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

• Published in: Chemometrics and intelligent laboratory systems Vol. 172; pp. 188 - 193
• Main Authors: Yu, Xinjie, Lu, Huanda, Liu, Qiyu
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.01.2018
• Subjects: Deep learning; Fully-connected neural network; Nitrogen concentration; Nondestructive detection; Oilseed rape; Stacked auto-encoders; Deep learning; Oilseed rape; Nitrogen concentration; Nondestructive detection; Stacked auto-encoders; Fully-connected neural network

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...