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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.


Leaf angle distribution of a sugar beet sample. Leaf angle distributions of sugar beet populations: (A) frequency of zenith angles; (B) combined zenith and azimuth angles in a 2-d histogram plot for a single image of Berenika (May 30/ N+), the example given in Figure 3. Curvature flow smoothing has been applied on the 3-d data. In this representation the azimuth angles θ= 0°, 90°, 180° and 270° correspond to the cardinal points North, East, South and West. (C) compares state effects (date of measurement) and cultivar effects, each diagram representing the average of 2 repetitions and 2 different nitrogen treatments: As indicated by the overall averages (right), the growth state differences are most obviously reflected in the location and center of the azimuth distribution. (All images have been generated with an extra visualization tool).
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Fig10: Leaf angle distribution of a sugar beet sample. Leaf angle distributions of sugar beet populations: (A) frequency of zenith angles; (B) combined zenith and azimuth angles in a 2-d histogram plot for a single image of Berenika (May 30/ N+), the example given in Figure 3. Curvature flow smoothing has been applied on the 3-d data. In this representation the azimuth angles θ= 0°, 90°, 180° and 270° correspond to the cardinal points North, East, South and West. (C) compares state effects (date of measurement) and cultivar effects, each diagram representing the average of 2 repetitions and 2 different nitrogen treatments: As indicated by the overall averages (right), the growth state differences are most obviously reflected in the location and center of the azimuth distribution. (All images have been generated with an extra visualization tool).

Mentions: The orientation of each face (as given by the face normal) within the reconstructed meshes can easily be converted into azimuth (θ) and zenith (φ) angles and - if related to the individual face area - summed up to the distribution of leaf angles. Distributions are calculated as normalized histograms with 1° bin width, if not stated differently. The interpretation may be carried out on the basis of φ or θ angle distributions separately or as joint distributions, assuming that there is a dependency between both distributions. (i) Figure 10A shows the distribution of φ for Berenika (May 30/ N+). Further statistics measures like the mean or median of the φ distribution may help to characterize drought stress, diseases or diurnal leaf movements. The shape of the φ distribution displayed characteristic differences between the cultivars, which were distinctly increased in the older developmental states, while for the θ angle distributions we recorded seasonal differences particularly reflected in the location of the angle averages (data not shown). (ii) On a higher level θ and φ distributions are combined in a two-dimensional histogram with the angular axis denoting θ and the radial axis denoting φ (Figure 10B). The θ distribution is not uniform and there are clusters of increased frequency, in this representation in the southwestern hemisphere with a maximum around θ= 220°. This in turn means that a comparatively large amount of leaf parts are facing Southwest. In the following we checked the consistency of results with respect to genetic and environmental conditions. The setup of the Central Experiment of Campus Klein-Altendorf displays four major factors with potential influence on the phenotype, namely state, genotypic, site, and fertilization effects. Most obvious are the strong morphological changes of all sugar beet cultivars during seasonal development (state effects), while variety-specific differences become more apparent the older the leaves are (genotypic effects). We expected less impact from site (repetitions) and from fertilization (nitrogen treatment) effects, which is why we averaged over these two factors in Figure 10C, displaying the distributions of all state-cultivar combinations. Looking at the state effects one observes a cultivar-independent preferred direction in θ (also illustrated by the averages on the right) which changes distinctly over the season especially from the youngest to the older states. The center of the azimuth distribution, which is the median of the distribution, is shifted from Southwest ( 217°) to North ( 354° and 4°). We do not know yet the exact cause for this effect, but there are several reasons, that have to be taken into account: (i) Strong west winds at the location of our study site at Campus Klein-Altendorf may affect the leaf orientation and plants may react to windy conditions differently according to their susceptibility (leaf size or closeness of the canopy due to the sawing pattern); (ii) sugar beets are sown in rows from east to west with a row distance of 50 cm and a plant distance of 20 cm within the rows. Plants in the youngest state s do not have any contact to their neighbors. Plants in the intermediate state m have contact to the plants within their rows, plants in the oldest state l also across the rows. Therefore, young plants with small leaves and no neighborhood competition may orientate their leaves most efficiently to the south, while in older plants this deviation from the optimal distribution may be caused by a combination of several factors. Concerning the distribution of φ the results are rather homogeneous, however differences between states and cultivars are present, particularly in the shape of the distributions. The width of the φ distribution for instance differs strongly between Pauletta and Berenika in all states being much broader in the case of Pauletta and also the youngest states display lower average φ angles with medians of 1° compared to the older states with 2° and 5°. Again, this finding may result from a combination of different effects: (i) leaf stages differ distinctly between plants at younger or older states and (ii) leaf erection as a result of neighborhood interaction. Young plants have enough space to adjust their leaves optimally for light interception, while older plants have to compete for light on the one hand and are featured by very large and heavy leaves with planophile leaf sections on the other.Figure 10


