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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.


Box diagram of module 1-3. General outline of the leaf angle processing toolbox: The structure of this toolbox comprises 3 basic modules (A-C), all of them controlled by individual graphical user interfaces (these GUIs are depicted in Figures 3, 4, and 5). Optional GUI-supported tools for HSV segmentation, stereo rig settings and result summary are not illustrated here. Module 3 comprises two alternative processing paths (left: via surface smoothing; right: via surface fitting). Alternative and optional modes in module 1 are indicated on the right sides as dotted boxes. Round boxes indicate the input and outcome of a process, rectangular boxes the processes themselves. Green boxes point to the subsequent processes in the other modules. The 3-d reconstruction (A) starts with the input of stereo images, calibration images and technical specifications. Outputs are rectified images and disparity maps on the one hand, which serve as the input data for the subsequent segmentation process. On the other hand, the 3-d point cloud data is transferred to the surface modeling process. With the data provided by the 3-d reconstruction the full or partial recognition of leaves is the intention of the image segmentation (B). On the base of segment-specific points clouds leaf surface structures are modeled in the third module (C) either using smoothing operations or bipolynomial surface functions. The resulting polygon mesh of the canopy provides a basis for further statistical analysis of particular plant traits like leaf angles or leaf area.
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Fig2: Box diagram of module 1-3. General outline of the leaf angle processing toolbox: The structure of this toolbox comprises 3 basic modules (A-C), all of them controlled by individual graphical user interfaces (these GUIs are depicted in Figures 3, 4, and 5). Optional GUI-supported tools for HSV segmentation, stereo rig settings and result summary are not illustrated here. Module 3 comprises two alternative processing paths (left: via surface smoothing; right: via surface fitting). Alternative and optional modes in module 1 are indicated on the right sides as dotted boxes. Round boxes indicate the input and outcome of a process, rectangular boxes the processes themselves. Green boxes point to the subsequent processes in the other modules. The 3-d reconstruction (A) starts with the input of stereo images, calibration images and technical specifications. Outputs are rectified images and disparity maps on the one hand, which serve as the input data for the subsequent segmentation process. On the other hand, the 3-d point cloud data is transferred to the surface modeling process. With the data provided by the 3-d reconstruction the full or partial recognition of leaves is the intention of the image segmentation (B). On the base of segment-specific points clouds leaf surface structures are modeled in the third module (C) either using smoothing operations or bipolynomial surface functions. The resulting polygon mesh of the canopy provides a basis for further statistical analysis of particular plant traits like leaf angles or leaf area.

Mentions: Our software package has been developed with Matlab R2012b on a Windows-based platform. Three external toolboxes for image calibration [31-33] and a tool for unstructured 2-d triangular surface meshing [34] are included in the package. Image, calibration and subsequent computed data are organized within a project structure, which builds on a fixed stereo setup. The software consists of 3 essential modules, which control the 3-d reconstruction (i), the leaf segmentation (ii) and the surface modeling (iii), all of them featuring individual graphical user interfaces (GUIs). An outline of each module is given in the block diagram in Figure 2. All modules are interlinked, i.e. some work only with the particular input data (B-C top: green-framed boxes), which come from the first two modules (4 green boxes in Figure 2). Alternative processing options are indicated as dotted boxes, which may be applied. A more vivid view on the overall process is depicted in Figure 3 which uses an example of our case study. The outcome is a 3-d polygon mesh on the base of fitted (planar, quadratic and cubic surface function) or smoothed (Laplacian or curvature flow) leaf surface models, which then can be used for further surface statistics, e.g. estimation of the leaf angle distribution and leaf area index. In addition to surface reconstruction this tool also provides linear, quadratic and cubic modeling of leaf axes and calculation of the respective leaf axes angles. We tested this to be useful for modeling grass-like species, but do not go into details here. We included four additional tools each equipped with a GUI, which will also be outlined here only briefly. The first one (depicted in Figure 4) uses a manual segmentation approach to separate plant pixels from the background. This segmentation, which helps to improve the result in module (i) and (ii), is performed in the HSV color space [35]. The second supplementary tool helps to select the right settings for individual cameras and stereo rig. The the third tool is a visualization tool, which displays the highlights of each processed part together with a summary on settings and estimated parameters, like average leaf inclination and leaf area index. We are also providing an additional tool (with a GUI), which allows for an easy manual post-editing of prior segmentations. The main output of the complete processing pipeline is a surface mesh data file in the well-established ply-format and the leaf angle statistics as an excel-file.Figure 2


