A 3D Canopy Reconstruction and Phenotype Analysis Method for Wheat

  • Boxiang Xiao
  • Sheng WuEmail author
  • Xinyu Guo
  • Weiliang Wen
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 545)


A high precise and high realistic three-dimensional wheat canopy model is important in modern agriculture. In this paper, we proposed a 3D reconstruction and quantitative calculation for phenotype analysis method for wheat. First, we made use of a 3D digitizer to acquire spatial structure and distribution data of wheat canopy. After data processing, we constructed three-dimensional organ models including stalks, leaves and others, based on a surface modeling algorithm. Under this process, we constructed a 3D canopy model by frames of wheat colony. Furthermore, we made phenotype analyses on structure and organs distribution features including leaf length, azimuth and obliquity values. By use of constructed 3D canopy model, we used a light distribution computing algorithm to analyze the potential light interception, and we also calculated interception in different layers and different organs. The synchronous light intensity and leaf area index (LAI) measured by a PAR device were used to compare and examine the constructed canopy models. We also compute macroscopic canopy attributes including leaf area, leaf area index, projection area, shading area, and so on. Finally, parts of experimental results are shown, and the results show that our method is feasible and effective for wheat as well as other similar crops. At the end, the main contributions and limitations are also discussed, and some future works are addressed.


Wheat Canopy reconstruction 3D Phenotype analysis Light distribution 



This work is supported by National Natural Science Foundation of China (Grant No. 61300079); by Beijing Municipal Science and Technology Project (Grant No. D151100004215004); by Creative Team Project of Beijing Academy of Agriculture and Forestry Science (Grant No. JNKYT201604).


  1. 1.
    Prusinkiewicz, P.: Modeling of spatial structure and development of plants: a review. Sci. Hortic. 74, 113–149 (1998)CrossRefGoogle Scholar
  2. 2.
    Deussen, O.: Dynamic a framework for geometry generation and rendering of plants with applications in landscape architecture. Landscape Urban Plann. 64, 105–113 (2003)CrossRefGoogle Scholar
  3. 3.
    Guo, Y., Li, B.: Progress in virtual plant research. Chin. Sci. Bull. 46(4), 273–280 (2001). (In Chinese)Google Scholar
  4. 4.
    Zhao, C.J., Lu, S.L., Guo, X.Y., et al.: Exploration of digital plant and its technology system. Sci. Agric. Sin. 43(10), 2023–2030 (2010). (In Chinese)Google Scholar
  5. 5.
    Xiao, B., Guo, X., Du, X., et al.: An interactive digital design system for corn modeling. Math. Comput. Model. 51(11–12), 1383–1389 (2010)CrossRefGoogle Scholar
  6. 6.
    Munier-Jolain, N.M., Guyot, S.H.M., Colbach, N.: A 3D model for light interception in heterogeneous crop: weed canopies: Model structure and evaluation. Ecol. Model. 250, 101–110 (2013)CrossRefGoogle Scholar
  7. 7.
    Zhang, W., Tang, L., Yang, X., et al.: A simulation model for predicting canopy structure and light distribution in wheat. Eur. J. Agron. 67, 1–11 (2015)CrossRefGoogle Scholar
  8. 8.
    Gou, F., van Ittersum, M.K., et al.: Intercropping wheat and maize increases total radiation interception and wheat RUE but lowers maize RUE. Eur. J. Agron. 84, 125–139 (2017)CrossRefGoogle Scholar
  9. 9.
    Baccar, R., Fournier, C., Dornbusch, T., et al.: Modelling the effect of wheat canopy architecture as affected by sowing density on Septoria tritici epidemics using a coupled epidemic–virtual plant model. Ann. Bot. 108, 1179–1194 (2011)CrossRefGoogle Scholar
  10. 10.
    Evers, J.B., Vos, J., Chelle, M.: Simulating the effects of localized red: far-red ratio on tillering in spring wheat (Triticum aestivum) using a three-dimensional virtual plant model. New Phytol. 176, 325–336 (2007)CrossRefGoogle Scholar
  11. 11.
    Fournier, C., Andrieu, B., Ljutovac, S., et al.: ADEL-wheat: a 3D architectural model of wheat development. In: Proceedings of International Symposium on Plant Growth Modeling, Simulation, Visualization, and their Applications, pp. 54–63. Tsinghua University Press/Springer, Beijing (2003)Google Scholar
  12. 12.
    Evers, J.B., Vos, J., Fournier, C., et al.: An architectural model of spring wheat: evaluation of the effects of population density and shading on model parameterization and performance. Ecol. Model. 200, 308–320 (2007)CrossRefGoogle Scholar
  13. 13.
    Hosoi, F., Omasa, K.: Estimating vertical plant area density profile and growth parameters of a wheat canopy at different growth stages using three-dimensional portable lidar imaging. ISPRS J. Photogramm. Remote Sens. 64, 151–158 (2009)CrossRefGoogle Scholar
  14. 14.
    Bietresato, M., Carabin, G., Vidoni, R., et al.: Evaluation of a LiDAR-based 3D-stereoscopic vision system for crop-monitoring applications. Comput. Electron. Agric. 124, 1–13 (2016)CrossRefGoogle Scholar
  15. 15.
    Bai, G., Ge, Y., Hussain, W., et al.: A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding. Comput. Electron. Agric. 128, 181–192 (2016)CrossRefGoogle Scholar
  16. 16.
    Burgess, A.J., Retkute, R., Pound, M.P., et al.: Image-based 3D canopy reconstruction to determine potential productivity in complex multi-species crop systems. Ann. Bot. 119, 517–532 (2017)Google Scholar
  17. 17.
    Wen, W., Meng, J., Guo, X., et al.: Calculation system of light distribution within crop canopy based on radiosity methods. Trans. Chin. Soc. Agric. Mach. 40(S1), 190–193 (2009)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Boxiang Xiao
    • 1
    • 2
    • 3
  • Sheng Wu
    • 1
    • 2
    • 3
  • Xinyu Guo
    • 1
    • 2
    • 3
  • Weiliang Wen
    • 1
    • 2
    • 3
  1. 1.Beijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry ScienceBeijingChina
  2. 2.National Engineering Research Center for Information Technology in AgricultureBeijingChina
  3. 3.Beijing Key Lab of Digital PlantBeijingChina

Personalised recommendations