificial Neural Network" Transactions of the ASAE Vol. 46 Iss. 4 (2003) p. 1247 - 1254
Available at: