Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B8, 237-241, 2012
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B8/237/2012/
doi:10.5194/isprsarchives-XXXIX-B8-237-2012
© Author(s) 2012. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
28 Jul 2012
ESTIMATING BIOCHEMICAL PARAMETERS OF TEA (CAMELLIA SINENSIS (L.)) USING HYPERSPECTRAL TECHNIQUES
M. Bian1,2, A. K. Skidmore2, M. Schlerf2, Y. Liu3, and T. Wang2 1School of Remote Sensing and Information Engineering, Wuhan University, 129 LuoYuRoad, Wuhan, 430079, P.R. China
2Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE, Enschede, The Netherlands
3School of Resource and Environmental Science, Wuhan University, 129 LuoYuRoad, Wuhan, 430079, P.R. China
Keywords: Agriculture, Quality, Hyper spectral, Estimation, Statistics 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.
Conference paper (PDF, 709 KB)


Citation: Bian, M., Skidmore, A. K., Schlerf, M., Liu, Y., and Wang, T.: ESTIMATING BIOCHEMICAL PARAMETERS OF TEA (CAMELLIA SINENSIS (L.)) USING HYPERSPECTRAL TECHNIQUES, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B8, 237-241, doi:10.5194/isprsarchives-XXXIX-B8-237-2012, 2012.

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