Abstract
BackgroundPlant 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.ResultsThe 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.ConclusionAn 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0665-2) contains supplementary material, which is available to authorized users.
Highlights
Plant organ segmentation from 3D point clouds is a relevant task for plant phenotyping and plant growth observation
As the histogram representation is influenced by the points neighborhood, it makes the application of algorithms such as Support Vector Machines (SVM)’s possible in general
Hellinger distance To arrive at an automated clustering approach for histograms, we propose to transform the data before computing similarites/differences between feature point histograms
Summary
Plant organ segmentation from 3D point clouds is a relevant task for plant phenotyping and plant growth observation. Methods that can be subordinated under collective classification approaches take the surrounding information of a point into account They often rely on complex algorithms, are time consuming, and much research has gone into the direction making them more efficient (see [12] and references). One way for identification and segmentation of plant organs without time and labor intensive preprocessing are surface feature histograms As it has been shown before in Paulus et al [8], they are an innovative and suitable method for plant organ parametrization from 3D data. The reason why plants organs lead to specific feature histograms and provide a good separation is that leaf and stem very well correspond to primitives like plane or cylinders, for example It has been previously shown, that this method is independent to the point to point distance and applicable to multiple plants. A fully automated data analysis cascade is missing but highly desirable, to save the time and cost for manual labelling the training data by skilled operators
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