AE—Automation and Emerging Technologies
Identifieur interne : 009391 ( Main/Exploration ); précédent : 009390; suivant : 009392AE—Automation and Emerging Technologies
Auteurs : A.-G. Manh [France] ; G. Rabatel [France] ; L. Assemat [France] ; M.-J. Aldon [France]Source :
- Journal of Agricultural Engineering Research [ 0021-8634 ] ; 2001.
English descriptors
- Teeft :
- Active contour models, Agricultural engineering research, Asae, Binary segmentation threshold, Brown steckler, Colour, Computer vision, Deformable, Deformable models, Deformable templates, Digital images, Energy gradient, Energy minimization, Energy term, Erroneous models, Foxtail, Gradient direction, Gradient forces, Green foxtail, Growth stage, Herbicide, Ieee transactions, Image energy, Image processing, Internal constraints, Internal energy, International journal, Journees internationales, Leaf edges, Leaf shape, Local criterion, Local forces, Lutte contre, Machine intelligence, Machine vision, Mahalanobis distance, Mauvaises herbes, Medical imaging, Model contour, Model evolution, Model initialization, Model structure, Montpellier cedex, Next step, Other applications, Parametric model, Pattern analysis, Pixel, Plant colour class, Plant transactions, Prescription maps, Quality criterion, Segmentation, Setaria viridis, Silsoe research institute, Skeleton, Spatial distribution, Spectral properties, Spot spraying, Vegetation pixels, Visual assessment, Weed, Weed competitivity, Weed populations, Weed science, Weed species, Weed technology, Weeds.
Abstract
Abstract: In order to improve weeding strategies in terms of pesticide reduction, spatial distribution and characterization of in-field weed populations are important. With recent improvements in image processing, many studies have focused on weed detection by vision techniques. However, weed identification still remains difficult because of outdoor scenic complexity and morphological variability of plants. A new method of weed leaf segmentation based on the use of deformable templates is proposed. This approach has the advantage of applying a priori knowledge to the object searched, improving the robustness of the segmentation stage. The principle consists of fitting a parametric model to the leaf outlines in the image, by minimizing an energy term related to internal constraints of the model and salient features of the image, such as the colour of the plant. This method showed promising results for one weed species, green foxtail (Setaria viridis). In particular, it was possible to characterize partially occluded leaves. This constitutes a first step towards a recognition system, based on leaf characteristics and their relative spatial position.
Url:
DOI: 10.1006/jaer.2001.0725
Affiliations:
- France
- Bourgogne, Bourgogne-Franche-Comté, Languedoc-Roussillon, Occitanie (région administrative)
- Dijon, Montpellier
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Le document en format XML
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With recent improvements in image processing, many studies have focused on weed detection by vision techniques. However, weed identification still remains difficult because of outdoor scenic complexity and morphological variability of plants. A new method of weed leaf segmentation based on the use of deformable templates is proposed. This approach has the advantage of applying a priori knowledge to the object searched, improving the robustness of the segmentation stage. The principle consists of fitting a parametric model to the leaf outlines in the image, by minimizing an energy term related to internal constraints of the model and salient features of the image, such as the colour of the plant. This method showed promising results for one weed species, green foxtail (Setaria viridis). In particular, it was possible to characterize partially occluded leaves. 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