Advertisement

3D Reconstruction of Plants Under Outdoor Conditions Using Image-Based Computer Vision

  • Abhipray PaturkarEmail author
  • Gaurab Sen Gupta
  • Donald Bailey
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)

Abstract

3D reconstruction of plants under outdoor conditions is a challenging task, for applications such as plant phenotyping which needs non-invasive methods. With the availability of new sensors and reconstructions techniques, 3D reconstruction is improving rapidly. However, sensors are still expensive for researchers. In this paper, we propose a cost-effective image-based 3D reconstruction approach which can be achieved by off-the-shelf cameras. This approach is based on the structure-from-motion method. We implemented this approach in MATLAB and Meshlab is used for further processing to achieve an exact 3D model. We also investigated the effect of different adverse outdoor scenarios which affect quality of 3D model such as movement of plants because of strong wind, drastic change in light condition while capturing the images. We have decreased the appropriate number of images needed to get precise 3D model. This method gives accurate results and it is a fast platform for non-invasive plant phenotyping.

Keywords

3D reconstruction Structure-from-motion Feature extraction Feature matching Plant phenotying 

References

  1. 1.
    Mishra, K.B., Mishra, A., Klem, K., Govindjee: Plant phenotyping: a perspective. Indian J. Plant Physiol. 21(4), 514–527 (2016)CrossRefGoogle Scholar
  2. 2.
    Li, L., Zhang, Q., Huang, D.: A review of imaging techniques for plant phenotyping. Sensors 14(11), 20078–20111 (2014)CrossRefGoogle Scholar
  3. 3.
    Zhang, Z.: Microsoft Kinect sensor and its effect. IEEE Multimedia 19(2), 4–10 (2012)CrossRefGoogle Scholar
  4. 4.
    Kazmi, W., Foix, S., Alenyà, G., Andersen, H.J.: Indoor and outdoor depth imaging of leaves with time-of-flight and stereo vision sensors: analysis and comparison. ISPRS J. Photogramm. Remote Sens. 88, 128–146 (2014)CrossRefGoogle Scholar
  5. 5.
    Guo, Q., et al.: Crop 3D—a LiDAR based platform for 3D high-throughput crop phenotyping. Sci. China Life Sci. 61(3), 328–339 (2018)CrossRefGoogle Scholar
  6. 6.
    Jebara, T., Azarbayejani, A., Pentland, A.: 3D structure from 2D motion. IEEE Signal Process. Mag. 16(3), 66–84 (1999)CrossRefGoogle Scholar
  7. 7.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vision 47(1–3), 7–42 (2002)CrossRefGoogle Scholar
  8. 8.
    Cremers, D., Kolev, K.: Multiview stereo and silhouette consistency via convex functionals over convex domains. IEEE Trans. Pattern Anal. Mach. Intell. 33(6), 1161–1174 (2011)CrossRefGoogle Scholar
  9. 9.
    Paturkar, A., Gupta, G.S., Bailey, D.: Overview of image-based 3D vision systems for agricultural applications. In: 2017 International Conference on Image and Vision Computing New Zealand (IVCNZ), pp. 1–6, December 2017Google Scholar
  10. 10.
    Kaminuma, E., et al.: Automatic quantification of morphological traits via three-dimensional measurement of Arabidopsis. Plant J. 38(2), 358–365 (2004)CrossRefGoogle Scholar
  11. 11.
    Paulus, S., Dupuis, J., Riedel, S., Kuhlmann, H.: Automated analysis of barley organs using 3D laser scanning: an approach for high throughput phenotyping. Sensors 14(7), 12670–12686 (2014)CrossRefGoogle Scholar
  12. 12.
    Baumberg, A., Lyons, A., Taylor, R.: 3D S.O.M.—a commercial software solution to 3D scanning. Graph. Models 67(6), 476–495 (2005)CrossRefGoogle Scholar
  13. 13.
    Chéné, Y., et al.: On the use of depth camera for 3D phenotyping of entire plants. Comput. Electron. Agric. 82, 122–127 (2012)CrossRefGoogle Scholar
  14. 14.
    Ivanov, N., Boissard, P., Chapron, M., Andrieu, B.: Computer stereo plotting for 3-D reconstruction of a maize canopy. Agric. For. Meteorol. 75(1), 85–102 (1995)CrossRefGoogle Scholar
  15. 15.
    Takizawa, H., Yamamoto, S., Ezaki, N., Mizuno, S.: Plant recognition by integrating color and range data obtained through stereo vision. J. Adv. Comput. Intell. Intell. Inform. 9(6), 630–636 (2005)CrossRefGoogle Scholar
  16. 16.
    Jay, S., Rabatel, G., Hadoux, X., Moura, D., Gorretta, N.: In-field crop row phenotyping from 3D modeling performed using structure from motion. Comput. Electron. Agric. 110, 70–77 (2015)CrossRefGoogle Scholar
  17. 17.
    Quan, L., Tan, P., Zeng, G., Yuan, L., Wang, J., Kang, S.B.: Image-based plant modeling. ACM Trans. Graph. 25(3), 599–604 (2006)CrossRefGoogle Scholar
  18. 18.
    Tan, P., Zeng, G., Wang, J., Kang, S.B., Quan, L.: Image-based tree modeling. ACM Trans. Graph. 26(3), 87 (2007)CrossRefGoogle Scholar
  19. 19.
    Paproki, A., Sirault, X., Berry, S., Furbank, R., Fripp, J.: A novel mesh processing based technique for 3D plant analysis. BMC Plant Biol. 12(1), 63 (2012)CrossRefGoogle Scholar
  20. 20.
    Meyer, G., Camargo Neto, J.: Verification of color vegetation indices for automated crop imaging applications. Comput. Electron. Agric. 63, 282–293 (2008)CrossRefGoogle Scholar
  21. 21.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  22. 22.
    Cignoni, P., Callieri, M., Corsini, M., Dellepiane, M., Ganovelli, F., Ranzuglia, G.: Meshlab: an open-source mesh processing tool. In: Scarano, V., Chiara, R.D., Erra, U. (eds.) Eurographics Italian Chapter Conference. The Eurographics Association (2008)Google Scholar
  23. 23.
    Liu, S.-X., An, P., Zhang, Z.-Y., Zhang, Q., Shen, L.-Q., Jiang, G.-Y.: On the relationship between multi-view data capturing and quality of rendered virtual view. Imaging Sci. J. 57(5), 250–259 (2009)CrossRefGoogle Scholar
  24. 24.
    Ni, Z., Burks, T., Lee, W.: 3D reconstruction of plant/tree canopy using monocular and binocular vision. J. Imaging 2(4), 28 (2016)CrossRefGoogle Scholar
  25. 25.
    Pound, M.P., French, A.P., Murchie, E.H., Pridmore, T.P.: Automated recovery of three-dimensional models of plant shoots from multiple color images. Plant Physiol. 166(4), 1688–1698 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Abhipray Paturkar
    • 1
    Email author
  • Gaurab Sen Gupta
    • 1
  • Donald Bailey
    • 1
  1. 1.School of Engineering and Advanced TechnologyMassey UniversityPalmerston NorthNew Zealand

Personalised recommendations