Automated analysis of barley organs using 3D laser scanning: An approach for high throughput phenotyping

Sensors (Switzerland)

Volumn 14, Issue 7, 2014, Pages 12670-12686

  Paulus, Stefan ^a   Dupuis, Jan ^a   Riedel, Sebastian ^a   Kuhlmann, Heiner ^a  

^a UNIVERSITY OF BONN   (Germany)

Author keywords

Automatic classification; Automatic parameterization; Barley; Laser scanning; Plant phenotyping

Indexed keywords

FUNCTIONAL GROUPS; GRAPHIC METHODS; LASER APPLICATIONS; LEAST SQUARES APPROXIMATIONS; PARAMETERIZATION; PLANTS (BOTANY); STATISTICAL METHODS; SURFACE ANALYSIS;

AUTOMATED ANALYSIS; AUTOMATED INTERPRETATION; AUTOMATIC CLASSIFICATION; BARLEY; CLASSIFICATION ALGORITHM; HIGH-THROUGHPUT PHENOTYPING; LASER SCANNING; PLANT PHENOTYPING;

APPROXIMATION ALGORITHMS;

EID: 84904437920     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s140712670     Document Type: Article

References (51)

1. 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, 2166-2174.
2. Omasa, K.; Hosoi, F.; Konishi, A. 3-D LiDAR imaging for detecting and understanding plant responses and canopy structure. J. Exp. Bot. 2007, 58, 881-898.
3. Hartmann, A.; Czauderna, T.; Hoffmann, R.; Stein, N.; Schreiber, F. HTPheno: An image analysis pipeline for high-throughput plant phenotyping. BMC Bioinforma. 2011, 12, 148.
4. Paulus, S.; Behmann, J.; Mahlein, A. K.; Plümer, L.; Kuhlmann, H. Low-cost 3D systems-Well suited tools for plant phenotyping. Sensors 2014, 14, 3001-3018.
5. Sanz-Cortiella, R.; Llorens-Calveras, J.; Escolà, A.; Arnó-Satorra, J.; Ribes-Dasi, M.; Masip-Vilalta, J.; Camp, F.; Gràcia-Aguilá, F.; Solanelles-Batlle, F.; Planas-DeMartí, S.; et al. Innovative LIDAR 3D Dynamic Measurement System to Estimate Fruit-Tree Leaf Area. Sensors 2011, 11, 5769-5791.
6. Van der Heijden, G.; Song, Y.; Horgan, G.; Polder, G.; Dieleman, A.; Bink, M.; Palloix, A.; van Eeuwijk, F.; Glasbey, C. SPICY: Towards automated phenotyping of large pepper plants in the greenhouse. Funct. Plant Biol. 2012, 39, 870-877.
7. Paulus, S.; Schumann, H.; Leon, J.; Kuhlmann, H. A high precision laser scanning system for capturing 3D plant architecture and analysing growth of cereal plants. Biosyst. Eng. 2014, 121, 1-11.
8. Furbank, R.; Tester, M. Phenomics-technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 2011, 16, 635-644.
9. Dhondt, S.; Wuyts, N.; Inzé, D. Cell to whole-plant phenotyping: The best is yet to come. Trends Plant Sci. 2013, 18, 1-12.
10. Wagner, B.; Santini, S.; Ingensand, H.; Gärtner, H. A tool to model 3D coarse-root development with annual resolution. Plant Soil 2011, 346, 79-96.
11. Keightley, K.; Bawden, G. 3D volumetric modeling of grapevine biomass using Tripod LiDAR. Comput. Electron. Agric. 2010, 74, 305-312.
12. Paulus, S.; Dupuis, J.; Mahlein, A.; Kuhlmann, H. Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping. BMC Bioinforma. 2013, 14, 238.
13. Munns, R.; James, R.; Sirault, X.; Furbank, R.; Jones, H. New phenotyping methods for screening wheat and barley for beneficial responses to water deficit. J. Exp. Bot. 2010, 61, 3499-3507.
14. Frasson, R.; Krajewski, W. Three-dimensional digital model of a maize plant. Agric. For. Meteorol. 2010, 150, 478-488.
15. 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.
