Limits...
The leaf angle distribution of natural plant populations: assessing the canopy with a novel software tool.

Müller-Linow M, Pinto-Espinosa F, Scharr H, Rascher U - Plant Methods (2015)

Bottom Line: Based on the resulting surface meshes leaf angle statistics are computed on the whole-leaf level or from local derivations.In contrast nitrogen treatment had no effect on leaf angles.Our software package provides whole-leaf statistics but also a local estimation of leaf angles, which may have great potential to better understand and quantify structural canopy traits for guided breeding and optimized crop management.

View Article: PubMed Central - PubMed

Affiliation: Institute of Bio and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str, Jülich, 52425 Germany.

ABSTRACT

Background: Three-dimensional canopies form complex architectures with temporally and spatially changing leaf orientations. Variations in canopy structure are linked to canopy function and they occur within the scope of genetic variability as well as a reaction to environmental factors like light, water and nutrient supply, and stress. An important key measure to characterize these structural properties is the leaf angle distribution, which in turn requires knowledge on the 3-dimensional single leaf surface. Despite a large number of 3-d sensors and methods only a few systems are applicable for fast and routine measurements in plants and natural canopies. A suitable approach is stereo imaging, which combines depth and color information that allows for easy segmentation of green leaf material and the extraction of plant traits, such as leaf angle distribution.

Results: We developed a software package, which provides tools for the quantification of leaf surface properties within natural canopies via 3-d reconstruction from stereo images. Our approach includes a semi-automatic selection process of single leaves and different modes of surface characterization via polygon smoothing or surface model fitting. Based on the resulting surface meshes leaf angle statistics are computed on the whole-leaf level or from local derivations. We include a case study to demonstrate the functionality of our software. 48 images of small sugar beet populations (4 varieties) have been analyzed on the base of their leaf angle distribution in order to investigate seasonal, genotypic and fertilization effects on leaf angle distributions. We could show that leaf angle distributions change during the course of the season with all varieties having a comparable development. Additionally, different varieties had different leaf angle orientation that could be separated in principle component analysis. In contrast nitrogen treatment had no effect on leaf angles.

Conclusions: We show that a stereo imaging setup together with the appropriate image processing tools is capable of retrieving the geometric leaf surface properties of plants and canopies. Our software package provides whole-leaf statistics but also a local estimation of leaf angles, which may have great potential to better understand and quantify structural canopy traits for guided breeding and optimized crop management.

No MeSH data available.


Related in: MedlinePlus

Principal component analysis (PCA) of plot-specific leaf angle distributions. PCA reveals distinct clustering of states and cultivars, while nitrogen treatment effects are lacking. (A) Complete set of 48 analyzed images representing 2 repetitions (1,2) of 24 parameter constellations - 4 cultivars (B,C,M,P), 3 states (s, m, l) and 2 nitrogen treatments (+, -): The states (color-indexed measurement days) display strong clustering with the strongest separation of the youngest plant state. (B-D) Analysis of each state indicates variety-specific leaf angle distributions resulting in a pronounced clustering of all 4 cultivars (color-indexed) regardless of the measurement day.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4359433&req=5

Fig11: Principal component analysis (PCA) of plot-specific leaf angle distributions. PCA reveals distinct clustering of states and cultivars, while nitrogen treatment effects are lacking. (A) Complete set of 48 analyzed images representing 2 repetitions (1,2) of 24 parameter constellations - 4 cultivars (B,C,M,P), 3 states (s, m, l) and 2 nitrogen treatments (+, -): The states (color-indexed measurement days) display strong clustering with the strongest separation of the youngest plant state. (B-D) Analysis of each state indicates variety-specific leaf angle distributions resulting in a pronounced clustering of all 4 cultivars (color-indexed) regardless of the measurement day.

Mentions: As these results displayed only slight differences between the cultivars, we analyzed the leaf angle distributions of the 48 combinations (measurement date, cultivar, nitrogen treatment and repetition) more deeply by performing a principle component analysis (PCA). To this end we interpret each angle distribution with its N bins as a point in an N-dimensional space, i.e. we populate this space with 48 points. PCA then delivers directions of main variations in this N-dimensional space. We investigated clustering effects when using θ- and φ-distributions separately or jointly. As effects were more pronounced for the latter case, we focus on this analysis in the following. The first two components of the PCA have been depicted in Figure 11A. Most apparently, the three plant states are well clustered (as indicated by the three colors) and also separated in the case of the youngest state s. There is no systematics within the distribution of nitrogen treatments, but clustering of cultivars is present within each sub-group. For a detailed analysis we repeated the PCA separately for each measurement date (Figure 11B-D). As indicated before, all states are featured by a fairly good separation of cultivars, especially for the last state l, while nitrogen treatment effects seem to be negligible. This study was also carried out with a planar leaf model and the quadratic surface function model. The results were comparable but less pronounced than with the model-free surface smoothing option.Figure 11


The leaf angle distribution of natural plant populations: assessing the canopy with a novel software tool.

