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An Illumination Invariant Maize Canopy Structure Parameters Analysis Method Based on Hemispherical Photography

  • Chuanyu Wang
  • Xinyu GuoEmail author
  • Jianjun Du
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 546)

Abstract

Hemispherical photography (HP) has already proven to be a powerful indirect method for measuring various components of canopy structure. In this paper an illumination invariant multiple exposure images fusion and mapping method was proposed in order to squash negative impact of variant illumination. Firstly, a series of multiple exposure maize canopy hemispherical images was captured under natural light condition. Secondly, the multiple photographs fused into a single radiance map whose pixel truncated in shadowed and lighted parts of original images expended to higher range. We were able to determine the irradiance value at each pixel, the pixel values are proportional to the true irradiance values in the scene. The pixel values, exposure times, and irradiance values form a least squares problem. Finally, we also employed a histogram equalisation method to map irradiance values to RGB color space. The comparison results show that canopy gaps fraction of HP acquired at 14:00 and 17:00 with threshold value 180 has difference of 15.4% percent, and our method reduces the difference up to 2.8%. Results of regression analysis shows that our method have a high consistency with canopy structure parameter direct surveying method, the correlation coefficient between two methods hit 0.94. The line slope was 1.463, our method measurement values were lower than direct surveying method. Our method expands the HP canopy structure parameters acquire timing, provides an automatic monitoring solution.

Keywords

Canopy structure Hemispherical photography Image fusion Image mapping Variant illumination 

Notes

Acknowledgemet

This work is supported by Nature Science Foundation of China (31501226).

