Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping

BMC Bioinformatics

Stefan Paulus²  Jan Dupuis²  Anne-Katrin Mahlein¹  Heiner Kuhlmann² 

[1] Institute for Crop Science and Resource Conservation (INRES) - Phytomedicine, University of Bonn, Nussallee 9, 53115 Bonn, Germany

[2] Institute of Geodesy and Geoinformation - Professorship of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany

关键词: Grapevine;    Wheat;    High throughput;    Plant phenotyping;    Automatic classification;    Surface feature histogram;    3D-laserscanning;   

Others  :  1087798
DOI  :  10.1186/1471-2105-14-238

 received in 2013-01-18, accepted in 2013-07-21,  发布年份 2013

【 摘 要 】

Background

Laserscanning recently has become a powerful and common method for plant parameterization and plant growth observation on nearly every scale range. However, 3D measurements with high accuracy, spatial resolution and speed result in a multitude of points that require processing and analysis. The primary objective of this research has been to establish a reliable and fast technique for high throughput phenotyping using differentiation, segmentation and classification of single plants by a fully automated system. In this report, we introduce a technique for automated classification of point clouds of plants and present the applicability for plant parameterization.

Results

A surface feature histogram based approach from the field of robotics was adapted to close-up laserscans of plants. Local geometric point features describe class characteristics, which were used to distinguish among different plant organs. This approach has been proven and tested on several plant species. Grapevine stems and leaves were classified with an accuracy of up to 98%. The proposed method was successfully transferred to 3D-laserscans of wheat plants for yield estimation. Wheat ears were separated with an accuracy of 96% from other plant organs. Subsequently, the ear volume was calculated and correlated to the ear weight, the kernel weights and the number of kernels. Furthermore the impact of the data resolution was evaluated considering point to point distances between 0.3 and 4.0 mm with respect to the classification accuracy.

Conclusion

We introduced an approach using surface feature histograms for automated plant organ parameterization. Highly reliable classification results of about 96% for the separation of grapevine and wheat organs have been obtained. This approach was found to be independent of the point to point distance and applicable to multiple plant species. Its reliability, flexibility and its high order of automation make this method well suited for the demands of high throughput phenotyping.

Highlights

• Automatic classification of plant organs using geometrical surface information

• Transfer of analysis methods for low resolution point clouds to close-up laser measurements of plants