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)

Box diagram of module 1-3. General outline of the leaf angle processing toolbox: The structure of this toolbox comprises 3 basic modules (A-C), all of them controlled by individual graphical user interfaces (these GUIs are depicted in Figures 3, 4, and 5). Optional GUI-supported tools for HSV segmentation, stereo rig settings and result summary are not illustrated here. Module 3 comprises two alternative processing paths (left: via surface smoothing; right: via surface fitting). Alternative and optional modes in module 1 are indicated on the right sides as dotted boxes. Round boxes indicate the input and outcome of a process, rectangular boxes the processes themselves. Green boxes point to the subsequent processes in the other modules. The 3-d reconstruction (A) starts with the input of stereo images, calibration images and technical specifications. Outputs are rectified images and disparity maps on the one hand, which serve as the input data for the subsequent segmentation process. On the other hand, the 3-d point cloud data is transferred to the surface modeling process. With the data provided by the 3-d reconstruction the full or partial recognition of leaves is the intention of the image segmentation (B). On the base of segment-specific points clouds leaf surface structures are modeled in the third module (C) either using smoothing operations or bipolynomial surface functions. The resulting polygon mesh of the canopy provides a basis for further statistical analysis of particular plant traits like leaf angles or leaf area.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig2: Box diagram of module 1-3. General outline of the leaf angle processing toolbox: The structure of this toolbox comprises 3 basic modules (A-C), all of them controlled by individual graphical user interfaces (these GUIs are depicted in Figures 3, 4, and 5). Optional GUI-supported tools for HSV segmentation, stereo rig settings and result summary are not illustrated here. Module 3 comprises two alternative processing paths (left: via surface smoothing; right: via surface fitting). Alternative and optional modes in module 1 are indicated on the right sides as dotted boxes. Round boxes indicate the input and outcome of a process, rectangular boxes the processes themselves. Green boxes point to the subsequent processes in the other modules. The 3-d reconstruction (A) starts with the input of stereo images, calibration images and technical specifications. Outputs are rectified images and disparity maps on the one hand, which serve as the input data for the subsequent segmentation process. On the other hand, the 3-d point cloud data is transferred to the surface modeling process. With the data provided by the 3-d reconstruction the full or partial recognition of leaves is the intention of the image segmentation (B). On the base of segment-specific points clouds leaf surface structures are modeled in the third module (C) either using smoothing operations or bipolynomial surface functions. The resulting polygon mesh of the canopy provides a basis for further statistical analysis of particular plant traits like leaf angles or leaf area.
Mentions: Our software package has been developed with Matlab R2012b on a Windows-based platform. Three external toolboxes for image calibration [31-33] and a tool for unstructured 2-d triangular surface meshing [34] are included in the package. Image, calibration and subsequent computed data are organized within a project structure, which builds on a fixed stereo setup. The software consists of 3 essential modules, which control the 3-d reconstruction (i), the leaf segmentation (ii) and the surface modeling (iii), all of them featuring individual graphical user interfaces (GUIs). An outline of each module is given in the block diagram in Figure 2. All modules are interlinked, i.e. some work only with the particular input data (B-C top: green-framed boxes), which come from the first two modules (4 green boxes in Figure 2). Alternative processing options are indicated as dotted boxes, which may be applied. A more vivid view on the overall process is depicted in Figure 3 which uses an example of our case study. The outcome is a 3-d polygon mesh on the base of fitted (planar, quadratic and cubic surface function) or smoothed (Laplacian or curvature flow) leaf surface models, which then can be used for further surface statistics, e.g. estimation of the leaf angle distribution and leaf area index. In addition to surface reconstruction this tool also provides linear, quadratic and cubic modeling of leaf axes and calculation of the respective leaf axes angles. We tested this to be useful for modeling grass-like species, but do not go into details here. We included four additional tools each equipped with a GUI, which will also be outlined here only briefly. The first one (depicted in Figure 4) uses a manual segmentation approach to separate plant pixels from the background. This segmentation, which helps to improve the result in module (i) and (ii), is performed in the HSV color space [35]. The second supplementary tool helps to select the right settings for individual cameras and stereo rig. The the third tool is a visualization tool, which displays the highlights of each processed part together with a summary on settings and estimated parameters, like average leaf inclination and leaf area index. We are also providing an additional tool (with a GUI), which allows for an easy manual post-editing of prior segmentations. The main output of the complete processing pipeline is a surface mesh data file in the well-established ply-format and the leaf angle statistics as an excel-file.Figure 2

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.