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Tea (Camellia Sinensis (L.)) is an important economic crop and the market price of tea depends largely on its quality. This researchaims to explore the potential of hyperspectral remote sensing on predicting the concentration of biochemical components, namelytotal tea polyphenols, as indicators of tea quality at canopy scale. Experiments were carried out for tea plants growing in the field andgreenhouse. Partial least squares regression (PLSR), which has proven to be the one of the most successful empirical approach, wasperformed 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 networkswas developed and applied to detect the concentration of total tea polyphenols for one tea variety, in order to explore and modelcomplex nonlinearity relationships between independent (wavebands) and dependent (biochemicals) variables. The good predictionaccuracies (r2 > 0.8 and relative RMSEP < 10 %) achieved for tea plants using both linear (partial lease squares regress) andnonlinear (artificial neural networks) modelling approaches in this study demonstrates the feasibility of using airborne and spacebornesensors to cover wide areas of tea plantation for in situ monitoring of tea quality cheaply and rapidly

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

ESTIMATING BIOCHEMICAL PARAMETERS OF TEA (CAMELLIA SINENSIS (L.)) USING HYPERSPECTRAL TECHNIQUES