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Nondestructive detection of cadmium content in oilseed rape leaves under different silicon environments using deep transfer learning and Vis-NIR hyperspectral imaging

文献类型： 外文期刊

作者： Zhou, Xin 1 ; Liu, Yang 1 ; Zhao, Chunjiang 1 ; Sun, Jun 1 ; Shi, Lei 1 ; Cong, Sunli 1 ;

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

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

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

4.Jiangsu Univ, Key Lab Theory & Technol Intelligent Agr Machinery, Zhenjiang 212013, Peoples R China

5.Jiangsu Prov & Educ Minist Cosponsored Synergist I, Zhenjiang 212013, Peoples R China

关键词： Stacked denoising autoencoder; Deep learning; Transfer learning; Silicon environment; Cadmium hyperspectral imaging

期刊名称：FOOD CHEMISTRY （ 影响因子：9.8； 五年影响因子：9.7 ）

ISSN： 0308-8146

年卷期： 2025 年 479 卷

页码：

收录情况： SCI

摘要： In this paper, a transfer stack denoising autoencoder (T-SDAE) algorithm is proposed to implement the migration of cadmium (Cd) prediction depth characteristic model of oilseed rape leaves in different silicon environments. Stacked denoising autoencoder (SDAE) algorithm was used to reduce dimensionality, and the most effective SDAE deep learning network was transferred to create the T-SDAE model. The results showed that SVR model using SDAE to extract depth features had the best prediction effect on Cd content in silicon-free, low-silicon and higher-silicon environments. Moreover, the coefficient of determination of prediction set (Rp2) were 0.9127, 0.9829 and 0.9606, respectively. Specifically, the Rp2 value of the T-SDAE-SVR optimal prediction set under different silicon environments is 0.9273, RMSEP is 0.01465 mg/kg, and RPD is 3.237. By integrating hyperspectral imaging technology with a deep transfer learning algorithm, accurate detection of various Cd contents in oilseed rape leaves is feasible under different silicon environments.

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