Automated weed detection in winter wheat by using artificial neural networks

A. Kluge; H. Nordmeyer

Automated weed detection in winter wheat by using artificial neural networks

In: Precision agriculture '09

Authors:

A. Kluge

A. Kluge Julius Kühn-Institut. Federal Research Centre for Cultivated Plants, Institute for Plant Protection in Field Crops and Grassland, Messeweg 11-12, 38100 Braunschweig, Germany; Alexander.Kluge@jki.bund.de

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H. Nordmeyer

H. Nordmeyer Julius Kühn-Institut. Federal Research Centre for Cultivated Plants, Institute for Plant Protection in Field Crops and Grassland, Messeweg 11-12, 38100 Braunschweig, Germany; Alexander.Kluge@jki.bund.de

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Type:: Chapter

Pages:: 321–327

DOI:: https://doi.org/10.1163/9789086866649_039

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Abstract

A weed detection system was developed that consisted of artificial neural networks. A CCD camera acquired digital colour images of weeds. To recognize weeds more effectively than by discriminant analysis, an artificial neural network was used. In this paper new geometrical features were applied, these were the angle between a point on the leaf axis and a point at the edge of the leaf. This feature is nearly independent from the dimension of leaves. Several angles measured in this way were evaluated by an artificial neural network. A classification between two selected weeds (Galium aparine and Veronica hederifolia) was made. The recognition rate was 90.0% for Veronica hederifolia and 93.4% for Galium aparine. The results show that the neural network model can distinguish weed species at the cotyledon growth stage. The weed detection system with artificial neural networks could improve weed detection for site specific weed control.

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Precision agriculture '09

E-Book ISBN:: 9789086866649

Publisher:: Wageningen Academic

Print Publication Date:: 01 Jan 2009

Subjects
- Life Sciences
  - General
  - Agriculture & Environment

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Automated weed detection in winter wheat by using artificial neural networks

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Precision agriculture '09

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