Reconstructing leaf growth based on non-destructive digitizing and low-parametric shape evolution for plant modelling over a growth cycle

Abstract

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. 23 p. 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. AbstractA 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.

Using the Negative Soil Adjustment Factor of Soil Adjusted Vegetation Index (SAVI) to Resist Saturation Effects and Estimate Leaf Area Index (LAI) in Dense Vegetation Areas

“…In particular, Vegetation indices (VIs) are often used to estimate LAI from broad spectral bands [7,8]. Although their analytical expressions differ significantly, the implementations of these indices can be divided roughly into three categories: (1) Intrinsic VIs such as simple ratio (SR) [9] and normalized difference vegetation index (NDVI) [10]. (2) Soil adjusted VIs such as soil adjusted vegetation index (SAVI) [11], transformed SAVI (TSAVI) [12], modified SAVI (MSAVI) [13], modified transformed SAVI (MTSAVI) [14], optimized SAVI (OSAVI) [15], and generalized SAVI (GESAVI) [16].…”

A flexible geometric model for leaf shape descriptions with high accuracy

“…This allows the user to replace one or more parameters with well-chosen constants, effectively finetuning the model for the leaves in question. In comparison to other methods, this model provides the advantage of being able to describe multivalued function shapes (as opposed to polynomials (Fournier et al 2003), smoothing splines (Mündermann et al 2005;Henke et al 2014) or hermit curves (Henke et al 2014)) with high flexibility (as opposed to Bézier curves (Chi et al 2003)) and a relatively low amount of diagnostic parameters (as opposed to elliptic Fourier analysis (Iwata et al 2002;Neto et al 2006)). …”

“…The prediction of these complex interactions and how morphological traits influence the final production is an important issue for applications such as plant breeding (Bergez et al 2013). This makes a mathematical description and simulation of realistic leaf shapes indispensable in plant and tree models that take 3D geometry into account, such as functional-structural plant models (FSPMs) (Perttunen et al 1996;Henke et al 2014).…”

“…This can be achieved using polynomials (Fournier et al 2003), smoothing splines (Mündermann et al 2005;Henke et al 2014) or hermit curves (Henke et al 2014). These methods are able to accurately describe simple leaf shapes, but have non-functional parameters, and therefore, cannot be directly interpreted as geometric features.…”

“…A typical approximation uses the first 20 (Iwata et al 2002) or 30 (Neto et al 2006) harmonics resulting in 77 or 117 (non-functional) parameters, respectively. While this imposes no limitations for multivariate analysis, such a number of parameters are not practically applicable for plant and tree models (Henke et al 2014). Bézier curves (Chi et al 2003) allow the description of a multivalued function with a low number of functional parameters.…”