Nondestructive Measurement of Grape Leaf Chlorophyll Content Using Multi-Spectral Imaging Technology and Calibration Models


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Nondestructive measurement of grape leaf chlorophyll content is essential for precision vineyard management. Multi-spectral imaging technology was adopted for image acquisition of grape leave. For each leaf, a color (R-G-B) image and a near-infrared (NIR) image were taken. These images were then transformed into three vegetation indices, e.g. RVI, NDVI and GNDVI. Calibration models were established, by single-variable linear regression, multi-variable linear regression and BP-ANN. Three color space systems, e.g. R-G-B, CIE XYZ and HIS, were examined with the purpose of model optimization. A total of 112 leave were divided into a calibration set(62) and an independent validation set(50). A SPAD-502 chlorophyll meter was used for reference measurement. The single-variable linear regression result shows that the NDVI index is most significant for the measurement of leaf chlorophyll content with coefficient of determination (r2) of 0.70 for calibration set and 0.69 for independent validation set. It is found that the model for R-index produces higher accuracy than those for G- and B-index, which confirms that chlorophyll content can be correlated with R-grayscale values. By comparison, the multi-variable linear regression models based on R-G-B-NIR achieves higher prediction accuracy with r2 of 0.8174. To further improve the prediction accuracy, several BP-ANN models were developed. The best result was achieved for R-G-B-NIR with r2 of 0.99 for independent validation set. It is concluded that multi-spectral imaging technology coupled with BP-ANN calibration model of R-G-B-NIR grayscales is promising for nondestructive measurement of grape leaf chlorophyll content. This method proposed in the study is worthy of being further examined for in situ determination of nutrition diagnose of grape plant.



Edited by:

Elwin Mao and Linli Xu






G. Lv and H. Q. Yang, "Nondestructive Measurement of Grape Leaf Chlorophyll Content Using Multi-Spectral Imaging Technology and Calibration Models", Advanced Engineering Forum, Vol. 1, pp. 365-369, 2011

Online since:

September 2011


[1] Q. Zhen, Z. Wang, Y. Wang, D. Zu, J. Duan, Study on spatial-temporal distribution of chlorophyll content and its correlation to plant N content in summer maize, Journal of Maize Sciences. 16(2008)75-78.

[2] D.I. Arnon, Copper enzymes in isolated chloroplasts, polyphenoloxidase in beta vulgaris, Plant Physiology. 24(1949)1-15.

DOI: 10.1104/pp.24.1.1

[3] Y. Zhao, H. Wei, D. Li, X. Liu, X. Zhang, Z. Liu, Research on the technique and instrument of chlorophyll fluorescence measurement, Chinese Journal of Scientific Instrument. 31(2010)1343-1346.

[4] Z. Zhao, X. Li, H. Lei, Y. Li, Application of fusing technology of radar and multispectral images on ORE exploration, Geology and Prospecting. 43(2007)82-87.

[5] J.H. Rouse, J.A. Shaw, R.L. Lawrence, J.L. Lewicki, L.M. Dobeck, K.S. Repasky, L.H. Spangler, Multi-spectral imaging of vegetation for detecting CO2 leaking from underground. Environ Earth Sci. 60(2010)313–323.

DOI: 10.1007/s12665-010-0483-9

[6] F. Deiss, N. Sojic , D.J. White, P.R. Stoddart. Nanostructured optical fibre arrays for high-density biochemical sensing and remote imaging. Anal Bioanal Chem. 396(2010) 53-71.

DOI: 10.1007/s00216-009-3211-0

[7] H. Yang, G. Lv, Determination of pear leaf Nitrogen content based on multi-spectral imaging technology and multivariate calibration. Key Engineering Materials. 467(2011) 718-724.

DOI: 10.4028/

[8] M. Steele, A.A. Gitelson, D. Rundquist, Nondestructive estimation of leaf chlorophyll content in grapes. American Journal of Enology and Viticulture. 59(2008)299-305.

[9] P. Chen, G. Fedosejevs, M. T. O-Lopez, J. G. Arnold, Assessment of modis-EVI, modis-NDVI and vegetation-NDVI composite data using agricultural measurements: an example at corn field in western Mexico. Environmental Monitoring and Assessment 119(2006).

DOI: 10.1007/s10661-005-9006-7

[10] Information on http: /www. colorbasics. com/CIESystem.

[11] L. Zhang, C. Wang, The method of image segmentation based on HIS Color Space and multi-scale characteristics, Journal of Linyi Teachers' College. 27(2005)111-114.

[12] Information on http: /en. wikipedia. org/wiki/Linear_regression.

[13] Z. Rong, B. Dan, J. Yi, A BP Neural Network predictor model for desulfurizing molten iron, Lecture Notes in Computer Science. 3584(2005)728-736.

DOI: 10.1007/11527503_86

[14] Information on http: /plantphys. info/plant_physiology/light. shtml.

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