Detection of weeds using image processing and clustering

Publikations-Art

Kongressbeitrag

Autoren

Weis, Martin and Gerhards, Roland

Erscheinungsjahr

2009

Veröffentlicht in

Image analysis for agricultural products and processes

Herausgeber

Zude, Manuela

Verlag

Leibnitz Institute for Agricultural Enineering (ATB) , Potsdam-Bornim

Band/Volume

69/

Serie/Bezeichnung

Bornimer Agrartechnische Berichte

ISBN / ISSN / eISSN

00947-7314

Seite (von - bis)

138-144

Tagungsname

1st International Workshop on Computer Image Analysis in Agriculture and 15. Workshop Computer-Bildanalyse in der Landwirtschaft

Tagungsort

Potsdam, Germany

Tagungsdatum

27-28 August 2009

Knowledge about the distribution of weeds in the field is a prerequisite for site-specific treatment.
Optical sensors make it possible to detect varying weed densities and species, which can be mapped using GPS data.
The weeds are extracted from images using image processing and described by shape features.
A classification based on the features reveals the type and number of weeds per image.

For the classification only a maximum of 16 features out of the 81 computed ones are used.
Features are used, which enable an optimal distinction of the weed classes.
The selection can be done using data mining algorithms, which rate the discriminance of the features of prototypes.

If no prototypes are available, clustering algorithms can be used to automatically generate clusters.
In a next step weed classes can be assigned to the clusters.
Such a procedure aids to select prototypes, which is done manually.
Classes can be identified, that are distinct in the feature space or which are overlapping and therefore not well seperable.
Clustering can be used in some, less complex cases to establish an automatic procedure for the classification.

Weed maps are generated using the system.
These are compared to the results of a manual weed sampling.