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CLASSIFICATION OF WEED SPECIES USING ARTIFICIAL NEURAL NETWORKS BASED ON COLOR LEAF TEXTURE FEATURE

  • Zhichen Li
  • Qiu An
  • Changying Ji
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 294)

Abstract

The potential impact of herbicide utilization compel people to use new method of weed control. Selective herbicide application is optimal method to reduce herbicide usage while maintain weed control. The key of selective herbicide is how to discriminate weed exactly. The HIS color co-occurrence method (CCM) texture analysis techniques was used to extract four texture parameters: Angular second moment (ASM), Entropy(E), Inertia quadrature (IQ), and Inverse difference moment or local homogeneity (IDM).The weed species selected for studying were Arthraxon hispidus, Digitaria sanguinalis, Petunia, Cyperus, Alternanthera Philoxeroides and Corchoropsis psilocarpa. The software of neuroshell2 was used for designing the structure of the neural network, training and test the data. It was found that the 8-40-1 artificial neural network provided the best classification performance and was capable of classification accuracies of 78%.

Keywords

Artificial Neural Network Texture Feature Weed Species Good Classification Performance Hide Layer Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Zhichen Li
    • 1
  • Qiu An
    • 1
  • Changying Ji
    • 1
  1. 1.Nanjing Agricultural UniversityNanjingChina

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