Crop and weed species recognition based on hyperspectral sensing and active learning

Conference paper

DOI: 10.3920/978-90-8686-778-3_68

Cite this paper as:
Moshou D., Kateris D., Pantazi XE., Gravalos I. (2013) Crop and weed species recognition based on hyperspectral sensing and active learning. In: Stafford J.V. (eds) Precision agriculture ’13. Wageningen Academic Publishers, Wageningen


In this paper, a new active learning method is proposed, which distinguishes between crop and weed based on their different spectral reflectances. A method comprising novel detection and incremental class augmentation was implemented. Novel detection was based on one class classifiers. Under field conditions, crop and weed species can be recognised successfully. Best results for the active learning were obtained for the one-class Mixture of Gaussians (MOG) and Self Organising Map (SOM) classifiers. The MOG and SOM performance in crop recognition was 100% and 96% respectively while the recognition for different weed species ranged from 31-98% (MOG) and 43-94% (SOM).


spectrograph self-organizing map mixture of Gaussians one-class classifier 

Copyright information

© Wageningen Academic Publishers The Netherlands 2013

Authors and Affiliations

  • D. Moshou
    • 1
  • D. Kateris
    • 1
  • X-E. Pantazi
    • 1
  • I. Gravalos
    • 2
  1. 1.Agricultural Engineering LaboratoryAristotle University, School of AgricultureThessalonikiGreece
  2. 2.Department of Biosystems EngineeringTechnological Educational Institute of Larissa, School of Agricultural TechnologyLarissaGreece

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