A deep learning method for predicting lead content in oilseed rape leaves using fluorescence hyperspectral imaging

•FHSI was applied to the detection of lead content in oilseed rape leaves.•WT-SDAE model was proposed for deep feature extraction.•The WT-SDAE-SVR model achieved high-precision prediction of Pb concentration. The purpose of this study was to develop a deep learning method involving wavelet transform...

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Veröffentlicht in:	Food chemistry Jg. 409; S. 135251
Hauptverfasser:	Zhou, Xin, Zhao, Chunjiang, Sun, Jun, Cao, Yan, Yao, Kunshan, Xu, Min
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England Elsevier Ltd 30.05.2023
Schlagworte:	Algorithms Brassica napus Deep Learning Fluorescence hyperspectral imaging Heavy metal lead Hyperspectral Imaging Least-Squares Analysis Nondestructive testing Oilseed rape Plant Leaves Spectroscopy, Near-Infrared - methods Stacked denoising autoencoder Wavelet transform Oilseed rape Wavelet transform Heavy metal lead Stacked denoising autoencoder Nondestructive testing Fluorescence hyperspectral imaging
ISSN:	0308-8146, 1873-7072, 1873-7072
Online-Zugang:	Volltext
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