Journal
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
Volume 172, Issue -, Pages 188-193Publisher
ELSEVIER
DOI: 10.1016/j.chemolab.2017.12.010
Keywords
Stacked auto-encoders; Fully-connected neural network; Deep learning; Nitrogen concentration; Oilseed rape; Nondestructive detection
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Funding
- Zhejiang Provincial Natural Science Foundation of China [LY15C190011]
- National Natural Science Foundation of China [31201446, 31402352]
- Ningbo Science and Technology Special Project of China [2017C110002]
- Ningbo Peoples Livelihood Science and Technology Project of China [2013C11026]
- Zhejiang Education Department [Y201738720]
- Ningbo Natural Science Foundation [2015A610131]
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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 R-p(2) = 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.
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