Genetic algorithm-based partial least squares regression for estimating legume content in a grass-legume mixture using field hyperspectral measurements

摘要

This study investigated the ability of a field hyperspectral radiometer (400-2350nm) and genetic algorithm-based partial least squares (GA-PLS) regression to estimate legume content in a mixed sown pasture in Hokkaido, Japan. Canopy reflectance data and plant samples were obtained from 50 selected sites in the spring (May) and summer (July) of 2007 (n=100). The predictive accuracy of GA-PLS was compared with that of multiple linear regression (MLR) and of standard full-spectrum PLS (FS-PLS) for the spring and summer datasets. Overall, the highest coefficient of determination (R-2) and the lowest root mean squared error of cross validation (RMSECV) values were obtained in the GA-PLS models for both datasets (R-2=0.72-0.86, RMSECV=4.10-5.73%). Selected hyperspectral wavebands in the GA-PLS models did not perfectly match wavelengths identified previously using MLR, but in most cases, they were within 20nm of previously known wavelength regions.