Prediction of chlorophyll content of leaves of oil camelliae after being infected with anthracnose based on Vis/NIR spectroscopy

Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis

Volumn 32, Issue 5, 2012, Pages 1221-1224

  Wu, Nan ^a   Liu, Jun Ang ^a   Zhou, Guo Ying ^a   Yan, Rui Kun ^a   Zhang, Lei ^a  

^a CENTRAL SOUTH UNIVERSITY OF FORESTRY AND TECHNOLOGY   (China)

Author keywords

BP neural network; Chlorophyll; Forecast; Oil camellia anthracnose; Vis NIR spectroscopy

Indexed keywords

BP NEURAL NETWORKS; CHLOROPHYLL CONTENTS; CORRELATION COEFFICIENT; DISEASE INDEX; FIELD SURVEYS; FIRST-ORDER DIFFERENTIALS; FORECAST; INPUT VARIABLES; MOVING AVERAGE FILTER; NEAR INFRARED; OIL CAMELLIA ANTHRACNOSE; PREDICTION MODEL; PREDICTIVE VALUES; RED EDGE; RED LIGHT; REFLECTION PEAKS; SPECTRAL DATA; SPECTRAL RESAMPLE; VIS/NIR SPECTROSCOPY; VISIBLE LIGHT; WAVEBANDS;

FORECASTING; MATHEMATICAL MODELS; NEURAL NETWORKS;

CHLOROPHYLL;

CHLOROPHYLL;

ARTIFICIAL NEURAL NETWORK; CAMELLIA; CHEMISTRY; MICROBIOLOGY; NEAR INFRARED SPECTROSCOPY; PLANT DISEASE; PLANT LEAF;

CAMELLIA; CHLOROPHYLL; NEURAL NETWORKS (COMPUTER); PLANT DISEASES; PLANT LEAVES; SPECTROSCOPY, NEAR-INFRARED;

EID: 84862701611     PISSN: 10000593     EISSN: None     Source Type: Journal    
DOI: 10.3964/j.issn.1000-0593(2012)05-1221-04     Document Type: Article

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