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Modelling agronomic images for weed detection and comparison of crop/weed discrimination algorithm performance

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Abstract

A new method for weed detection based on modelling agronomic images taken from a virtual camera placed in a virtual field is proposed. The aim was to measure and compare the effectiveness of the developed algorithms. Two sets of images with and without perspective effects were simulated. For images with no perspective, based on Gabor filtering and on the Hough transform, the performance of two crop/inter-row weed discrimination algorithms were tested and compared. The method based on the Hough transform is, in any case, better than the one based on Gabor filtering. For images with perspective effects only, an algorithm based on the Hough transform was tested and an extension to real images is discussed. These tests were done by a comparison between the weed infestation rate detected by these algorithms and the true one. This evaluation was completed with a crop/weed pixel classification and it demonstrated that the algorithm based on a Hough transform gave the best results (up to 90%).

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Acknowledgements

The authors are grateful for the financial support provided by Tecnoma (trademark of the EXEL Industries group: http://www.tecnoma.com) and the Regional Council of Burgundy.

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Authors and Affiliations

  1. ENESAD/DSI/UP-GAP ‘Génie des Agroéquipements et des Procédés’, 21 Bld Olivier de Serres, 21800, Quetigny, France

    G. Jones & Ch. Gée

  2. LE2i, UMR 5158 uB-CNRS, 12 rue de la Fonderie, 71200, Le Creusot, France

    F. Truchetet

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  1. G. Jones
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  2. Ch. Gée
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  3. F. Truchetet
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Correspondence to Ch. Gée.

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Jones, G., Gée, C. & Truchetet, F. Modelling agronomic images for weed detection and comparison of crop/weed discrimination algorithm performance. Precision Agric 10, 1–15 (2009). https://doi.org/10.1007/s11119-008-9086-9

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