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- Title
Genetic algorithm-based partial least squares regression for estimating legume content in a grass-legume mixture using field hyperspectral measurements.
- Authors
Kawamura, Kensuke; Watanabe, Nariyasu; Sakanoue, Seiichi; Lee, Hyo‐Jin; Lim, Jihyun; Yoshitoshi, Rena
- 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 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.
- Subjects
HOKKAIDO (Japan); RADIOMETERS; GENETIC algorithms; GRASS-legume pastures; REGRESSION analysis; LEGUMES; SPECTRAL reflectance
- Publication
Grassland Science, 2013, Vol 59, Issue 3, p166
- ISSN
1744-6961
- Publication type
Article
- DOI
10.1111/grs.12026