Model-based Segmentation of 3D Point Clouds for Phenotyping Sunflower Plants

William Gelard, Michel Devy, Ariane Herbulot, Philippe Burger

Abstract

This article presents a model-based segmentation method applied to 3D data acquired on sunflower plants. Our objective is the quantification of the plant growth using observations made automatically from sensors moved around plants. Here, acquisitions are made on isolated plants: a 3D point cloud is computed using Structure from Motion with RGB images acquired all around a plant. Then the proposed method is applied in order to segment and label the plant leaves, i.e. to split up the point cloud in regions corresponding to plant organs: stem, petioles, and leaves. Every leaf is then reconstructed with NURBS and its area is computed from the triangular mesh. Our segmentation method is validated comparing these areas with the ones measured manually using a planimeter: it is shown that differences between automatic and manual measurements are less than 10%. The present results open interesting perspectives in direction of high-throughput sunflower phenotyping.

References

  1. Bernardini, F., Mittleman, J., Rushmeier, H., Silva, C., and Taubin, G. (1999). The ball-pivoting algorithm for surface reconstruction. IEEE Transactions on Visualization and Computer Graphics, 5(4):349-359.
  2. Chéné, Y., Rousseau, D., Lucidarme, P., Bertheloot, J., Caffier, V., Morel, P., Ótienne Belin, and ChapeauBlondeau, F. (2012). On the use of depth camera for 3d phenotyping of entire plants. Computers and Electronics in Agriculture, 82:122 - 127.
  3. Dhondt, S., Wuyts, N., and Inzé, D. (2013). Cell to wholeplant phenotyping: the best is yet to come. Trends in Plant Science, 18(8):428 - 439.
  4. Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. AAAI Press.
  5. Fiorani, F. and Schurr, U. (2013). Future Scenarios for Plant Phenotyping. Annual review of plant biology, 64:267 - 291.
  6. Furukawa, Y., Curless, B., Seitz, S. M., and Szeliski, R. (2010). Towards internet-scale multi-view stereo. In CVPR.
  7. Furukawa, Y. and Ponce, J. (2010). Accurate, dense, and robust multi-view stereopsis. IEEE Trans. on Pattern Analysis and Machine Intelligence, 32(8):1362-1376.
  8. Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., and Fitzgibbon, A. (2011). Kinectfusion: Real-time 3d reconstruction and interaction using a moving depth camera. In ACM Symposium on User Interface Software and Technology, UIST 7811, pages 559-568, New York, NY, USA. ACM.
  9. J. A. Hartigan, M. A. W. (1979). Algorithm as 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1):100-108.
  10. Jay, S., Rabatel, G., Hadoux, X., Moura, D., and Gorretta, N. (2015). In-field crop row phenotyping from 3d modeling performed using structure from motion. Computers and Electronics in Agriculture, 110:70 - 77.
  11. Kazhdan, M., Bolitho, M., and Hoppe, H. (2006). Poisson Surface Reconstruction. In Sheffer, A. and Polthier, K., editors, Symposium on Geometry Processing. The Eurographics Association.
  12. Lhuillier, M. and Quan, L. (2005). A Quasi-Dense Approach to Surface Reconstruction from Uncalibrated Iages. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(3):418-433.
  13. Lou, L., Liu, Y., Han, J., and Doonan, J. H. (2014). Accurate Multi-View Stereo 3D Reconstruction for CostEffective Plant Phenotyping, pages 349-356. Springer International Publishing, Cham.
  14. Louarn, G., Carré, S., Boudon, F., Eprinchard, A., and Combes, D. (2012). Characterization of whole plant leaf area properties using laser scanner point clouds. In Fourth International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications, Shanghai, China.
  15. Morwald, T. (2012). Fitting trimmed b-splines to unordered point clouds.
