Back
 AS  Vol.7 No.10 , October 2016
Improve the Prediction Accuracy of Apple Tree Canopy Nitrogen Content through Multiple Scattering Correction Using Spectroscopy
Abstract:
Method: Use Multiple Scattering Correction to eliminate the interference of scattering on spectrum in the process of field measurement so as to improve the accuracy of prediction model of tree canopy nitrogen content. Apple trees in Qixia of Yantai City were taken as the test material. The spectral reflectivity of apple tree canopy went through the First Derivative (FD) and Multiple Scattering Correction (MSC) plus first derivative, respectively. The correlation coefficients were calculated between spectral reflectivity and nitrogen content. The Support Vector Machine (SVM) method was used to establish the prediction model. The result indicates that the MSC pre-processing can improve the correlation between spectral reflectivity and nitrogen content. The SVM model with MSC + FD pre-processing was a good way to predict the nitrogen content. The calibration R2 of the model was 0.746; the validation R2 was 0.720; and its RMSE was 0.452 g·kgˉ1. MSC can commendably eliminate scattering error to improve the prediction accuracy of prediction model.
Cite this paper: Gao, L. , Zhu, X. , Li, C. , Cheng, L. , Wang, L. , Zhao, G. and Jiang, Y. (2016) Improve the Prediction Accuracy of Apple Tree Canopy Nitrogen Content through Multiple Scattering Correction Using Spectroscopy. Agricultural Sciences, 7, 651-659. doi: 10.4236/as.2016.710061.
References

[1]   [1]Peng, F.T., Jiang, Y.M., Gu, M.R. and Shu, H.R. (2003) Effect of Nitrogen on Apple Fruit Hormone Changing Trends and Development. Plant Nutrition and Fertilizer Science, 9, 208-213.

[2]   Zhu, X.C., Zhao, G.X., Wang, L., Dong, F., Lei, T. and Zhan, B. (2010) Hyperspectrum Based Prediction Model for Nitrogen Content of Apple Flowers. Spectroscopy and Spectral Analysis, 30, 416-420.

[3]   Huang, H., Wang, W., Peng, Y.K., Wu, J.H., Gao, X.D., Wang, X. and Zhang, J. (2010) Measurement of Chlorophyll Content in Wheat Leaves Using Hyperspectral Scanning. Spectroscopy and Spectral Analysis, 30, 1811-1814.

[4]   Liang, L., Yang, M.H. and Zang, Z. (2010) Determination of Wheat Canopy Nitrogen Content Ratio by Hyperspectral Technology Based on Wavelet Denoising and Support Vector Regression. Transaction of the CSAE, 26, 248-253.

[5]   Daughtry, C.S.T., Walthall, C.L., Kim, M.S., de Colstount, E.B. and McMurtrey Ⅲ, J.E. (2000) Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance. Remote Sensing of Environment, 74, 229-239.

[6]   Yang, J., Tian, Y.C., Yao, X., Cao, W.X., Zhang, Y.S. and Zhu, Y. (2009) Hyperspectral Estimation Model for Chlorophyll Concentrations in Top Leaves of Rice. Acta Ecology Sinica, 29, 6561-6571.

[7]   Huang, C.Y., Wang, D.W., Yan, H., Zhang, Y.X., Cao, L.P. and Cheng, C. (2007) Monitoring of Cotton Canopy Chlorophyll Density and Leaf Nitrogen Accumulation Status by Using Hyperspectral Data. Acta Agronomica Sinica, 33, 931-936.

[8]   Centner, V., Verdu-Andres, J., Walczak, B., Jouan-Rimbaud, D., Despagne, F., Pasti, L., Massart, D.-L. and De Noord, O.E. (2000) Comparison of Multivariate Calibration Techniques Applied to Experimental NIR Data Sets. Applied Spectroscopy, 54, 608-623.
http://dx.doi.org/10.1366/0003702001949816

[9]   Wang, K., Chi, G.Y., Lau, R. and Chen, T. (2011) Multivariate Calibration of Near Infrared Spectroscopy in the Presence of Light Scattering Effect: A Comparative Study. Analytical Letters, 44, 824-836.
http://dx.doi.org/10.1080/00032711003789967

[10]   Wang, D.M., Ji, J.M. and Gao, H.Z. (2014) The Effect of MSC Spectral Pretreatment Regions on Near Infrared Spectroscopy Calibration Results. Spectroscopy and Spectral Analysis, 34, 2387-2390.

[11]   Zhao, Q., Zhang, G.L. and Chen, X.D. (2005) Effects of Multiplicative Scatter Correction on a Calibration Model of Near Infrared Spectral Analysis. Optics and Precision Engineering, 13, 53-58.

[12]   Xiong, Z.X., Wu, Z.C., Chen, Z.X. and Hu, M.Y. (2007) Application of MSC to the Improvement of Analyzing Accuracy of Fatty Acid in Rapeseed by Near-Infrared Spectroscopy. Chinese Journal of Spectroscopy Laboratory, 24, 953-958.

[13]   Geladi, P., Macdougall, D. and Martens, H. (1985) Linearization and Scatter Correction for Near Infrared Reflectance Spectra of Meat. Applied Spectroscopy, 39, 491-500.
http://dx.doi.org/10.1366/0003702854248656

[14]   Estienne, F., Despagne, F., Walczak, B., de Noord, O.E. and Massart, D.L. (2004) A Comparison of Multivariate Calibration Techniques Applied to Experimental NIR Data Sets: Part III: Robustness against Instrumental Perturbation Conditions. Chemometrics and Intelligent Laboratory Systems, 73, 207-218.
http://dx.doi.org/10.1016/j.chemolab.2004.04.007

[15]   Wang, L., Zhao, G.X., Zhu, X.C., Lei, T. and Dong, F. (2010) Quantitative Models between Canopy Hyperspectrum and Its Component Features at Apple Tree Prosperous Fruit Stage. Spectroscopy and Spectral Analysis, 30, 2719-2723.

 
 
Top