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)

Leaf angle distribution of a sugar beet sample. Leaf angle distributions of sugar beet populations: (A) frequency of zenith angles; (B) combined zenith and azimuth angles in a 2-d histogram plot for a single image of Berenika (May 30/ N+), the example given in Figure 3. Curvature flow smoothing has been applied on the 3-d data. In this representation the azimuth angles θ= 0°, 90°, 180° and 270° correspond to the cardinal points North, East, South and West. (C) compares state effects (date of measurement) and cultivar effects, each diagram representing the average of 2 repetitions and 2 different nitrogen treatments: As indicated by the overall averages (right), the growth state differences are most obviously reflected in the location and center of the azimuth distribution. (All images have been generated with an extra visualization tool).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig10: Leaf angle distribution of a sugar beet sample. Leaf angle distributions of sugar beet populations: (A) frequency of zenith angles; (B) combined zenith and azimuth angles in a 2-d histogram plot for a single image of Berenika (May 30/ N+), the example given in Figure 3. Curvature flow smoothing has been applied on the 3-d data. In this representation the azimuth angles θ= 0°, 90°, 180° and 270° correspond to the cardinal points North, East, South and West. (C) compares state effects (date of measurement) and cultivar effects, each diagram representing the average of 2 repetitions and 2 different nitrogen treatments: As indicated by the overall averages (right), the growth state differences are most obviously reflected in the location and center of the azimuth distribution. (All images have been generated with an extra visualization tool).
Mentions: The orientation of each face (as given by the face normal) within the reconstructed meshes can easily be converted into azimuth (θ) and zenith (φ) angles and - if related to the individual face area - summed up to the distribution of leaf angles. Distributions are calculated as normalized histograms with 1° bin width, if not stated differently. The interpretation may be carried out on the basis of φ or θ angle distributions separately or as joint distributions, assuming that there is a dependency between both distributions. (i) Figure 10A shows the distribution of φ for Berenika (May 30/ N+). Further statistics measures like the mean or median of the φ distribution may help to characterize drought stress, diseases or diurnal leaf movements. The shape of the φ distribution displayed characteristic differences between the cultivars, which were distinctly increased in the older developmental states, while for the θ angle distributions we recorded seasonal differences particularly reflected in the location of the angle averages (data not shown). (ii) On a higher level θ and φ distributions are combined in a two-dimensional histogram with the angular axis denoting θ and the radial axis denoting φ (Figure 10B). The θ distribution is not uniform and there are clusters of increased frequency, in this representation in the southwestern hemisphere with a maximum around θ= 220°. This in turn means that a comparatively large amount of leaf parts are facing Southwest. In the following we checked the consistency of results with respect to genetic and environmental conditions. The setup of the Central Experiment of Campus Klein-Altendorf displays four major factors with potential influence on the phenotype, namely state, genotypic, site, and fertilization effects. Most obvious are the strong morphological changes of all sugar beet cultivars during seasonal development (state effects), while variety-specific differences become more apparent the older the leaves are (genotypic effects). We expected less impact from site (repetitions) and from fertilization (nitrogen treatment) effects, which is why we averaged over these two factors in Figure 10C, displaying the distributions of all state-cultivar combinations. Looking at the state effects one observes a cultivar-independent preferred direction in θ (also illustrated by the averages on the right) which changes distinctly over the season especially from the youngest to the older states. The center of the azimuth distribution, which is the median of the distribution, is shifted from Southwest ( 217°) to North ( 354° and 4°). We do not know yet the exact cause for this effect, but there are several reasons, that have to be taken into account: (i) Strong west winds at the location of our study site at Campus Klein-Altendorf may affect the leaf orientation and plants may react to windy conditions differently according to their susceptibility (leaf size or closeness of the canopy due to the sawing pattern); (ii) sugar beets are sown in rows from east to west with a row distance of 50 cm and a plant distance of 20 cm within the rows. Plants in the youngest state s do not have any contact to their neighbors. Plants in the intermediate state m have contact to the plants within their rows, plants in the oldest state l also across the rows. Therefore, young plants with small leaves and no neighborhood competition may orientate their leaves most efficiently to the south, while in older plants this deviation from the optimal distribution may be caused by a combination of several factors. Concerning the distribution of φ the results are rather homogeneous, however differences between states and cultivars are present, particularly in the shape of the distributions. The width of the φ distribution for instance differs strongly between Pauletta and Berenika in all states being much broader in the case of Pauletta and also the youngest states display lower average φ angles with medians of 1° compared to the older states with 2° and 5°. Again, this finding may result from a combination of different effects: (i) leaf stages differ distinctly between plants at younger or older states and (ii) leaf erection as a result of neighborhood interaction. Young plants have enough space to adjust their leaves optimally for light interception, while older plants have to compete for light on the one hand and are featured by very large and heavy leaves with planophile leaf sections on the other.Figure 10

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.