Advertisement

Estimation of Leaf Nitrogen Concentration of Winter Wheat Using UAV-Based RGB Imagery

  • Qinglin Niu
  • Haikuan Feng
  • Changchun Li
  • Guijun YangEmail author
  • Yuanyuan Fu
  • Zhenhai Li
  • Haojie Pei
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 546)

Abstract

Leaf nitrogen concentration (LNC) of winter wheat can reflect its nitrogen (N) status. Rapid, non-destructive and accurate monitoring of LNC of winter wheat has important practical applications in monitoring N nutrition and fertilizing management. The experimental site of winter wheat was located at Xiaotangshan National Demonstration Base of Precision Agricultural Research located in Changping District, Beijing, China. High spatial resolution digital images of the winter wheat were acquired using a low-cost unmanned aerial vehicle (UAV) with digital camera system at three key growth stages of booting, flowering and filling during April to June in 2015. Firstly, the acquired UAV digital images were mosaicked to generate a Digital Orthophoto Map (DOM) of the entire experimental site and 15 digital image variables were constructed. Then, based on the ground measured data onto LNC and digital image variables derived from the DOM for 48 sampling plots of winter wheat, linear and stepwise regression models were constructed for estimating LNC. Finally, the optimum model for estimating LNC was screened out by comprehensively considering the coefficient of determination (R2), the root mean square error (RMSE), the normalized root mean square error (nRMSE) and the simplicity of model calibrating and validating. The experimental results showed that the linear regression model of r/b that was one of the digital image variables for estimating LNC had the best accuracy with the model’s calibration and validation of R2, RMSE and nRMSE were 0.76, 0.40, 11.97% and 0.69, 0.43, 13.02%, respectively. The results suggest that it is feasible to estimate LNC of winter wheat based on the DOM acquired by UAV remote sensing platform carrying a low-cost, high-resolution digital camera, which can rapidly and non-destructively obtains the LNC of winter wheat experiment site and provide a quick and low-cost method for monitoring N nutrition and fertilizing management.

Keywords

Winter wheat Leaf nitrogen concentration (LNC) Remote sensing Unmanned aerial vehicle (UAV) Digital imagery High-resolution 

