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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.


Error estimation. Surface zenith angles of artificial setups were measured with an inclinometer and compared to respective estimated leaf angles from stereo reconstruction. Estimation errors were quantified by the normalized root mean square error (NRMSE): artificial plant leaves (red) displayed a NRMSE of 2.5%; planarly-fixed sugar beet leaf in various orientations (blue) displayed a NRMSE of 4.6%. The dashed line indicates where angle estimates are known to be unreliable [27].
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Fig8: Error estimation. Surface zenith angles of artificial setups were measured with an inclinometer and compared to respective estimated leaf angles from stereo reconstruction. Estimation errors were quantified by the normalized root mean square error (NRMSE): artificial plant leaves (red) displayed a NRMSE of 2.5%; planarly-fixed sugar beet leaf in various orientations (blue) displayed a NRMSE of 4.6%. The dashed line indicates where angle estimates are known to be unreliable [27].

Mentions: We tested the accuracy of leaf angle estimation in two experiments. The first one uses an artificial plant with 8 green-colored flat leaves made form plywood which can be adjusted to any zenith angle. The second one employs a sugar beet leaf fixed on a flat surface, which could be oriented arbitrarily. Targets were imaged from nadir position (3.5 m distance) with two Canon EOS 5D Mark II (f=50 mm; b≈200 mm). We set the leaves of the artificial plant target to different zenith angles, such that most parts of each leaf were in camera view. Individual leaf angles were manually measured using a high resolution dual-axis digital inclinometer (Level Developments LD-2M). Inclination of the sugar beet leaf was manually changed and measured between each imaging step. Here we applied 7 different orientations. Images were processed using the target calibration pipeline. Leaves were segmented and fitted with a planar surface model. Figure 8 summarizes results for both tests. The deviation from the identity line was quantified for the accessible zenith angle interval [0°, 70°]. Steeper leaf parts are not well visible and thus do not give reliable angle estimates [27]. The normalized root mean square error (NRMSE) is approx. 2.5% for the artificial plant and approx. 4.6% for the fixed leaf. Moreover, we computed the sugar beet leaf area of all orientations and estimated the error using the normalized variation coefficient, which is approx. 2.8%. We do not observe a bias towards fronto-parallel surfaces well known for other stereo reconstruction approaches [61].Figure 8


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)

Error estimation. Surface zenith angles of artificial setups were measured with an inclinometer and compared to respective estimated leaf angles from stereo reconstruction. Estimation errors were quantified by the normalized root mean square error (NRMSE): artificial plant leaves (red) displayed a NRMSE of 2.5%; planarly-fixed sugar beet leaf in various orientations (blue) displayed a NRMSE of 4.6%. The dashed line indicates where angle estimates are known to be unreliable [27].
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig8: Error estimation. Surface zenith angles of artificial setups were measured with an inclinometer and compared to respective estimated leaf angles from stereo reconstruction. Estimation errors were quantified by the normalized root mean square error (NRMSE): artificial plant leaves (red) displayed a NRMSE of 2.5%; planarly-fixed sugar beet leaf in various orientations (blue) displayed a NRMSE of 4.6%. The dashed line indicates where angle estimates are known to be unreliable [27].
Mentions: We tested the accuracy of leaf angle estimation in two experiments. The first one uses an artificial plant with 8 green-colored flat leaves made form plywood which can be adjusted to any zenith angle. The second one employs a sugar beet leaf fixed on a flat surface, which could be oriented arbitrarily. Targets were imaged from nadir position (3.5 m distance) with two Canon EOS 5D Mark II (f=50 mm; b≈200 mm). We set the leaves of the artificial plant target to different zenith angles, such that most parts of each leaf were in camera view. Individual leaf angles were manually measured using a high resolution dual-axis digital inclinometer (Level Developments LD-2M). Inclination of the sugar beet leaf was manually changed and measured between each imaging step. Here we applied 7 different orientations. Images were processed using the target calibration pipeline. Leaves were segmented and fitted with a planar surface model. Figure 8 summarizes results for both tests. The deviation from the identity line was quantified for the accessible zenith angle interval [0°, 70°]. Steeper leaf parts are not well visible and thus do not give reliable angle estimates [27]. The normalized root mean square error (NRMSE) is approx. 2.5% for the artificial plant and approx. 4.6% for the fixed leaf. Moreover, we computed the sugar beet leaf area of all orientations and estimated the error using the normalized variation coefficient, which is approx. 2.8%. We do not observe a bias towards fronto-parallel surfaces well known for other stereo reconstruction approaches [61].Figure 8

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.