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 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 (R2) and the lowest root mean squared error of cross validation (RMSECV) values were obtained in the GA‐PLS models for both datasets (R2 = 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.