16. Evers, J.; Vos, J.; Yin, X.; Romero, P.; van der Putten, P.; Struik, P. Simulation of wheat growth and development based on organ-level photosynthesis and assimilate allocation. J. Exp. Bot. 2010, 61, 2203-2216.
17. Thornley, J. Modelling Stem Height and Diameter Growth in Plants. Ann. Bot. 1999, 84, 195-205.
18. Rui, Y. K.; Peng, Y. F.; Wang, Z. R.; Shen, J. B. Stem perimeter, height and biomass of maize (Zea mays L.) grown under different N fertilization regimes in Beijing, China. Int. J. Plant Prod. 2009, 3, 85-90.
19. Araus, J. L.; Cairns, J. E. Field high-throughput phenotyping: The new crop breeding frontier. Trends Plant Sci. 2014, 19, 52-61.
20. Fanourakis, D.; Briese, C.; Max, J. F.; Kleinen, S.; Putz, A.; Fiorani, F.; Ulbrich, A.; Schurr, U. Rapid determination of leaf area and plant height by using light curtain arrays in four species with contrasting shoot architecture. Plant Methods 2014, 10, 9.
21. Xu, L.; Henke, M.; Zhu, J.; Kurth, W.; Buck-Sorlin, G. A functional-structural model of rice linking quantitative genetic information with morphological development and physiological processes. Ann. Bot. 2011, 107, 817-828.
22. Buck-Sorlin, G.; Hemmerling, R.; Kniemeyer, O.; Burema, B.; Kurth, W. A Rule-Based Model of Barley Morphogenesis, with Special Respect to Shading and Gibberellic Acid Signal Transduction. Ann. Bot. 2008, 101, 1109-1123.
23. Bastet, J.; Müller, J.; Christen, O. LEAFC3-N: Modeling effects of drought stress on photosynthesis, stomatal conductance and transpiration. In Proceedings of the 7th International Conference on Functional-Structural Plant Models, Saariselkä, Finland, 9-14 June 2013.
24. Bohmann, A.; Claus, J.; Chavarria-Krauser, A. Modelling transport processes in tissues and organs at a mesoscopic scale. In Proceedings of the 7th International Conference on Functional-Structural Plant Models, Saariselkä, Finland, 9-14 June 2013.
25. Le Gall, J.; Autret, H.; Combes, D.; Renaud, C.; Barthloot, J.; Leduc, N.; Andrieu, B.; Guérin, V.; Chelle, M.; Demotes-Mainard, S. Evaluation of a photon tracing model and virtual plants to simulate light distribution within a canopy in a growth chamber. In Proceedings of the 7th International Conference on Functional-Structural Plant Models, Saariselkä, Finland, 9-14 June 2013.
26. Vapnik, N. V. Statistical Learning Theory; Wiley: New York, NY, USA, 1998.
27. Technical Data Perceptron ScanWorks V5 for Romer. Available online: http://www. hexagon metrology. com. ar/en/perceptron-scanworks-v5-for-romer_275. htm#. U6kXKCeZvYM (accessed on 2 February 2014).
28. Hexagon Metrology GmbH. Technical Data Romer Infinite 2. 0. Available online: http://www. hexa gonmetrology. de/ROMER-Absolute-Arm_860. htm (accessed on 2 February 2014).
29. Dupuis, J.; Paulus, S.; Behmann, J.; Plümer, L.; Kuhlmann, H. A Multi-Resolution Approach for an Automated Fusion of Different Low-Cost 3D Sensors. Sensors 2014, 14, 7563-7579.
30. Rusu, R.; Marton, Z.; Blodow, N.; Beetz, M. Learning informative point classes for the acquisition of object model maps. In Proceedings of 2008 10th International Conference on Control, Automation, Robotics and Vision, Hanoi, Vietnam, 17-20 December 2008; pp. 643-650.
31. Chang, C.; Lin, C. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2011, 27, 1-27.
32. Scholkopf, B.; Smola, A. J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond; MIT Press: Cambridge, MA, USA, 2001.
