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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.


Field of applications. Depth maps and respective RGBs (inlay) of different experimental plant systems: Pixel disparities in the depth map are color-coded ranging from red (closer to cameras) to blue (further away); distance ranges are given in brackets: (A) trays of Arabidopsis thaliana were monitored in studies of diurnal leaf movement (≈20 mm); (B) single trees (apple orchards) were analyzed with respect to the leaf and fruit stratification; Klein-Altendorf, 2013 (≈700 mm); (C) small plot of sugar beet; case study from CROP.SENSe.net central experiment (Campus Klein-Altendorf); 2012, June 14 (≈500 mm); (D) small barley populations; Crop Garden experiment at FZ Juelich; 2011, July 5 (≈400 mm).
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Fig9: Field of applications. Depth maps and respective RGBs (inlay) of different experimental plant systems: Pixel disparities in the depth map are color-coded ranging from red (closer to cameras) to blue (further away); distance ranges are given in brackets: (A) trays of Arabidopsis thaliana were monitored in studies of diurnal leaf movement (≈20 mm); (B) single trees (apple orchards) were analyzed with respect to the leaf and fruit stratification; Klein-Altendorf, 2013 (≈700 mm); (C) small plot of sugar beet; case study from CROP.SENSe.net central experiment (Campus Klein-Altendorf); 2012, June 14 (≈500 mm); (D) small barley populations; Crop Garden experiment at FZ Juelich; 2011, July 5 (≈400 mm).

Mentions: Our stereo imaging approach has been tested with different plants demonstrating the functionality of our software across species and applications (Figure 9). Our test cases ranged from the small rosette plant Arabidopsis thaliana (Figure 9A) to single trees in apple orchards (Figure 9B) to the agricultural crops sugar beet and barley, which are the main focus species of the Crop.Sense.net network (Figure 9C, D). Currently further studies with Arabidopsis are on the way to better understand gene-phenotype interactions and with apple trees to assess fruit traits by 3-d stereo imaging (results will be published elsewhere). In this manuscript we focus on a detailed investigation of four different sugar beet varieties that were subjected to different nitrogen availability. We performed a detailed case study demonstrating the potential of our stereo approach to distinguish subtle seasonal, variety and treatment specific differences in leaf display.Figure 9


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)

Field of applications. Depth maps and respective RGBs (inlay) of different experimental plant systems: Pixel disparities in the depth map are color-coded ranging from red (closer to cameras) to blue (further away); distance ranges are given in brackets: (A) trays of Arabidopsis thaliana were monitored in studies of diurnal leaf movement (≈20 mm); (B) single trees (apple orchards) were analyzed with respect to the leaf and fruit stratification; Klein-Altendorf, 2013 (≈700 mm); (C) small plot of sugar beet; case study from CROP.SENSe.net central experiment (Campus Klein-Altendorf); 2012, June 14 (≈500 mm); (D) small barley populations; Crop Garden experiment at FZ Juelich; 2011, July 5 (≈400 mm).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig9: Field of applications. Depth maps and respective RGBs (inlay) of different experimental plant systems: Pixel disparities in the depth map are color-coded ranging from red (closer to cameras) to blue (further away); distance ranges are given in brackets: (A) trays of Arabidopsis thaliana were monitored in studies of diurnal leaf movement (≈20 mm); (B) single trees (apple orchards) were analyzed with respect to the leaf and fruit stratification; Klein-Altendorf, 2013 (≈700 mm); (C) small plot of sugar beet; case study from CROP.SENSe.net central experiment (Campus Klein-Altendorf); 2012, June 14 (≈500 mm); (D) small barley populations; Crop Garden experiment at FZ Juelich; 2011, July 5 (≈400 mm).
Mentions: Our stereo imaging approach has been tested with different plants demonstrating the functionality of our software across species and applications (Figure 9). Our test cases ranged from the small rosette plant Arabidopsis thaliana (Figure 9A) to single trees in apple orchards (Figure 9B) to the agricultural crops sugar beet and barley, which are the main focus species of the Crop.Sense.net network (Figure 9C, D). Currently further studies with Arabidopsis are on the way to better understand gene-phenotype interactions and with apple trees to assess fruit traits by 3-d stereo imaging (results will be published elsewhere). In this manuscript we focus on a detailed investigation of four different sugar beet varieties that were subjected to different nitrogen availability. We performed a detailed case study demonstrating the potential of our stereo approach to distinguish subtle seasonal, variety and treatment specific differences in leaf display.Figure 9

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.