Estimating biochemical parameters of tea (Camellia Sinensis (L.)) using hyperspectral techniques

Meng Bian*, Andrew K. Skidmore, Martin Schlerf, Yanfang Liu, Tiejun Wang

*Corresponding author for this work

Research output: Contribution to journalConference paper

3 Citations (Scopus)
22 Downloads (Pure)


Tea (Camellia Sinensis (L.)) is an important economic crop and the market price of tea depends largely on its quality. This research aims to explore the potential of hyperspectral remote sensing on predicting the concentration of biochemical components, namely total tea polyphenols, as indicators of tea quality at canopy scale. Experiments were carried out for tea plants growing in the field and greenhouse. Partial least squares regression (PLSR), which has proven to be the one of the most successful empirical approach, was performed to establish the relationship between reflectance and biochemical concentration across six tea varieties in the field. Moreover, a novel integrated approach involving successive projections algorithms as band selection method and neural networks was developed and applied to detect the concentration of total tea polyphenols for one tea variety, in order to explore and model complex nonlinearity relationships between independent (wavebands) and dependent (biochemicals) variables. The good prediction accuracies (r2 >0.8 and relative RMSEP < 10 %) achieved for tea plants using both linear (partial lease squares regress) and nonlinear (artificial neural networks) modelling approaches in this study demonstrates the feasibility of using airborne and spaceborne sensors to cover wide areas of tea plantation for in situ monitoring of tea quality cheaply and rapidly.

Original languageEnglish
Pages (from-to)237-241
Number of pages5
JournalThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publication statusPublished - 2012
Externally publishedYes
EventXXII ISPRS Congress, Technical Commission VIII - Melbourne, Australia
Duration: 25 Aug 20121 Sep 2012
Conference number: 22nd

Bibliographical note

Copyright the Author(s) 2012. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • Agriculture
  • Quality
  • Hyper spectral
  • Estimation
  • Statistics

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