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

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)

Abstract

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
Title of host publicationXXII ISPRS Congress, Technical Commission VIII (Volume XXXIX-B8)
Subtitle of host publication25 August – 01 September 2012, Melbourne, Australia
EditorsM. Shortis, H. Shimoda, K. Cho
Place of PublicationMelbourne
PublisherInternational Society for Photogrammetry and Remote Sensing (ISPRS)
Pages237-241
Number of pages5
Volume39
DOIs
Publication statusPublished - 1 Jan 2012
Event22nd Congress of the International Society for Photogrammetry and Remote Sensing, ISPRS 2012 - Melbourne, Australia
Duration: 25 Aug 20121 Sep 2012
Conference number: 22

Publication series

NameInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
PublisherCopernicus Publications
ISSN (Print)1682-1750

Conference

Conference22nd Congress of the International Society for Photogrammetry and Remote Sensing, ISPRS 2012
Abbreviated titleISPRS 2012
CountryAustralia
CityMelbourne
Period25/08/121/09/12

    Fingerprint

Keywords

  • Agriculture
  • Estimation
  • Hyper spectral
  • Quality
  • Statistics
  • ITC-GOLD

Cite this

Bian, M., Skidmore, A. K., Schlerf, M., Liu, Y., & Wang, T. (2012). Estimating biochemical parameters of tea (Camellia Sinensis (L.)) USING hyperspectral techniques. In M. Shortis, H. Shimoda, & K. Cho (Eds.), XXII ISPRS Congress, Technical Commission VIII (Volume XXXIX-B8): 25 August – 01 September 2012, Melbourne, Australia (Vol. 39, pp. 237-241). (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives). Melbourne: International Society for Photogrammetry and Remote Sensing (ISPRS). https://doi.org/10.5194/isprsarchives-XXXIX-B8-237-2012