Classification of Broadleaf and Grass Weeds Using Gabor Wavelets and an Artificial Neural Network

L. Tang, L. Tian, B. L. Steward

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)1247-1254
Number of pages8
JournalTransactions of the American Society of Agricultural Engineers
Issue number4
StatePublished - Jul 2003


  • Broadleaf
  • Gabor wavelets
  • Grass
  • Neural network
  • Real-time
  • Selective weed control
  • Texture

ASJC Scopus subject areas

  • Agricultural and Biological Sciences (miscellaneous)


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