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High-precision identification transgenic sugarcane using active terahertz low-frequency excitation

文献类型： 外文期刊

作者： Tu, Shan 1 ; Song, Zhongzhou 1 ; Pang, Senhao 1 ; He, Qilin 1 ; Su, Jingkai 1 ; Zhang, Cheng 1 ; Xiao, Heng 1 ; Wang, Jungang 2 ; Liu, Xihui 3 ; Zhang, Wentao 4 ; Hu, Junhui 1 ;

作者机构： 1.Guangxi Normal Univ, Sch Phys Sci & Technol, Guangxi Key Lab Nucl Phys & Technol, Guilin 541004, Peoples R China

2.Chinese Acad Trop Agr Sci, Inst Trop Biosci & Biotechnol, Natl Key Lab Trop Crop Breeding, Haikou 571101, Peoples R China

3.Guangxi Acad Agr Sci, Guangxi Key Lab Sugarcane Genet Improvement, Nanning 530007, Peoples R China

4.Guilin Univ Elect & Technol, Guangxi Key Lab Optoelect Informat Proc, Guilin 541004, Peoples R China

关键词： Transgenic sugarcane; THz detection; Feature extraction; Data splitting; Deep Learning

期刊名称：INDUSTRIAL CROPS AND PRODUCTS （ 影响因子：6.2； 五年影响因子：6.2 ）

ISSN： 0926-6690

年卷期： 2025 年 226 卷

页码：

收录情况： SCI

摘要： This study proposes a novel, environmentally friendly, accurate, rapid, and cost-effective technique for the classification and identification of transgenic sugarcane, combining terahertz (THz) time-domain spectroscopy with an Isomap-Deep Convolutional Neural Network (Isomap-DCNN). Different types of transgenic sugarcane derived from the same receptor material exhibit similar THz absorbance spectra, posing significant challenges for accurate categorization. The Tukey window function was first applied to the THz absorbance spectra of five transgenic sugarcane varieties, and the processed data were subsequently used for feature extraction with tdistributed Stochastic Neighbor Embedding (t-SNE), LargeVis (LV), and Isomap. To enhance the robustness and generalization capability of the model, the Kennard-Stone (KS) algorithm was employed to partition the training and testing sets. The processed key "fingerprint" features were then input into a Decision Tree (DT) classifier and a Deep Convolutional Neural Network (DCNN) classifier, respectively. Notably, the Isomap-DCNN model achieved 100 % accuracy in identifying the five transgenic sugarcane varieties. The Isomap-DCNN model, constructed based on THz spectroscopy, is expected to provide a fast, high-precision, environmentally friendly, and cost-effective technique for the classification and identification of transgenic sugarcane. This approach will aid in the development and implementation of regulatory standards, enhance the safety and efficacy of transgenic sugarcane, and promote public transparency regarding information about transgenic sugarcane.

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