Simulation of agronomic images for an automatic evaluation of crop/ weed discrimination algorithm accuracy

Gawain Jones 1 Christelle Gée 1 Frederic Truchetet 2
1 LGAP - Laboratoire en Génie des Agroéquipements et des Procédés
2 Le2i - Laboratoire Electronique, Informatique et Image [UMR6303]
Abstract : In the context of precision agriculture, we present a robust and automatic method based on simulated images for evaluating the efficiency of any crop/weed discrimination algorithms for a inter-row weed infestation rate. To simulate these images two different steps are required: 1) modeling of a crop field from the spatial distribution of plants (crop and weed) 2) projection of the created field through an optical system to simulate photographing. Then an application is proposed investigating the accuracy and robustness of crop/weed discrimination algorithm combining a line detection (Hough transform) and a plant discrimination (crop and weeds). The accuracy of weed infestation rate estimate for each image is calculated by direct comparison to the initial weed infestation rate of the simulated images. It reveals an performance better than 85%.
Keywords : Simulated images Statistic Crop Weed Hough transform blob coloring Poisson law Neyman Scott process Pinhole model
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Conference papers
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https://hal-agrosup-dijon.archives-ouvertes.fr/hal-01827828
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Submitted on : Monday, July 2, 2018 - 5:02:09 PM
Last modification on : Saturday, July 14, 2018 - 1:05:53 AM

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AGROSUP-DIJON | ENESAD | CNRS | LE2I | UNIV-BOURGOGNE | ICB

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Gawain Jones, Christelle Gée, Frederic Truchetet. Simulation of agronomic images for an automatic evaluation of crop/ weed discrimination algorithm accuracy. Eigth International Conference on Quality Control by Artificial Vision, May 2007, Le Creusot, France. ⟨10.1117/12.736905⟩. ⟨hal-01827828⟩

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