Hyperspectral data mining to identify relevant canopy spectral features for estimating durum wheat growth, nitrogen status, and grain yield

Computers and Electronics in Agriculture

Volumn 136, Issue , 2017, Pages 1-12

  Thorp, K R ^a   Wang, G ^b   Bronson, K F ^a   Badaruddin, M ^c   Mon, J ^a  

^a AGRICULTURAL RESEARCH SERVICE   (United States)

^b Bridgestone Americas Agricultural Operations   (United States)

^c UNIVERSITY OF ARIZONA   (United States)

Author keywords

Derivative spectra; Genetic algorithm; Partial least squares regression; Spectral reflectance; Spectroradiometer

Indexed keywords

CULTIVATION; GENETIC ALGORITHMS; GRAIN (AGRICULTURAL PRODUCT); GRAIN GROWTH; LEAST SQUARES APPROXIMATIONS; MEAN SQUARE ERROR; NITROGEN; PLANTS (BOTANY); RADIOMETERS; REFLECTION; SPECTROMETERS;

CANOPY SPECTRAL REFLECTANCE; DERIVATIVE SPECTRA; GENETIC ALGORITHM APPROACH; HYPERSPECTRAL REFLECTANCE; PARTIAL LEAST SQUARES REGRESSION; PARTIAL LEAST SQUARES REGRESSIONS (PLSR); SPECTRAL REFLECTANCES; SPECTRO-RADIOMETERS;

DATA MINING;

CANOPY REFLECTANCE; CROP YIELD; DATA MINING; GENETIC ALGORITHM; LEAST SQUARES METHOD; NUTRITIONAL STATUS; SPECTRAL REFLECTANCE; WHEAT;

ARIZONA; UNITED STATES;

TRITICUM AESTIVUM; TRITICUM TURGIDUM SUBSP. DURUM;

EID: 85014327983     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2017.02.024     Document Type: Article

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