A Non-destructive Terahertz Spectroscopy-Based Method for Transgenic Rice Seed Discrimination via Sparse Representation

Hu, Xiaohua; Lang, Wenhui; Liu, Wei; Xue, Xiangxin; Yang, Jian‐Bo; Zheng, Lei

doi:10.1007/s10762-017-0392-z

Cited by 18 publications

(7 citation statements)

References 25 publications

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“…The use of 2D spectral images of rice seeds combined with deep learning algorithms shows great potential for classifying rice seeds which are genetically very similar but with Chromosome Single Segment Substitutions. The results ( Table 2 ) showed that for each algorithm, much more accurate classification was obtained using the terahertz absorption spectra than the NIR ones, perhaps because the terahertz spectra can detect biomolecules ( Kistner et al, 2007 ; Hu et al, 2017 ). The variation in the output of each layer on the visualization map ( Figure 7 ) showed the effectiveness of the deep learning algorithm for identifying the terahertz absorption spectra of the BB resistant rice seeds.…”

Section: Discussionmentioning

confidence: 99%

“…Due to the special position of the terahertz band in the electromagnetic spectrum, the terahertz waves are transient and have high penetration, broadband, coherence, and low energy ( Demidova et al, 2016 ). The terahertz band with a radiant energy level of approximately 1–10 meV corresponds to the low-energy rotational modes or vibration modes of molecules and so provides a means for detecting most biomolecules such as DNA, protein etc ( Hu et al, 2017 ). Terahertz spectroscopy is now widely used in biology ( Mueller-Holtz et al, 2014 ), security ( Shen et al, 2005 ), communication technology ( Nagatsuma et al, 2016 ), and food safety ( Qin et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

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Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network

Zhang

Yang²,

Feng

et al. 2020

Front. Plant Sci.

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Because bacterial blight (BB) disease seriously affects the yield and quality of rice, breeding BB resistant rice is an important priority for plant breeders but the process is time-consuming. The feasibility of using terahertz imaging technology and near-infrared hyperspectral imaging technology to identify BB resistant seeds has therefore been studied. The two-dimensional (2D) spectral images and one-dimensional (1D) spectra provided by both imaging methods were used to build discriminant models based on a deep learning method, the convolutional neural network (CNN), and traditional machine learning methods, support vector machine (SVM), random forest (RF), and partial least squares discriminant analysis (PLS-DA). The highest classification accuracy was achieved by the discriminate model based on CNN using the terahertz absorption spectra. Confusion matrixes were pictured to show the identification details. The t-distributed stochastic neighbor embedding (t-SNE) method was used to visualize the process of CNN data processing. Terahertz imaging technology combined with CNN has great potential to quickly identify BB resistant rice seeds and is more accurate than using near-infrared hyperspectral imaging.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network

Zhang

Yang²,

Feng

et al. 2020

Front. Plant Sci.

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“…Rice (Oryza sativa), as one of the most important food crops, supplies staple foods for more than half of the world's population [1][2][3]. In the world rice market, ailand is the largest rice exporting country over the past decade according to USDA [4], and ai jasmine rice is one of the most popular varieties that command a premium price due to its unique flavor and delicious taste [5].…”

Section: Introductionmentioning

confidence: 99%

Nondestructive Detection of Authenticity of Thai Jasmine Rice Using Multispectral Imaging

Liu

Xue

Liu

et al. 2021

Journal of Food Quality

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The detection of authenticity is essential to the development and management of Thai jasmine rice industry. In this study, the multispectral imaging system (405–970 nm) was used for the detection of adulteration in Thai jasmine rice combined with chemometric methods including principal component analysis (PCA), partial least squares (PLS), least squares-support vector machines (LS-SVM), and backpropagation neural network (BPNN). Three varieties of rice that were similar to Thai jasmine rice in appearance were selected to perform the classification and quantitative prediction experiments by multispectral images. For the classification experiment, four varieties of rice samples could be easily classified with accuracy achieved to 92% by the BPNN model. For the quantitative prediction of adulteration proportion experiments, the results showed that, among the different chemometric methods, LS-SVM achieved the best prediction performance comparing the results of coefficient of determination, root-mean-square error (RMSEP), bias, and residual predictive deviation (RPD). It can be concluded that multispectral imaging technology with chemometric methods can be applied in the rapid and nondestructive detection of authenticity of Thai jasmine rice.

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“…Apart from spectral recognition using various techniques in previous works 14 – 18 , statistical pattern recognition with Feed-Forward Artificial Neuronal Network (ANN), Convolutional Neural Networks (CNN), and Gaussian Mixture Model (GMM) were applied for sorting of recycling plastics by using THz frequency domain data 19 . Classification of breast cancer, if benign or malignant, from biomedical THz images with ANN and K -Nearest Neighbor (KNN) pattern recognition algorithms was also reported 20 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning for pattern and waveform recognitions in terahertz image data

Bulgarevich

Talara

Tani

et al. 2021

Sci Rep

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Several machine learning (ML) techniques were tested for the feasibility of performing automated pattern and waveform recognitions of terahertz time-domain spectroscopy datasets. Out of all the ML techniques under test, it was observed that random forest statistical algorithm works well with the THz datasets in both the frequency and time domains. With such ML algorithm, a classifier can be created with less than 1% out-of-bag error for segmentation of rusted and non-rusted sample regions of the image datasets in frequency domain. The degree of linear correlation between the rusted area percentage and the image spatial resolution with terahertz frequency can be used as an additional cross-validation criteria for the evaluation of classifier quality. However, for different rust staging measured datasets, a standardized procedure of image pre-processing is necessary to create/apply a single classifier and its usage is only limited to 1 ± 0.2 THz. Moreover, random forest is practically the best choice among the several popular ML techniques under test for waveform recognition of time-domain data in terms of classification accuracy and timing. Our results demonstrate the usefulness of random forest and several other machine learning algorithms for terahertz hyperspectral pattern recognition.

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A Non-destructive Terahertz Spectroscopy-Based Method for Transgenic Rice Seed Discrimination via Sparse Representation

Cited by 18 publications

References 25 publications

Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network

Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network

Nondestructive Detection of Authenticity of Thai Jasmine Rice Using Multispectral Imaging

Machine learning for pattern and waveform recognitions in terahertz image data

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