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Automated interpretation of 3D laserscanned point clouds for plant organ segmentation.

Wahabzada M, Paulus S, Kersting K, Mahlein AK - BMC Bioinformatics (2015)

Bottom Line: We investigate the performance of the resulting approaches in the practical context of grouping 3D point clouds and demonstrate empirically that they lead to clustering results with high accuracy for monocotyledonous and dicotyledonous plant species with diverse shoot architecture.This approach is applicable to different plant species with high accuracy.The analysis cascade can be implemented in future high-throughput phenotyping scenarios and will support the evaluation of the performance of different plant genotypes exposed to stress or in different environmental scenarios.

View Article: PubMed Central - PubMed

Affiliation: INRES-Phytomedicine, University of Bonn, Meckenheimer Allee 166a, Bonn, 53115, Germany. mirwaes@uni-bonn.de.

ABSTRACT

Background: Plant organ segmentation from 3D point clouds is a relevant task for plant phenotyping and plant growth observation. Automated solutions are required to increase the efficiency of recent high-throughput plant phenotyping pipelines. However, plant geometrical properties vary with time, among observation scales and different plant types. The main objective of the present research is to develop a fully automated, fast and reliable data driven approach for plant organ segmentation.

Results: The automated segmentation of plant organs using unsupervised, clustering methods is crucial in cases where the goal is to get fast insights into the data or no labeled data is available or costly to achieve. For this we propose and compare data driven approaches that are easy-to-realize and make the use of standard algorithms possible. Since normalized histograms, acquired from 3D point clouds, can be seen as samples from a probability simplex, we propose to map the data from the simplex space into Euclidean space using Aitchisons log ratio transformation, or into the positive quadrant of the unit sphere using square root transformation. This, in turn, paves the way to a wide range of commonly used analysis techniques that are based on measuring the similarities between data points using Euclidean distance. We investigate the performance of the resulting approaches in the practical context of grouping 3D point clouds and demonstrate empirically that they lead to clustering results with high accuracy for monocotyledonous and dicotyledonous plant species with diverse shoot architecture.

Conclusion: An automated segmentation of 3D point clouds is demonstrated in the present work. Within seconds first insights into plant data can be deviated - even from non-labelled data. This approach is applicable to different plant species with high accuracy. The analysis cascade can be implemented in future high-throughput phenotyping scenarios and will support the evaluation of the performance of different plant genotypes exposed to stress or in different environmental scenarios.

No MeSH data available.


Related in: MedlinePlus

Example for clusterings of the grapevine dataset (berry and rachis) using Algorithm 1 and with different data mappings (k=3). For each cluster a subset of points are illustrated, containing the most histograms located on the rachis and parts containing the berry surface and the inner parts of the fruit
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Fig3: Example for clusterings of the grapevine dataset (berry and rachis) using Algorithm 1 and with different data mappings (k=3). For each cluster a subset of points are illustrated, containing the most histograms located on the rachis and parts containing the berry surface and the inner parts of the fruit

Mentions: In addition to the quantitative analysis, we report qualitative results achieved from all datasets. For that we additionally consider the clustering on the second grapevine dataset consisting of berry and rachis for which no manual annotations were available. The clusters achieved by using all three methods are illustrated in Fig. 3 for the grapevine dataset and in Fig. 4 for the barley dataset.Fig. 3


Automated interpretation of 3D laserscanned point clouds for plant organ segmentation.

Wahabzada M, Paulus S, Kersting K, Mahlein AK - BMC Bioinformatics (2015)

Example for clusterings of the grapevine dataset (berry and rachis) using Algorithm 1 and with different data mappings (k=3). For each cluster a subset of points are illustrated, containing the most histograms located on the rachis and parts containing the berry surface and the inner parts of the fruit
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4528849&req=5

Fig3: Example for clusterings of the grapevine dataset (berry and rachis) using Algorithm 1 and with different data mappings (k=3). For each cluster a subset of points are illustrated, containing the most histograms located on the rachis and parts containing the berry surface and the inner parts of the fruit
Mentions: In addition to the quantitative analysis, we report qualitative results achieved from all datasets. For that we additionally consider the clustering on the second grapevine dataset consisting of berry and rachis for which no manual annotations were available. The clusters achieved by using all three methods are illustrated in Fig. 3 for the grapevine dataset and in Fig. 4 for the barley dataset.Fig. 3

Bottom Line: We investigate the performance of the resulting approaches in the practical context of grouping 3D point clouds and demonstrate empirically that they lead to clustering results with high accuracy for monocotyledonous and dicotyledonous plant species with diverse shoot architecture.This approach is applicable to different plant species with high accuracy.The analysis cascade can be implemented in future high-throughput phenotyping scenarios and will support the evaluation of the performance of different plant genotypes exposed to stress or in different environmental scenarios.

View Article: PubMed Central - PubMed

Affiliation: INRES-Phytomedicine, University of Bonn, Meckenheimer Allee 166a, Bonn, 53115, Germany. mirwaes@uni-bonn.de.

ABSTRACT

Background: Plant organ segmentation from 3D point clouds is a relevant task for plant phenotyping and plant growth observation. Automated solutions are required to increase the efficiency of recent high-throughput plant phenotyping pipelines. However, plant geometrical properties vary with time, among observation scales and different plant types. The main objective of the present research is to develop a fully automated, fast and reliable data driven approach for plant organ segmentation.

Results: The automated segmentation of plant organs using unsupervised, clustering methods is crucial in cases where the goal is to get fast insights into the data or no labeled data is available or costly to achieve. For this we propose and compare data driven approaches that are easy-to-realize and make the use of standard algorithms possible. Since normalized histograms, acquired from 3D point clouds, can be seen as samples from a probability simplex, we propose to map the data from the simplex space into Euclidean space using Aitchisons log ratio transformation, or into the positive quadrant of the unit sphere using square root transformation. This, in turn, paves the way to a wide range of commonly used analysis techniques that are based on measuring the similarities between data points using Euclidean distance. We investigate the performance of the resulting approaches in the practical context of grouping 3D point clouds and demonstrate empirically that they lead to clustering results with high accuracy for monocotyledonous and dicotyledonous plant species with diverse shoot architecture.

Conclusion: An automated segmentation of 3D point clouds is demonstrated in the present work. Within seconds first insights into plant data can be deviated - even from non-labelled data. This approach is applicable to different plant species with high accuracy. The analysis cascade can be implemented in future high-throughput phenotyping scenarios and will support the evaluation of the performance of different plant genotypes exposed to stress or in different environmental scenarios.

No MeSH data available.


Related in: MedlinePlus