• Analysis of 3D-data requirements for automated plant organ classification

【 授权许可】

   
2013 Paulus et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

【 参考文献 】

• [1]Paproki A, Fripp J, Salvado O, Sirault X, Berry S, Furbank R: Automated 3D segmentation and analysis of cotton plants. In DICTA. Noosa QLD, Australia, IEEE; 2011:555-560.
• [2]Setter T: Analysis of constituents for phenotyping drought tolerance in crop improvement. Front Plant Physiol 2012, 3:1-12.
• [3]Furbank RT, Tester M: Phenomics–technologies to relieve the phenotyping bottleneck. Trends Plant Sci 2011, 16(12):635-44.
• [4]Mahlein AK, Oerke EC, Steiner U, Dehne HW: Recent advances in sensing plant diseases. Eur J Plant Pathol 2012, 133:197-209.
• [5]Schurr U, Heckenberger U, Herdel K, Walter A Feil: Leaf development in Ricinus communis during drought stress: dynamics of growth processes, of cellular structure and of sink-source transition. J Exp Bot 2000, 51(350):1515-1529.
• [6]Frasson RPdM, Krajewski WF: Three-dimensional digital model of a maize plant. Agric Forest Meteorol 2010, 150:478-488.
• [7]Dornbusch T, Wernecke P, Diepenbrock W: A method to extract morphological traits of plant organs from 3D point clouds as a database for an architectural plant model. Ecol Model 2007, 200(1–2):119-129.
• [8]Omasa K, Hosoi F, Konishi A: 3D lidar imaging for detecting and understanding plant responses and canopy structure. J Exp Bot 2007, 58(4):881-898.
• [9]Chambelland JC, Dassot M, Adam B, Donès N, Balandier P, Marquier A, Saudreau M, Sonohat G, Sinoquet H: A double-digitising method for building 3D virtual trees with non-planar leaves: application to the morphology and light-capture properties of young beech trees (Fagus sylvatica). Funct Plant Biol 2008, 35(10):1059-1069.
• [10]Hosoi F, Nakabayashi K, Omasa K: 3-D modeling of tomato canopies using a high-resolution portable scanning lidar for extracting structural information. Sensors 2011, 11(2):2166-2174.
• [11]Gärtner H, Wagner B, Heinrich I, Denier C: 3D-laser scanning : a new method to analyze coarse tree root. ISSR Symp Root Res Appli 2009, 106:95-106.
• [12]Beder C, Förstner W: Direct solutions for computing cylinders from minimal sets of 3d points. In Proceedings of the 9th European conference on Computer Vision - Volume Part I,. ECCV’06, Berlin, Heidelberg: Springer-Verlag; 2006:135-146.
• [13]Rabbani T, Van DenHeuvel F: Efficient hough transform for automatic detection of cylinders in point clouds. Proc ISPRS Workshop Laser Scan 2005, ISPRS Arch 2005, 36:60-65.
• [14]Paproki A, Sirault X, Berry S, Furbank R, Fripp J: A novel mesh processing based technique for 3D plant analysis. BMC Plant Biol 2012, 12:63. BioMed Central Full Text
• [15]Rusu RB, Marton ZC, Blodow N, Beetz M: Persistent point feature histograms for 3D point clouds. Proc 10th Int Conf Intel Autonomous Syst (IAS-10), Baden-Baden, Germany 2008,, 119-128.
• [16]Rusu RB, Blodow N, Beetz M: Fast point feature histograms (FPFH) for 3D registration. In Proc IEEE Int Conf Robot Automation (ICRA). Edited by Kazuhiro Kosuge, Katsushi Ikeuchi Kobe. Japan 2009; 3212-3217.
• [17]Rusu RB, Bradski G, Thibaux R, Hsu J: Fast 3D recognition and pose using the viewpoint feature histogram. In Proceedings of the 23rd IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),. Edited by Institute of Electrical and Electronics Engineers (IEEE). Taipei, Taiwan 2010; 2155-2162.
• [18]Rusu RB, Marton ZC, Blodow N, Dolha M, Beetz M: Towards 3D point cloud based object maps for household environments. Robot Auton Syst 2008, 56(11):927-941.
• [19]Rusu RB, Holzbach A, Blodow N, Beetz M: Fast geometric point labeling using conditional random fields. In Proc 22nd IEEE/RSJ Int Conf Intel Robots Syst(IROS). Edited by Institute of Electrical and Electronics Engineers (IEEE). St. Louis, MO, USA 2009; 7-12.
• [20]Ma W, Xiang B, Zha H, Liu J, Zhang X: Modeling plants with sensor data. Sci China Series F - Inf Sci 2009, 52:500-511.
• [21]Quan L, Tan P, Zeng G, Yuan L, Wang J, Kang SB: Image-based plant modeling. ACN Trans Graph 2006, 25:599-604.
• [22]Ud-Din N, Carver BF, Krenzer EG: Visual selection for forage yield in winter wheat. Crop Sci 1993, 33:41-45.
• [23]Palloix A, van Eeuwijk F, Chris G, van der Heijden G: SPICY: towards automated phenotyping of large pepper plants in the greenhouse. Funct Plant Biol 2012, 39:870Ű877.
• [24]Alenyà G, Dellen B, Torras C: 3D modelling of leaves from color and ToF data for robotized plant measuring. ICRA 2011, 3408-3414.
• [25]Chéné Y, Rousseau D, Lucidarme P, Bertheloot J, Caffier V, Morel P, Belin ı, Chapeau-Blondeau F: On the use of depth camera for 3D phenotyping of entire plants. Comput Electron Agric 2012, 82:122-127.
• [26]Seitz S, Curless B, Diebel J, Scharstein D, Szeliski R: A comparison and evaluation of multi-view stereo reconstruction algorithms. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Edited by Institute of Electrical and Electronics Engineers (IEEE). New York, USA 2006; 519-528.
• [27]Rusu R, Blodow N, Marton Z, Beetz M: Aligning point cloud views using persistent feature histograms. In IEEE/RSJ Int Conf Intel Robots Syst 2008,. Edited by Institute of Electrical and Electronics Engineers (IEEE). Nice, France; 3384-3391.
• [28]Arvidsson S, Pérez-Rodríguez P, Mueller-Roeber B: A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects. New Phytologist 2011, 191(3):895-907.
• [29]Pieruschka R, Poorter H: Phenotyping plants: genes, phenes and machines. Funct Plant Biol 2012, 39:813Ű-820.
• [30]Vos J, Evers JB, Buck-Sorlin GH, Andrieu B, Chelle M, de Visser PHB: Functional-structural plant modelling: a new versatile tool in crop science. J Exp Botany 2010, 61(8):1-15. [http://jxb.oxfordjournals.org/content/61/8/2101 webcite]
• [31]Berdugo CA, Mahlein AK, Steiner U, Dehne HW, Oerke EC: Sensors and imaging techniques for the assessment of the delay of wheat senescence induced by fungicides. Functional Plant Biol 2013.
• [32]Vapnik NV: Statistical learning theory.
• [33]Mucherino A, Papajorgji P, Pardalos PM: A survey of data mining techniques applied to agriculture. Oper Res 2009, 9(2):121-140. [http://link.springer.com/article/10.1007%2Fs12351-009-0054-6 webcite]
• [34]Rumpf T, Mahlein AK, Steiner U, Oerke EC, Dehne HW, Plümer L: Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Comput Electron Agric 2010, 74:91-99.
• [35]Chang CC, Lin CJ: LIBSVM : a library for support vector machines. Trans Intel Syst Technol 2011, 27:1-27.