Advertisement

Hyperspectral Estimation of Nitrogen Content in Winter Wheat Leaves Based on Unmanned Aerial Vehicles

  • Liu Mingxing
  • Li ChangchunEmail author
  • Feng HaikuanEmail author
  • Pei Haojie
  • Li Zhenhai
  • Yang Fuqin
  • Yang Guijun
  • Xu Shouzhi
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 546)

Abstract

Leaf nitrogen content is an important index of crop growth and plays an important role in crop growth and development. In this paper, the hyperspectral data of winter wheat and the leaf nitrogen content is used to study winter wheat on flagging stage, flowering stage and grain filling stage. The estimation model of nitrogen content in winter wheat leaves at different growth stages is constructed by using partial least squares method and verified by using a cross-validation method. The results showed that R2 and the RMSE of the three growth stages were 0.53, 0.68, 0.64 and 0.331%, 0.246% and 0.406% respectively, and R2 and RMSE of model validation were 0.44, 0.71, 0.64 and 0.369%, 0.235% and 0.410%. Both the prediction model and the verification model had high reliability. Therefore, it is feasible for UAV to carry hyperspectral monitoring system for retrieving nitrogen content of winter wheat leaves.

Keywords

Unmanned aerial vehicles Hyperspectral Winter wheat Leaf nitrogen content Partial least squares method 

Notes

Acknowledgments

This work was supported in part by the National Key Research and Development Programs (2016YFD0300602), National Natural Science Foundation of China (No. 41601346), Surveying and mapping geographic information public industry scientific research projects (201512010).

References

  1. 1.
    Wang, J., Zhao, C., Huang, W., et al.: Quantitative agricultural remote sensing. Science Press, Beijing (2008)Google Scholar
  2. 2.
    Pinter Jr., P.J., Hatfield, J.L., Schepers, J.S., et al.: Remote sensing for crop management. Potogrammetric Eng. Remote Sens. 69(6), 647–664 (2003)CrossRefGoogle Scholar
  3. 3.
    Hansen, P.M., Schjoerring, J.K.: Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens. Environ. 86(4), 542–553 (2003)CrossRefGoogle Scholar
  4. 4.
    Feng, W., Yao, X., Zhu, Y., et al.: Monitoring leaf nitrogen status with hyperspectral reflectance in wheat. Eur. J. Agron. 28(3), 394–404 (2008)CrossRefGoogle Scholar
  5. 5.
    Clevers, J., Kooistra, L.: Using hyperspectral remote sensing data for retrieving total canopy chlorophyll and nitrogen content. In: 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1–4. IEEE (2011)Google Scholar
  6. 6.
    Zhang, X., Zhang, L., Zhang, X., et al.: The sensitivity study of retrieving leaf nitrogen content of Winter Wheat with different spectral vegetation index. Chin. Agric. Sci. 50(3), 474–485 (2017)Google Scholar
  7. 7.
    Zhu, X., Sheng, H., Gu, J., et al.: Preliminary study on predicting chlorophyll and nitrogen content in wheat leaves using SPAD value. J. Wheat Crop. 25(2), 46–50 (2005)Google Scholar
  8. 8.
    Li, Y., Zhu, Y., Tian, Y., et al.: Quantitative relationship between leaf nitrogen content and canopy reflectance spectral index. Proc. Crop 26(3), 3463–3469 (2006)Google Scholar
  9. 9.
    Sun, Y., Wang, J., Li, B., et al.: Establishment and validation of GRNN hyperspectral remote sensing model for retrieving winter wheat leaf nitrogen content based on GA. Bull. Soils 38(3), 508–512 (2007)Google Scholar
  10. 10.
    Zhang, G., Xu, T., Yu, F., et al.: Rice leaf nitrogen Hyperspectral Estimation and inversion based on the model. Zhejiang J. Agric. Sci. 29(5), 845–849 (2017)Google Scholar
  11. 11.
    Wang, J., Huang, W., Lao, C.L., et al.: Using wheat canopy reflectance spectra to retrieve the vertical distribution of nitrogen by PLS algorithm. Spectrosc. Spectr. Anal. 27(7), 1319–1322 (2007)Google Scholar
  12. 12.
    Ju, C.: Monitoring nitrogen status and growth characteristics of wheat using ground air hyperspectral remote sensing. Nanjing Agricultural University (2008)Google Scholar
  13. 13.
    Zhai, Q., Zhang, J., Xiong, S., et al.: Hyperspectral differences and monitoring model construction of nitrogen content in wheat leaves based on different soil texture. Agric. Sci. China 46(13), 2655–2667 (2013)Google Scholar
  14. 14.
    Li, F., Chang, Q., Shen, J., et al.: Estimation of nitrogen content in winter wheat leaves with wide band reflectance of simulated multispectral satellite. Chin. J. Agric. Mach. 47(2), 302–308 (2016)Google Scholar
  15. 15.
    Wang, R., Song, X., Li, Z.: Estimation of nitrogen nutrition index of winter wheat based on hyperspectral analysis. J. Agric. Eng. 30(19), 191–198 (2014)Google Scholar
  16. 16.
    Yang, F., Dai, H., Feng, H., et al.: Estimation of plant nitrogen content in winter wheat using hyperspectral based on akaike information criterion. J. Agric. Eng. 32(23), 161–167 (2016)Google Scholar
  17. 17.
    Liu, H., Zhu, H., Wang, P., et al.: Quantitative modeling for leaf nitrogen content of winter wheat using UAV-based hyperspectral data. Int. J. Remote Sens. 38(8–10), 2117–2134 (2017)CrossRefGoogle Scholar
  18. 18.
    Li, B., Li, Z., Yu, T., et al.: Study on fractal dimension of vegetation cover in watershed based on normalized vegetation index. J. Agric. Eng. 30(15), 239–247 (2014)Google Scholar
  19. 19.
    Tian, Y.C., Yao, X., Yang, J., et al.: Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground-and space-based hyperspectral reflectance. Field Crop. Res. 120(2), 299–310 (2011)CrossRefGoogle Scholar
  20. 20.
    Luo, P., Guo, J., Li, Q., et al.: Discussion on modeling based on partial least squares regression. J. Tianjin Univ. Nat. Sci. Eng. Technol. 35(6), 783–786 (2002)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Liu Mingxing
    • 1
    • 2
    • 3
    • 4
  • Li Changchun
    • 1
    Email author
  • Feng Haikuan
    • 2
    • 3
    • 4
    Email author
  • Pei Haojie
    • 1
    • 2
    • 3
    • 4
  • Li Zhenhai
    • 2
    • 3
    • 4
  • Yang Fuqin
    • 5
  • Yang Guijun
    • 2
    • 3
    • 4
  • Xu Shouzhi
    • 6
  1. 1.School of Surveying and Land Information EngineeringHenan Polytechnic UniversityJiaozuoChina
  2. 2.National Engineering Research Center for Information Technology in AgricultureBeijingChina
  3. 3.Key Laboratory for Information Technologies in AgricultureThe Ministry of AgricultureBeijingChina
  4. 4.Beijing Engineering Research Center of Agricultural Internet of ThingsBeijingChina
  5. 5.College of Civil EngineeringHenan Institute of EngineeringZhengzhouChina
  6. 6.National Calibration Center for Surveying InstrumentsBeijingChina

Personalised recommendations