33. Weber, C.; Hahmann, S.; Hagen, H. Sharp Feature Detection in Point Clouds. In Proceedings of the 2010 Shape Modeling International Conference, Aix-en-Provence, France, 21-23 June 2010; pp. 175-187.
34. Wiedenbeck, M.; Züll, C. Clusteranalyse. In Handbuch der Sozialwissenschaftlichen Datenanalyse; Wolf, C., Best, H., Eds.; VS Verlag für Sozialwissenschaften: Wiesbaden, Germany, 2010; pp. 525-552. (In German).
35. Neimeier, W. Ausgleichungsrechnung; Walter de Gruyter: Berlin, German, 2002. (In German).
36. Pfeifer, N.; Gorte, B.; Winterhalder, D. Automatic reconstruction of single trees from terrestrial laser scanner data. In Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Istanbul, Turkey, 12-23 July 2004; pp. 114-119.
37. Mikhail, E.; Ackermann, F. Observations and Least Squares; IEP: New York, NY, USA, 1976.
38. Fischler, M.; Bolles, R. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Commun. ACM 1981, 24, 381-395.
39. Paulus, S.; Eichert, T.; Goldbach, H.; Kuhlmann, H. Limits of active laser triangulation as an instrument for high precision plant imaging. Sensors 2014, 14, 2489-2509.
40. Liang, X.; Litkey, P.; Hyppä, J.; Kaartinen, H.; Vastaranta, M.; Holopainen, M. Automatic Stem Mapping Using Single Scan Terrestrial Laser Scanning. IEEE Trans. Geosci. Remote Sens. 2012, 50, 661-670.
41. Liang, X.; Hyppä, J. Automatic Stem Mapping by Merging Several Terrestrial Laser Scans at the Feature and Decision Levels. Sensors 2013, 13, 1614-1634.
42. Houle, D.; Govindaraju, D. R.; Omholt, S. Phenomics: The next challenge. Nat. Rev. 2010, 11, 855-866.
43. Fiorani, F.; Schurr, U. Future Scenarios for Plant Phenotyping. Ann. Rev. Plant Biol. 2013, 64, 267-291.
44. Bloomenthal, J. Modeling the Mighty Maple. In Proceedings of the 12th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH'85), San Francisco, CA, USA, July 1985; pp. 305-311.
45. Berdugo, C.; Zito, R.; Paulus, S.; Mahlein, A. Fusion of sensor data for the detection and differentiation of plant diseases in cucumber. Plant Pathol. 2014, in press.
46. Vos, J.; Evers, J.; Buck-Sorlin, G.; Andrieu, B.; Chelle, M.; de Visser, P. Functional-structural plant modeling: A new versatile tool in crop science. J. Exp. Bot. 2010, 61, 2101-2115.
47. Uhrmann, F.; Hügel, C.; Paris, S.; Scholz, O.; Zollhöfer, M.; Greiner, G. A Model-Based Approach to Extract Leaf Features from 3D Scans. In Proceedings of the 7th International Conference on Functional-Structural Plant Models, Saariselkä, Finland, 9-14 June 2013; pp. 115-118.
48. Polani, D.; Kim, J.; Martinetz, T. L-System Model of the Vegetative Growth of Winter Barley (Hordeum Vulgare L.). In Fifth German Workshop on Artificial Life: Abstracting and Synthesizing the Principles of Living Systems; Aka GmbH: Lübeck, Germany, 2002.
49. Praba, M. L.; Cairns, J. E.; Babu, R. C.; Lafitte, H. R. Identification of physiological traits underlying cultivar differences in drought tolerance in rice and wheat. J. Agron. Crop Sci. 2009, 195, 30-46.
50. Seitz, S.; Curless, B.; Diebel, J.; Scharstein, D.; Szeliski, R. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms. In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), New York, NY, USA, 17-22 June 2006; pp. 519-528.
51. Biskup, B.; Scharr, H.; Schurr, U.; Rascher, U. A stereo imaging system for measuring structural parameters of plant canopies. Plant Cell Environ. 2007, 30, 1299-308.