References

  1. 1.
    Scheromm, P., Martin, G., Bergoin, A., et al.: Influence of nitrogen fertilization on the potential bread-baking quality of two wheat cultivars differing in their responses to increasing nitrogen supplies. Cereal Chem. 69, 664–670 (1993)Google Scholar
  2. 2.
    Guo, S.L., Dang, T.H., Hao, M.D.: Effects of fertilization on wheat yield, NO_3--N accumulation and soil water content in semi-arid area of China. Scientia Agricultura Sinica 4(4), 745–751 (2005)Google Scholar
  3. 3.
    Pjjr, P., Hatfield, J.L., Schepers, J.S., et al.: Remote sensing for crop management. Photogram. Eng. Remote Sens. 69(6), 647–664 (2003)CrossRefGoogle Scholar
  4. 4.
    Xu, X.G., Zhao, C.J., Wang, J.H., et al.: Using optimal combination method and in situ hyperspectral measurements to estimate leaf nitrogen concentration in barley. Precision Agric. 15(2), 227–240 (2014)CrossRefGoogle Scholar
  5. 5.
    Jin, X., Liu, S., Baret, F., et al.: Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sens. Environ. 198, 105–114 (2017)CrossRefGoogle Scholar
  6. 6.
    Moharana, S., Dutta, S.: Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery. ISPRS J. Photogram. Remote Sens. 122, 17–29 (2016)CrossRefGoogle Scholar
  7. 7.
    Zhao, C.J.: Advances of research and application in remote sensing for agriculture. Trans. Chin. Soc. Agric. Mach. 45(12), 277–293 (2014)Google Scholar
  8. 8.
    Zhou, W.T., Wu, B.F., Zhang, M., et al.: Comprehensive monitoring of crop growth – take India as an example. J. Remote Sens. 19(4), 539–549 (2015)Google Scholar
  9. 9.
    Tang, J.M., Liao, Q.H., Liu, Y.Q., et al.: Estimating leaf area index of crops based on hyperspectral compact airborne spectrographic imager (CASI) data. Spectrosc. Spectral Anal. 35(5), 1351–1356 (2015)Google Scholar
  10. 10.
    Yang, G., Liu, J., Zhao, C., et al.: Unmanned aerial vehicle remote sensing for field-based crop phenotyping: current status and perspectives. Front. Plant Sci. 8, 1111 (2017)CrossRefGoogle Scholar
  11. 11.
    Zhang, C., Kovacs, J.M.: The application of small unmanned aerial systems for precision agriculture: a review. Precision Agric. 13(6), 693–712 (2012)CrossRefGoogle Scholar
  12. 12.
    Shao, S., Wei, X., et al.: Framework of SAGI agriculture remote sensing and its perspectives in supporting national food security. J. Integr. Agric. 13(7), 1443–1450 (2014)CrossRefGoogle Scholar
  13. 13.
    Candiago, S., Remondino, F., De Giglio, M., et al.: Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sens. 7(4), 4026–4047 (2015)CrossRefGoogle Scholar
  14. 14.
    Suomalainen, J., Anders, N., Iqbal, S., et al.: A lightweight hyperspectral mapping system and photogrammetric processing chain for unmanned aerial vehicles. Remote Sens. 6(11), 11013–11030 (2014)CrossRefGoogle Scholar
  15. 15.
    Zhao, X.Q., Yang, G.J., Liu, J.G., et al.: Estimation of soybean breeding yield based on optimization of spatial scale of UAV hyperspectral image. Trans. CSAE 33(1), 110–116 (2017)Google Scholar
  16. 16.
    Nie, S., Wang, C., Dong, P., et al.: Estimating leaf area index of maize using airborne discrete-return LiDAR Data. Remote Sens. Lett. 9(7), 3259–3266 (2016)Google Scholar
  17. 17.
    Vergaradíaz, O., Zamanallah, M.A., Masuka, B., et al.: A novel remote sensing approach for prediction of maize yield under different conditions of nitrogen fertilization. Front. Plant Sci. 7, 666 (2016)Google Scholar
  18. 18.
    Li, W., Niu, Z., Chen, H., et al.: Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system. Ecol. Ind. 67, 637–648 (2016)CrossRefGoogle Scholar
  19. 19.
    Bendig, J., Yu, K., Aasen, H., et al.: Combining UAV-based plant height from crop surface models, visible, and near-infrared vegetation indices for biomass monitoring in barley. Int. J. Appl. Earth Obs. Geoinf. 39, 79–87 (2015)CrossRefGoogle Scholar
  20. 20.
    Schirrmann, M., Giebel, A., Gleiniger, F., et al.: Monitoring agronomic parameters of winter wheat crops with low-cost UAV imagery. Remote Sens. 8(9), 706 (2016)CrossRefGoogle Scholar
  21. 21.
    Saberioon, M.M., Amin, M.S.M., Anuar, A.R., et al.: Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale. Int. J. Appl. Earth Obs. Geoinf. 32(10), 35–45 (2014)CrossRefGoogle Scholar
  22. 22.
    Torres-Sánchez, J., López-Granados, F., Peña, J.M.: An automatic object-based method for optimal thresholding in UAV images: application for vegetation detection in herbaceous crops. Comput. Electron. Agric. 114(C), 43–52 (2015)CrossRefGoogle Scholar
  23. 23.
    Zhou, X., Zheng, H.B., Xu, X.Q., et al.: Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery. ISPRS J. Photogram. Remote Sens. 130, 246–255 (2017)CrossRefGoogle Scholar
  24. 24.
    Tucker, C.J.: Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8(2), 127–150 (1979)CrossRefGoogle Scholar
  25. 25.
    Louhaichi, M., Borman, M.M., Johnson, D.E.: Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto Int. 16(1), 65–70 (2001)CrossRefGoogle Scholar
  26. 26.
    Meyer, G.E., Neto, J.C.: Verification of color vegetation indices for automated crop imaging applications. Comput. Electron. Agric. 63(2), 282–293 (2008)CrossRefGoogle Scholar
  27. 27.
    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
  28. 28.
    Kataoka, T., Kaneko, T., Okamoto, H., et al.: Crop growth estimation system using machine vision. In: Proceedings of 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2003, vol. 2, pp. b1079–b1083. IEEE (2003)Google Scholar
  29. 29.
    Gitelson, A.A., Viña, A., Arkebauer, T.J., et al.: Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys. Res. Lett. 30(5) (2003)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Qinglin Niu
    • 1
    • 2
  • Haikuan Feng
    • 2
    • 3
  • Changchun Li
    • 1
  • Guijun Yang
    • 2
    • 3
    Email author
  • Yuanyuan Fu
    • 2
    • 3
  • Zhenhai Li
    • 2
    • 3
  • Haojie Pei
    • 1
    • 2
  1. 1.School of Surveying and Land Information EngineeringHenan Polytechnic UniversityJiaozuoChina
  2. 2.Key Laboratory of Quantitative Remote Sensing in Agriculture P.R. ChinaBeijing Research Center for Information Technology in AgricultureBeijingChina
  3. 3.National Engineering Research Center for Information Technology in AgricultureBeijingChina

Personalised recommendations