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

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.

[1]  Sebastian Riedel,et al.  Automated Analysis of Barley Organs Using 3D Laser Scanning: An Approach for High Throughput Phenotyping , 2014, Sensors.

[2]  George Karypis,et al.  Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering , 2004, Machine Learning.

[3]  Nico Blodow,et al.  Towards 3D Point cloud based object maps for household environments , 2008, Robotics Auton. Syst..

[4]  Nico Blodow,et al.  Fast Point Feature Histograms (FPFH) for 3D registration , 2009, 2009 IEEE International Conference on Robotics and Automation.

[5]  Witold F. Krajewski,et al.  Three-dimensional digital model of a maize plant. , 2010 .

[6]  Ling Huang,et al.  Fast approximate spectral clustering , 2009, KDD.

[7]  Christian Bauckhage,et al.  Convex non-negative matrix factorization for massive datasets , 2011, Knowledge and Information Systems.

[8]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[9]  J. Aitchison.  On criteria for measures of compositional difference , 1992 .

[10]  Sanjoy Dasgupta,et al.  Random projection trees and low dimensional manifolds , 2008, STOC.

[11]  Anne-Katrin Mahlein,et al.  Fusion of sensor data for the detection and differentiation of plant diseases in cucumber , 2014 .

[12]  V. Pawlowsky-Glahn,et al.  Dealing with Zeros and Missing Values in Compositional Data Sets Using Nonparametric Imputation , 2003 .

[13]  Christian Bauckhage,et al.  Metro Maps of Plant Disease Dynamics—Automated Mining of Differences Using Hyperspectral Images , 2015, PloS one.

[14]  G. Mateu-Figueras,et al.  Isometric Logratio Transformations for Compositional Data Analysis , 2003 .

[15]  Julio Gonzalo,et al.  A comparison of extrinsic clustering evaluation metrics based on formal constraints , 2009, Information Retrieval.

[16]  P. Benfey,et al.  Advanced imaging techniques for the study of plant growth and development. , 2014, Trends in plant science.

[17]  Armin B. Cremers,et al.  Learning to hash logistic regression for fast 3D scan point classification , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Christian Bauckhage,et al.  Hierarchical Convex NMF for Clustering Massive Data , 2010, ACML.

[19]  Christopher D. Manning,et al.  Introduction to Information Retrieval , 2010, J. Assoc. Inf. Sci. Technol..

[20]  Anne-Katrin Mahlein,et al.  Recent advances in sensing plant diseases for precision crop protection , 2012, European Journal of Plant Pathology.

[21]  M. Tester,et al.  Phenomics--technologies to relieve the phenotyping bottleneck. , 2011, Trends in plant science.

[22]  J. Aitchison,et al.  Logratio Analysis and Compositional Distance , 2000 .

[23]  J. Fripp,et al.  A novel mesh processing based technique for 3D plant analysis , 2012, BMC Plant Biology.

[24]  Eric Wahl,et al.  Surflet-pair-relation histograms: a statistical 3D-shape representation for rapid classification , 2003, Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings..

[25]  U. Rascher,et al.  Imaging plants dynamics in heterogenic environments. , 2012, Current opinion in biotechnology.

[26]  Lutz Plümer,et al.  A Multi-Resolution Approach for an Automated Fusion of Different Low-Cost 3D Sensors , 2014, Sensors.

[27]  Igor Vajda,et al.  On Metric Divergences of Probability Measures , 2009, Kybernetika.

[28]  Nico Blodow,et al.  Fast geometric point labeling using conditional random fields , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[29]  Christian Bauckhage,et al.  Descriptive matrix factorization for sustainability Adopting the principle of opposites , 2011, Data Mining and Knowledge Discovery.

[30]  Arnold W. M. Smeulders,et al.  Active learning using pre-clustering , 2004, ICML.

[31]  H. Bleiholder,et al.  Growth Stages of the Grapevine: Phenological growth stages of the grapevine (Vitis vinifera L. ssp. vinifera)—Codes and descriptions according to the extended BBCH scale† , 1995 .

[32]  John Aitchison,et al.  The Statistical Analysis of Compositional Data , 1986 .

[33]  J. Léon,et al.  High-precision laser scanning system for capturing 3D plant architecture and analysing growth of cereal plants , 2014 .

[34]  Nico Blodow,et al.  Persistent Point Feature Histograms for 3D Point Clouds , 2008 .

[35]  Heiner Kuhlmann,et al.  Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping , 2013, BMC Bioinformatics.

[36]  Mohammed Bennamoun,et al.  A Comprehensive Performance Evaluation of 3D Local Feature Descriptors , 2015, International Journal of Computer Vision.

[37]  Kristian Kersting,et al.  Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions , 2015, Plant Methods.

[38]  Andrew McGregor,et al.  Finding Metric Structure in Information Theoretic Clustering , 2008, COLT.

[39]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.