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

文献类型： 外文期刊

作者： Zhou, Xin 1 ; Zhao, Chunjiang 1 ; Sun, Jun 1 ; Cao, Yan 1 ; Yao, Kunshan 1 ; Xu, Min 1 ;

作者机构： 1.Jiangsu Univ, Sch Elect & Informat Engn, Zhenjiang 212013, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Natl Engn Lab Agriprod Qual Traceabil, Beijing 100097, Peoples R China

关键词： Stacked denoising autoencoder; Wavelet transform; Heavy metal lead; Oilseed rape; Fluorescence hyperspectral imaging; Nondestructive testing

期刊名称：FOOD CHEMISTRY （ 影响因子：8.8； 五年影响因子：8.6 ）

ISSN： 0308-8146

年卷期： 2023 年 409 卷

页码：

收录情况： SCI

摘要： The purpose of this study was to develop a deep learning method involving wavelet transform (WT) and stacked denoising autoencoder (SDAE) for extracting deep features of heavy metal lead (Pb) detection of oilseed rape leaves. Firstly, the standard normalized variable (SNV) algorithm was established as the best preprocessing al-gorithm, and the SNV-treated fluorescence spectral data was used for further data analysis. Then, WT was used to decompose the SNV-treated fluorescence spectra of oilseed rape leaves to obtain the optimal wavelet decom-position layers using different wavelet basis functions, and SDAE was used for deep feature learning under the optimal wavelet decomposition layer. Finally, the best established support vector machine regression (SVR) model prediction set parameters Rp2, RMSEP and RPD were 0.9388, 0.0199 mg/kg and 3.275 using sym7 as the wavelet basis function. The results of this study verified that the huge potential of fluorescence hyperspectral technology combined with deep learning algorithms to detect heavy metals.

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