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Silva Fennica 1926-1997
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Acta Forestalia Fennica
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Articles containing the keyword 'image processing'.

Category: Research article

article id 7740, category Research article
Jonas R. Coussement, Kathy Steppe, Peter Lootens, Isabel Roldán-Ruiz, Tom De Swaef. (2018). A flexible geometric model for leaf shape descriptions with high accuracy. Silva Fennica vol. 52 no. 2 article id 7740. https://doi.org/10.14214/sf.7740
Highlights: A method for assessing leaf shape for 3D plant models is proposed; The model is highly flexible and fits a large variety of shapes; It allows analysis of shape differences within and between leaf datasets.

Accurate assessment of canopy structure is crucial in studying plant-environment interactions. The advancement of functional-structural plant models (FSPM), which incorporate the 3D structure of individual plants, increases the need for a method for accurate mathematical descriptions of leaf shape. A model was developed as an improvement of an existing leaf shape algorithm to describe a large variety of leaf shapes. Modelling accuracy was evaluated using a spatial segmentation method and shape differences were assessed using principal component analysis (PCA) on the optimised parameters. Furthermore, a method is presented to calculate the mean shape of a dataset, intended for obtaining a representative shape for modelling purposes. The presented model is able to accurately capture a large range of single, entire leaf shapes. PCA illustrated the interpretability of the parameter values and allowed evaluation of shape differences. The model parameters allow straightforward digital reconstruction of leaf shapes for modelling purposes such as FSPMs.

  • Coussement, Plant Sciences Unit, Institute for Agricultural and Fisheries Research (ILVO), Caritasstraat 39, B-9090 Melle, Belgium; Laboratory of Plant Ecology, Department of Plants and Crops, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium ORCID ID:E-mail: jonas.coussement@ilvo.vlaanderen.be
  • Steppe, Laboratory of Plant Ecology, Department of Plants and Crops, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium ORCID ID:E-mail: kathy.steppe@ugent.be
  • Lootens, Plant Sciences Unit, Institute for Agricultural and Fisheries Research (ILVO), Caritasstraat 39, B-9090 Melle, Belgium ORCID ID:E-mail: peter.lootens@ilvo.vlaanderen.be
  • Roldán-Ruiz, Plant Sciences Unit, Institute for Agricultural and Fisheries Research (ILVO), Caritasstraat 39, B-9090 Melle, Belgium; Department of Plant Biotechnology and Bioinformatics, Faculty of Sciences, Ghent University, Technologiepark Zwijnaarde 927, B-9052 Zwijnaarde, Belgium ORCID ID:E-mail: isabel.roldan-ruiz@ilvo.vlaanderen.be
  • De Swaef, Plant Sciences Unit, Institute for Agricultural and Fisheries Research (ILVO), Caritasstraat 39, B-9090 Melle, Belgium ORCID ID:E-mail: tom.deswaef@ilvo.vlaanderen.be (email)
article id 1019, category Research article
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
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.
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 ORCID ID:E-mail: mhenke@uni-goettingen.de (email)
  • Huckemann, Institute of Mathematical Stochastics, University of Göttingen, 37077 Göttingen, Germany ORCID ID:E-mail: huckeman@math.uni-goettingen.de
  • Kurth, Department Ecoinformatics, Biometrics & Forest Growth, University of Göttingen, 37077 Göttingen, Germany ORCID ID:E-mail: wk@informatik.uni-goettingen.de
  • Sloboda, Department Ecoinformatics, Biometrics & Forest Growth, University of Göttingen, 37077 Göttingen, Germany ORCID ID:E-mail: bslobod@web.de
article id 629, category Research article
S. Samarasinghe, G. D. Kulasiri. (2000). Displacement fields of wood in tension based on image processing: Part 1. Tension parallel- and perpendicular- to grain and comparisons with isotropic behaviour. Silva Fennica vol. 34 no. 3 article id 629. https://doi.org/10.14214/sf.629
Displacement fields for tensile loaded rubber and wood in parallel- and perpendicular-to-grain were obtained from digital image correlation. The results showed that the digital image correlation can reveal fine details of the nature of displacements in both rubber and wood. It was found that when load is perpendicular-to-grain, the lignin matrix produces uniform displacement fields similar to that of isotropic rubber. Uniform displacement fields also observed when lignin is involved in contraction due to Poisson effect in parallel-to-grain tension. However, when tracheids carry the load in parallel-to-grain loading, or are compressed in perpendicular-to-grain loading, a complex displacement pattern distorted by internal shear stress and slippage is produced.
  • Samarasinghe, Lincoln University, Appl. Computing, Mathematics and Statistics Group, P.O. Box 84, Canterbury, New Zealand ORCID ID:E-mail:
  • Kulasiri, Lincoln University, Appl. Computing, Mathematics and Statistics Group, P.O. Box 84, Canterbury, New Zealand ORCID ID:E-mail: kulasird@tui.lincoln.ac.nz (email)

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