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Determination of lead content in oilseed rape leaves in silicon-free and silicon environments based on deep transfer learning and fluorescence hyperspectral imaging

文献类型： 外文期刊

作者： Zhou, Xin 1 ; Zhao, Chunjiang 1 ; Sun, Jun 1 ; Cheng, Jiehong 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 convolution auto-encoder; Deep transfer learning; Silicon environment; Lead; Nondestructive testing

期刊名称：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY （ 影响因子：4.3； 五年影响因子：3.8 ）

ISSN： 1386-1425

年卷期： 2024 年 311 卷

页码：

收录情况： SCI

摘要： The ability of fluorescence hyperspectral imaging to predict heavy metal lead (Pb) concentration in oilseed rape leaves was studied in silicon -free and silicon environments. Further, the transfer stacked convolution autoencoder (T-SCAE) algorithm was proposed based on the stacked convolution auto -encoder (SCAE) algorithm. Fluorescence hyperspectral images of oilseed rape leaves under different Pb stress contents were obtained in the silicon -free and silicon environments. The entire region of oilseed rape leaves was chosen as the region of interest (ROI) to obtain fluorescence spectra. First of all, standard normalized variable (SNV) algorithm was implemented as the preferred preprocessing method, and the fluorescence spectral data processed by SNV was utilized for further analysis. Further, SCAE was used to reduce the dimensionality of the best pre-processed spectral data, and compared with the traditional dimensionality reduction algorithm. Finally, the optimal SCAE deep learning network was transferred to obtain the T-SCAE model to verify the transferability between the deep learning models in silicon -free and silicon environments. The results show that the SVR model based on the depth features extracted by SCAE has the best performance in predicting different Pb concentrations in silicon -free or silicon environments, and the coefficient of determination (Rp2), root mean square error (RMSEP) and residual predictive deviation (RPD) of prediction set in silicon -free or silicon environments were 0.9374, 0.02071 mg/kg and 3.268, and 0.9416, 0.01898 mg/kg and 3.316, respectively. Moreover, the SVR model based on the depth feature extracted by T-SCAE has the best performance in predicting different Pb concentrations in silicon -free and silicon environments, and the Rp2, RMSEP and RPD of the optimal prediction set were 0.9385, 0.02017 mg/kg and 3.291, respectively. The combination of hyperspectral fluorescence imaging and deep transfer learning algorithm can effectively detect different Pb concentrations in oilseed rape leaves in both non -silicon environment and silicon environment.

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