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

BMC Bioinformatics

Mirwaes Wahabzada³  Stefan Paulus¹  Kristian Kersting²  Anne-Katrin Mahlein³ 

[1] IGG-Geodesy, University of Bonn, Nussallee 17, Bonn 53115, Germany

[2] Computer Science Department, TU Dortmund University, Otto-Hahn-Str. 14, Dortmund 44227, Germany

[3] INRES-Phytomedicine, University of Bonn, Meckenheimer Allee 166a, Bonn 53115, Germany

关键词: Plant phenotyping;    High-throughput;    3D-laserscanning;    Clustering;    Automatic segmentation;   

Others  :  1230253
DOI  :  10.1186/s12859-015-0665-2

 received in 2015-01-28, accepted in 2015-07-08,  发布年份 2015

【 摘 要 】

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.

【 授权许可】

   
2015 Wahabzada et al.

【 图 表 】

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