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Proceedings Paper

Crop/weed discrimination in simulated agronomic images
Author(s): G. Jones; C. Gée; F. Truchetet
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Paper Abstract

In the context of site-specific weed management by vision systems, an efficient image processing for a crop/weed discrimination is required in order to quantify the Weed Infestation Rate (WIR) in an image. This paper presents a modeling of crop field in presence of different Weed Infestation Rates and a set of simulated agronomic images is used to test and validate the effectiveness of a crop/weed discrimination algorithm. For instance, an algorithm has been implemented to firstly detect the crop rows in the field by the use of a Hough Transform and secondly to detect plant areas by a region based-segmentation on binary images. This image processing has been tested on virtual cereal fields of a large field of view with perspective effects. The vegetation in the virtual field is modeled by a sowing pattern for crop plants and the weed spatial distribution is modeled by either a Poisson process or a Neyman-Scott cluster process. For each simulated image, a comparison between the initial and the detected weed infestation rate allows us to assess the accuracy of the algorithm. This comparison demonstrates an accuracy of better than 80% is possible, despite that intrarow weeds can not be detected from this spatial method.

Paper Details

Date Published: 8 March 2007
PDF: 7 pages
Proc. SPIE 6497, Image Processing: Algorithms and Systems V, 64970E (8 March 2007); doi: 10.1117/12.704012
Show Author Affiliations
G. Jones, ENESAD, DSI, LGAP (France)
C. Gée, ENESAD, DSI, LGAP (France)
F. Truchetet, Le2i, CNRS (France)


Published in SPIE Proceedings Vol. 6497:
Image Processing: Algorithms and Systems V
Jaakko T. Astola; Karen O. Egiazarian; Edward R. Dougherty, Editor(s)

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