References

  1. 1.
    Li, X., Wang, J.: Vegetation Optical Remote Sensing Models and Vegetation Structure Parameterization. Science Press, Beijing (1995)Google Scholar
  2. 2.
    Li, Y.: Theory and Application of Vegetation Radiative Transfer. Nanjing Normal University Press, Nanjing (2005)Google Scholar
  3. 3.
    Kvet, J., Marshall, J.K.: Assessment of leaf area and other assimilating plant surfaces. In: Sestak, Z., Catsky, J., Jarvis, P.G., et al. Plant Photosynthetic Production: Manual of Methods, The Hague, The Netherlands, pp. 517–574 (1971)Google Scholar
  4. 4.
    Song, G.Z.M., Doley, D., Yates, D., et al.: Improving accuracy of canopy hemispherical photography by a constant threshold value derived from an unobscured overcast sky. Can. J. For. Res. 44(1), 17–27 (2013)CrossRefGoogle Scholar
  5. 5.
    Leblanc, S.G., Fournier, R.A.: Hemispherical photography simulations with an architectural model to assess retrieval of leaf area index. Agric. For. Meteorol. 194(6), 64–76 (2014)CrossRefGoogle Scholar
  6. 6.
    Woodgate, W., Jones, S.D., Suarez, L., et al.: Understanding the variability in ground-based methods for retrieving canopy openness, gap fraction, and leaf area index in diverse forest systems. Agric. For. Meteorol. 205(3), 83–95 (2015)CrossRefGoogle Scholar
  7. 7.
    Foster, I., Kesselman, C., Nick, J., Tuecke, S.: The physiology of the grid: an open grid services architecture for distributed systems integration. Technical report, Global Grid Forum (2002)Google Scholar
  8. 8.
    Peng, H., Zhao, C., Feng, Z., et al.: Extracting the canopy structure parameters using hemispherical photography method. Acta Ecol. Sin. 31(12), 3376–3383 (2011)Google Scholar
  9. 9.
    Wu, T., Ni, S., Li, Y.: A comparison on the algorithms for retrieval of LAI based on gap fraction of vegetation canopy. J. Nanjing Norm. Univ. (Nat. Sci.) 29(1), 111–115 (2006)Google Scholar
  10. 10.
    Zarate-Valdez, J.L., Whiting, M.L., Lampinen, B.D., et al.: Prediction of leaf area index in almonds by vegetation indexes. Comput. Electron. Agric. 85(5), 24–32 (2012)CrossRefGoogle Scholar
  11. 11.
    Gonsamo, A., Jmn, W., Pellikka, P.: CIMES: a package of programs for determining canopy geometry and solar radiation regimes through hemispherical photographs. Comput. Electron. Agric. 79(2), 207–215 (2011)CrossRefGoogle Scholar
  12. 12.
    Baret, F., Solan, B.D., Lopez-Lozano, R., et al.: GAI estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5° zenith angle: theoretical considerations based on 3D architecture models and application to wheat crops. Agric. For. Meteorol. 150(11), 1393–1401 (2010)CrossRefGoogle Scholar
  13. 13.
    Confalonieri, R., Foi, M., Casa, R., et al.: Development of an app for estimating leaf area index using a smartphone. Trueness and precision determination and comparison with other indirect methods. Comput. Electron. Agric. 96(12), 67–74 (2013)CrossRefGoogle Scholar
  14. 14.
    Jeon, H.Y., Tian, L.F., Zhu, H.: Robust crop and weed segmentation under uncontrolled outdoor illumination. Sensors 11(6), 6270–6283 (2011)CrossRefGoogle Scholar
  15. 15.
    Lati, R.N., Filin, S., Eizenberg, H.: Robust methods for measurement of leaf-cover area and biomass from image data. Weed Sci. 59(2), 276–284 (2011)CrossRefGoogle Scholar
  16. 16.
    Liu, Y., Mu, X., Wang, H., et al.: A novel method for extracting green fractional vegetation cover from digital images. J. Veg. Sci. 23(3), 406–418 (2012)CrossRefGoogle Scholar
  17. 17.
    Panneton, B., Brouillard, M.: Colour representation methods for segmentation of vegetation in photographs. Biosyst. Eng. 102(4), 365–378 (2009)CrossRefGoogle Scholar
  18. 18.
    Woebbecke, D.M., Meyer, G.E., Von Bargen, K., Mortensen, D.A.: Color indices for weed identification under various soil residue and lighting conditions. Trans. ASAE 38, 259–269 (1995)CrossRefGoogle Scholar
  19. 19.
    Woebbecke, D.M., Meyer, G.E., Von Bargen, K., et al.: Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE 38(1), 259–269 (1995)CrossRefGoogle Scholar
  20. 20.
    Ruiz Ruiz, G., Gómez Gil, J., Navas Gracia, L.M.: Testing different color spaces based on hue for the environmentally adaptive segmentation algorithm (EASA). Comput. Electron. Agric. 68(1), 88–96 (2009)CrossRefGoogle Scholar
  21. 21.
    Zheng, L., Shi, D., Zhang, J.: Segmentation of green vegetation of crop canopy images based on mean shift and fisher linear discriminant. Pattern Recogn. Lett. 31(9), 920–925 (2010)CrossRefGoogle Scholar
  22. 22.
    Song, H., He, D., Gong, L.: Crops image fusion in different light conditions based on Contourlet transform. Trans. Chin. Soc. Agric. Eng. (Trans. CSAE) 30(11), 173–179 (2014)Google Scholar
  23. 23.
    Monsi, M., Saeki, T.: The light factor in plant communities and its significance for dry matter production. Jpn. J. Bot. 14(1), 22–52 (1953)Google Scholar
  24. 24.
    Kucharik, C.J., Norman, J.M., Gower, S.T.: Measurements of leaf orientation, light distribution and sunlit leaf area in a boreal aspen forest. Agric. For. Meteorol. 91(1), 127–148 (1998)CrossRefGoogle Scholar
  25. 25.
    Campbell, G.S.: Derivation of an angle density function for canopies with ellipsoidal leaf angle distributions. Agric. For. Meteorol. 49(90), 173–176 (1990)CrossRefGoogle Scholar
  26. 26.
    Lang, A.R.G.: An instrument for measuring canopy structure. Remote Sens. Rev. 5(1), 61–71 (1990)CrossRefGoogle Scholar
  27. 27.
    Ning, H., Chuangen, L., Kemin, Y., et al.: Inversion of rice canopy construction parameters from the hemispherical photograph. Acta Gronomica Sin. A 40(8), 1443–1451 (2014)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  1. 1.Beijing Research Center for Information Technology in AgricultureBeijingChina
  2. 2.Beijing Key Lab of Digital PlantBeijingChina

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