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

Abstract This study investigated the ability of a field hyperspectral radiometer (400–2350 nm) and genetic algorithm‐based partial least squares ( GA ‐ PLS ) regression to estimate legume content in a mixed sown pasture in H okkaido, J apan. Canopy reflectance data and plant samples were obtained from 50 selected sites in the spring ( M ay) 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 20 nm of previously known wavelength regions.