AE—Automation and Emerging Technologies: Weed Leaf Image Segmentation by Deformable Templates

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.

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