Deep-learning-based regression model and hyperspectral imaging for rapid detection of nitrogen concentration in oilseed rape (Brassica napus L.) leaf

Deep-learning-based regression model composed of stacked auto-encoders (SAE) and fully-connected neural network (FNN) was used for the detection and quantification of nitrogen (N) concentration in oilseed rape leaf. SAE was applied to extract deep spectral features from visible and near-infrared (38...

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Bibliographic Details
Published in:Chemometrics and intelligent laboratory systems 2018-01, Vol.172, p.188-193
Main Authors: Yu, Xinjie, Lu, Huanda, Liu, Qiyu
Format: Article
Language:English
Subjects:
Deep learning
Fully-connected neural network
Nitrogen concentration
Nondestructive detection
Oilseed rape
Stacked auto-encoders
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Summary:Deep-learning-based regression model composed of stacked auto-encoders (SAE) and fully-connected neural network (FNN) was used for the detection and quantification of nitrogen (N) concentration in oilseed rape leaf. SAE was applied to extract deep spectral features from visible and near-infrared (380–1030 nm) hyperspectral image of oilseed rape leaf, and then these features were used as input data for FNN to predict N concentration. The SAE-FNN model achieved reasonable performance with R2P = 0.903, RMSEP =0 .307% and RPDP = 3.238 for N concentration. Results confirmed the possibility of rapid and nondestructive detecting N concentration in oilseed rape leaf by the combination of hyperspectral imaging technique and deep learning method. •Hyperspectral imaging could non-destructively detect N in oilseed rape leaf.•Deep spectral features were extracted by SAE.•SAE-FNN model was applied to fit the spectral features to N content.
ISSN:0169-7439
1873-3239
DOI:10.1016/j.chemolab.2017.12.010