Müller-Linow M, Pinto-Espinosa F, Scharr H, Rascher U - Plant Methods (2015)

Principal component analysis (PCA) of plot-specific leaf angle distributions. PCA reveals distinct clustering of states and cultivars, while nitrogen treatment effects are lacking. (A) Complete set of 48 analyzed images representing 2 repetitions (1,2) of 24 parameter constellations - 4 cultivars (B,C,M,P), 3 states (s, m, l) and 2 nitrogen treatments (+, -): The states (color-indexed measurement days) display strong clustering with the strongest separation of the youngest plant state. (B-D) Analysis of each state indicates variety-specific leaf angle distributions resulting in a pronounced clustering of all 4 cultivars (color-indexed) regardless of the measurement day.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4359433&req=5

Fig11: Principal component analysis (PCA) of plot-specific leaf angle distributions. PCA reveals distinct clustering of states and cultivars, while nitrogen treatment effects are lacking. (A) Complete set of 48 analyzed images representing 2 repetitions (1,2) of 24 parameter constellations - 4 cultivars (B,C,M,P), 3 states (s, m, l) and 2 nitrogen treatments (+, -): The states (color-indexed measurement days) display strong clustering with the strongest separation of the youngest plant state. (B-D) Analysis of each state indicates variety-specific leaf angle distributions resulting in a pronounced clustering of all 4 cultivars (color-indexed) regardless of the measurement day.
Mentions: As these results displayed only slight differences between the cultivars, we analyzed the leaf angle distributions of the 48 combinations (measurement date, cultivar, nitrogen treatment and repetition) more deeply by performing a principle component analysis (PCA). To this end we interpret each angle distribution with its N bins as a point in an N-dimensional space, i.e. we populate this space with 48 points. PCA then delivers directions of main variations in this N-dimensional space. We investigated clustering effects when using θ- and φ-distributions separately or jointly. As effects were more pronounced for the latter case, we focus on this analysis in the following. The first two components of the PCA have been depicted in Figure 11A. Most apparently, the three plant states are well clustered (as indicated by the three colors) and also separated in the case of the youngest state s. There is no systematics within the distribution of nitrogen treatments, but clustering of cultivars is present within each sub-group. For a detailed analysis we repeated the PCA separately for each measurement date (Figure 11B-D). As indicated before, all states are featured by a fairly good separation of cultivars, especially for the last state l, while nitrogen treatment effects seem to be negligible. This study was also carried out with a planar leaf model and the quadratic surface function model. The results were comparable but less pronounced than with the model-free surface smoothing option.Figure 11

Bottom Line: Based on the resulting surface meshes leaf angle statistics are computed on the whole-leaf level or from local derivations.In contrast nitrogen treatment had no effect on leaf angles.Our software package provides whole-leaf statistics but also a local estimation of leaf angles, which may have great potential to better understand and quantify structural canopy traits for guided breeding and optimized crop management.

View Article: PubMed Central - PubMed

Affiliation: Institute of Bio and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str, Jülich, 52425 Germany.

ABSTRACT

Background: Three-dimensional canopies form complex architectures with temporally and spatially changing leaf orientations. Variations in canopy structure are linked to canopy function and they occur within the scope of genetic variability as well as a reaction to environmental factors like light, water and nutrient supply, and stress. An important key measure to characterize these structural properties is the leaf angle distribution, which in turn requires knowledge on the 3-dimensional single leaf surface. Despite a large number of 3-d sensors and methods only a few systems are applicable for fast and routine measurements in plants and natural canopies. A suitable approach is stereo imaging, which combines depth and color information that allows for easy segmentation of green leaf material and the extraction of plant traits, such as leaf angle distribution.

Results: We developed a software package, which provides tools for the quantification of leaf surface properties within natural canopies via 3-d reconstruction from stereo images. Our approach includes a semi-automatic selection process of single leaves and different modes of surface characterization via polygon smoothing or surface model fitting. Based on the resulting surface meshes leaf angle statistics are computed on the whole-leaf level or from local derivations. We include a case study to demonstrate the functionality of our software. 48 images of small sugar beet populations (4 varieties) have been analyzed on the base of their leaf angle distribution in order to investigate seasonal, genotypic and fertilization effects on leaf angle distributions. We could show that leaf angle distributions change during the course of the season with all varieties having a comparable development. Additionally, different varieties had different leaf angle orientation that could be separated in principle component analysis. In contrast nitrogen treatment had no effect on leaf angles.

Conclusions: We show that a stereo imaging setup together with the appropriate image processing tools is capable of retrieving the geometric leaf surface properties of plants and canopies. Our software package provides whole-leaf statistics but also a local estimation of leaf angles, which may have great potential to better understand and quantify structural canopy traits for guided breeding and optimized crop management.

No MeSH data available.


Related in: MedlinePlus