  16. Ng, A. Y., Jordan, M. I., and Weiss, Y. (2002). On spectral clustering: Analysis and an algorithm. In Dietterich, T. G., Becker, S., and Ghahramani, Z., editors, Advances in Neural Information Processing Systems 14, pages 849-856. MIT Press.
  17. Paproki, A., Sirault, X., Berry, S., Furbank, R., and Fripp, J. (2012). A novel mesh processing based technique for 3d plant analysis. BMC Plant Biology.
  18. Paulus, S., Behmann, J., Mahlein, A.-K., Plmer, L., and Kuhlmann, H. (2014). Low-cost 3d systems: Suitable tools for plant phenotyping. Sensors, 14(2):3001.
  19. Paulus, S., Dupuis, J., Mahlein, A.-K., and Kuhlmann, H. (2013). Surface feature based classification of plant organs from 3d laserscanned point clouds for plant phenotyping. BMC Bioinformatics, 14(1):1-12.
  20. Piegl, L. and Tiller, W. (1997). The NURBS Book (2Nd Ed.). Springer-Verlag New York, Inc., New York, NY, USA.
  21. Quan, L., Tan, P., Zeng, G., Yuan, L., Wang, J., and Kang, S. B. (2007). Image-based plant modeling. ACM SIGGRAPH and ACM Transactions on Graphics, 25(3):772778.
  22. Rey, H., Dauzat, J., Chenu, K., Barczi, J.-F., Dosio, G. A. A., and Lecoeur, J. (2008). Using a 3-d virtual sunflower to simulate light capture at organ, plant and plot levels: Contribution of organ interception, impact of heliotropism and analysis of genotypic differences. Ann Bot, 101(8):1139-1151. 18218705[pmid].
  23. Rusu, R. B. (2009). Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments. PhD thesis, Computer Science department, Technische Universitaet Muenchen, Germany.
  24. Rusu, R. B., Blodow, N., and Beetz, M. (2009). Fast point feature histograms (fpfh) for 3d registration. In Proceedings of the 2009 IEEE International Conference on Robotics and Automation, ICRA'09, pages 1848- 1853, Piscataway, NJ, USA. IEEE Press.
  25. Rusu, R. B. and Cousins, S. (2011). 3D is here: Point Cloud Library (PCL). In International Conference on Robotics and Automation, Shanghai, China.
  26. Santos, T. T., Koenigkan, L. V., Barbedo, J. G. A., and Rodrigues, G. C. (2015). Computer Vision - ECCV 2014 Workshops: Zurich, Switzerland, chapter 3D Plant Modeling: Localization, Mapping and Segmentation for Plant Phenotyping Using a Single Hand-held Camera, pages 247-263. Springer.
  27. Santos, T. T. and Oliveira, A. A. (2012). Image-based 3D digitizing for plant architecture analysis and phenotyping. In Saúde, A. V. and Guimara˜es, S. J. F., editors, Workshop on Industry Applications (WGARI) in SIBGRAPI 2012 (XXV Conference on Graphics, Patterns and Images), Ouro Preto, MG, Brazil.
  28. Snavely, N., Seitz, S. M., and Szeliski, R. (2006). Photo tourism: Exploring photo collections in 3d. ACM Trans. Graph., 25(3):835-846.
  29. Wahabzada, M., Paulus, S., Kersting, K., and Mahlein, A.-K. (2015). Automated interpretation of 3d laserscanned point clouds for plant organ segmentation. BMC Bioinformatics, 16(1):1-11.
  30. Xia, C., Wang, L., Chung, B.-K., and Lee, J.-M. (2015). In situ 3d segmentation of individual plant leaves using a rgb-d camera for agricultural automation. Sensors, 15(8):20463.
Download


Paper Citation


in Harvard Style

Gelard W., Devy M., Herbulot A. and Burger P. (2017). Model-based Segmentation of 3D Point Clouds for Phenotyping Sunflower Plants . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 459-467. DOI: 10.5220/0006126404590467


in Bibtex Style

@conference{visapp17,
author={William Gelard and Michel Devy and Ariane Herbulot and Philippe Burger},
title={Model-based Segmentation of 3D Point Clouds for Phenotyping Sunflower Plants},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={459-467},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006126404590467},
isbn={978-989-758-225-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Model-based Segmentation of 3D Point Clouds for Phenotyping Sunflower Plants
SN - 978-989-758-225-7
AU - Gelard W.
AU - Devy M.
AU - Herbulot A.
AU - Burger P.
PY - 2017
SP - 459
EP - 467
DO - 10.5220/0006126404590467