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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

GUI for leaf segmentation. Graphical user interface for leaf segmentation: This GUI from module 2 provides a half-automated graph-based method (FH-algorithm) to segment leaves or leaf sections in RGB images. Selection of small interest regions within the input RGB image (top right) allows for a fast FH segmentation with a subsequent detailed editing (with the tools on the bottom left). Segments of acceptable quality may then be transferred to the final output (bottom right). FH segmentation parameters are regulated in the HSV color space together with a threshold for depth separation (left). Pre-processing specifications (e.g. smoothing and background segmentation) are inserted on the top left side. The current state shows an intermediate result of a segmentation for complete leaves.
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Fig6: GUI for leaf segmentation. Graphical user interface for leaf segmentation: This GUI from module 2 provides a half-automated graph-based method (FH-algorithm) to segment leaves or leaf sections in RGB images. Selection of small interest regions within the input RGB image (top right) allows for a fast FH segmentation with a subsequent detailed editing (with the tools on the bottom left). Segments of acceptable quality may then be transferred to the final output (bottom right). FH segmentation parameters are regulated in the HSV color space together with a threshold for depth separation (left). Pre-processing specifications (e.g. smoothing and background segmentation) are inserted on the top left side. The current state shows an intermediate result of a segmentation for complete leaves.

Mentions: Background Figure 2B and Figure 6 display the outline and the GUI of the leaf segmentation process. To calculate leaf angle distribution each pixel has to be associated with a single leaf and then pixels have to be fitted by a realistic 3-d leaf model. For the planar leaves of soy bean, leaf segmentation was implemented as a graph partitioning method [27]. This method, also referred to as the Felzenszwalb-Huttenlocher (FH) algorithm [51], applies a graph structure on any pre-processed (usually blurred) image information considering pixels as nodes and differences in pixel properties as weighted edges. In the HSV color space, the pixel properties are hue (H), saturation (S) and value (V). We optimized this approach for a better identification of single leaves in various plant species and canopies, as described below.Figure 6


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)

GUI for leaf segmentation. Graphical user interface for leaf segmentation: This GUI from module 2 provides a half-automated graph-based method (FH-algorithm) to segment leaves or leaf sections in RGB images. Selection of small interest regions within the input RGB image (top right) allows for a fast FH segmentation with a subsequent detailed editing (with the tools on the bottom left). Segments of acceptable quality may then be transferred to the final output (bottom right). FH segmentation parameters are regulated in the HSV color space together with a threshold for depth separation (left). Pre-processing specifications (e.g. smoothing and background segmentation) are inserted on the top left side. The current state shows an intermediate result of a segmentation for complete leaves.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig6: GUI for leaf segmentation. Graphical user interface for leaf segmentation: This GUI from module 2 provides a half-automated graph-based method (FH-algorithm) to segment leaves or leaf sections in RGB images. Selection of small interest regions within the input RGB image (top right) allows for a fast FH segmentation with a subsequent detailed editing (with the tools on the bottom left). Segments of acceptable quality may then be transferred to the final output (bottom right). FH segmentation parameters are regulated in the HSV color space together with a threshold for depth separation (left). Pre-processing specifications (e.g. smoothing and background segmentation) are inserted on the top left side. The current state shows an intermediate result of a segmentation for complete leaves.
Mentions: Background Figure 2B and Figure 6 display the outline and the GUI of the leaf segmentation process. To calculate leaf angle distribution each pixel has to be associated with a single leaf and then pixels have to be fitted by a realistic 3-d leaf model. For the planar leaves of soy bean, leaf segmentation was implemented as a graph partitioning method [27]. This method, also referred to as the Felzenszwalb-Huttenlocher (FH) algorithm [51], applies a graph structure on any pre-processed (usually blurred) image information considering pixels as nodes and differences in pixel properties as weighted edges. In the HSV color space, the pixel properties are hue (H), saturation (S) and value (V). We optimized this approach for a better identification of single leaves in various plant species and canopies, as described below.Figure 6

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