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Pattern Analysis and Applications

, Volume 17, Issue 2, pp 401–414 | Cite as

Bayesian classification and unsupervised learning for isolating weeds in row crops

  • François-Michel De Rainville
  • Audrey Durand
  • Félix-Antoine Fortin
  • Kevin Tanguy
  • Xavier Maldague
  • Bernard Panneton
  • Marie-Josée Simard
Industrial and Commercial Application

Abstract

This paper presents a weed/crop classification method using computer vision and morphological analysis. Subsequent supervised and unsupervised learning methods are applied to extract dominant morphological characteristics of weeds present in corn and soybean fields. The novelty of the presented technique resides in the feature extraction process that is based on spatial localization of vegetation in fields. Features from the weed leaf area distribution are extracted from the cultivation inter-rows, then features from the crop are inferred from the mixture model equation. Those extracted features are then passed to a naive bayesian classifier and a gaussian mixture clustering algorithm to discriminate weed from crop plant. The presented technique correctly classifies an average of 94 % of corn and soybean plants and 85 % of the weed (multiple species) without any prior knowledge on the species present in the field.

Keywords

Bayesian classification Machine learning Weed detection Computer vision Plant morphology Corn and soybean fields 

Notes

Acknowledgements

The authors wish to thank B. Panneton and M.-J. Simard from Agriculture and Agri-Food Canada for their contributions and insights on the material presented in the current paper, L. Longchamps for his time labelling the test images and Prof. C. Gagné for his feedback and comments on the paper.

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Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  • François-Michel De Rainville
    • 1
  • Audrey Durand
    • 1
  • Félix-Antoine Fortin
    • 1
  • Kevin Tanguy
    • 1
  • Xavier Maldague
    • 1
  • Bernard Panneton
    • 2
  • Marie-Josée Simard
    • 3
  1. 1.Laboratoire de vision et systèmes numériques, Département de génie électrique et de génie informatiqueUniversité LavalQuebecCanada
  2. 2.Centre de recherche et de développement en horticultureAgriculture et Agroalimentaire CanadaSt-Jean-sur-RichelieuCanada
  3. 3.Centre de recherche et de développement sur les sols et les grandes culturesAgriculture et Agroalimentaire CanadaQuebecCanada

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