Michael Henke, Stephan Huckemann, Winfried Kurth, Branislav Sloboda. (2014). Reconstructing leaf growth based on non-destructive digitizing and low-parametric shape evolution for plant modelling over a growth cycle. Silva Fennica vol. 48 no. 2 article id 1019. https://doi.org/10.14214/sf.1019

Keywords: growth modelling; non-destructive data acquisition; automated data extraction; image processing tool; leaf shape modelling; reusable modules; Populus x canadensis

Highlights: A complete pipeline for plant organ modelling (at the example of poplar leaves) is presented, from non-destructive data acquisition, over automated data extraction, to growth and shape modelling; Leaf contour models are compared; Resulting “organ” modules are ready for use in FSPMs.

Abstract | Full text in HTML | Full text in PDF | Author Info

A simple and efficient photometric methodology is presented, covering all steps from field data acquisition to binarization and allowing for leaf contour modelling. This method comprises the modelling of area and size (correlated and modelled with a Chapman-Richards growth function, using final length as one parameter), and four shape descriptors, from which the entire contour can be reconstructed rather well using a specific spline methodology. As an improvement of this contour modelling method, a set of parameterized polynomials was used. To model the temporal kinetics of the shape, geodesics in shape spaces were employed. Finally it is shown how this methodology is integrated into the 3D modelling platform GroIMP.

Henke, Department Ecoinformatics, Biometrics & Forest Growth, University of Göttingen, 37077 Göttingen, Germany E-mail: mhenke@uni-goettingen.de
Huckemann, Institute of Mathematical Stochastics, University of Göttingen, 37077 Göttingen, Germany E-mail: huckeman@math.uni-goettingen.de
Kurth, Department Ecoinformatics, Biometrics & Forest Growth, University of Göttingen, 37077 Göttingen, Germany E-mail: wk@informatik.uni-goettingen.de
Sloboda, Department Ecoinformatics, Biometrics & Forest Growth, University of Göttingen, 37077 Göttingen, Germany E-mail: bslobod@web.de

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