Identification of soybean varieties by terahertz spectroscopy and integrated learning method

Luo, Hui; Zhu, Jianpeng; Xu, Wen-Ning; Cui, Mengjie

doi:10.1016/j.ijleo.2019.02.148

Cited by 22 publications

(7 citation statements)

References 17 publications

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“…This result agrees with the work of other researchers [49,50]. However, in some other studies, MSC was not the optimal pretreatment method; rather, these studies reported optimal pretreatment methods of median filter smoothing [51], SG [52] t-distributed stochastic neighborhood embedding (t-SNE) [53], and SNV [54,55]. In the hyperspectral identification of wheat [56] and maize [57] seed varieties, the best models for identifying seed varieties were the 5 point and 3 time smoothing and SNV.…”

Section: Resultssupporting

confidence: 91%

Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology

Zhu

Chao

Zhang

et al. 2019

Sensors

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Hyperspectral imaging is a nondestructive testing technology that integrates spectroscopy and iconology technologies, which enables us to quickly obtain both internal and external information of objects and identify crop seed varieties. First, the hyperspectral images of ten soybean seed varieties were collected and the reflectance was obtained. Savitzky-Golay smoothing (SG), first derivative (FD), standard normal variate (SNV), fast Fourier transform (FFT), Hilbert transform (HT), and multiplicative scatter correction (MSC) spectral reflectance pretreatment methods were used. Then, the feature wavelengths and feature information of the pretreated spectral reflectance data were extracted using competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA), and principal component analysis (PCA). Finally, 5 classifiers, Bayes, support vector machine (SVM), k-nearest neighbor (KNN), ensemble learning (EL), and artificial neural network (ANN), were used to identify seed varieties. The results showed that MSC-CARS-EL had the highest accuracy among the 90 combinations, with training set, test set, and 5-fold cross-validation accuracies of 100%, 100%, and 99.8%, respectively. Moreover, the contribution of spectral pretreatment to discrimination accuracy was higher than those of feature extraction and classifier selection. Pretreatment methods determined the range of the identification accuracy, feature-selective methods and classifiers only changed within this range. The experimental results provide a good reference for the identification of other crop seed varieties.

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Section: Resultssupporting

confidence: 91%

Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology

Zhu

Chao

Zhang

et al. 2019

Sensors

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“…THz spectroscopy combined with chemometric methods can be used for seed classification, such as the identification of transgenic seeds [76] and seed variety classification [77]. As early as 2005, Lu et al [78] used THz-TDS and THz spectral imaging to identify corn seeds and processed THz images of samples with spatial pattern analysis.…”

Section: Seed Classificationmentioning

confidence: 99%

Applications of THz Spectral Imaging in the Detection of Agricultural Products

et al. 2021

Photonics

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Agricultural products need to be inspected for quality and safety, and the issue of safety of agricultural products caused by quality is frequently investigated. Safety testing should be carried out before agricultural products are consumed. The existing technologies for inspecting agricultural products are time-consuming and require complex operation, and there is motivation to develop a rapid, safe, and non-destructive inspection technology. In recent years, with the continuous progress of THz technology, THz spectral imaging, with the advantages of its unique characteristics, such as low energies, superior spatial resolution, and high sensitivity to water, has been recognized as an efficient and feasible identification tool, which has been widely used for the qualitative and quantitative analyses of agricultural production. In this paper, the current main performance achievements of the use of THz images are presented. In addition, recent advances in the application of THz spectral imaging technology for inspection of agricultural products are reviewed, including internal component detection, seed classification, pesticide residues detection, and foreign body and packaging inspection. Furthermore, machine learning methods applied in THz spectral imaging are discussed. Finally, the existing problems of THz spectral imaging technology are analyzed, and future research directions for THz spectral imaging technology are proposed. Recent rapid development of THz spectral imaging has demonstrated the advantages of THz radiation and its potential application in agricultural products. The rapid development of THz spectroscopic imaging combined with deep learning can be expected to have great potential for widespread application in the fields of agriculture and food engineering.

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“…Therefore, rapid identification of soybean seed varieties plays an essential role in agricultural products, standardization of seed quality, and food processing. It becomes more and more crucial to build a general discriminant model for distinguishing different soybean seed varieties with large amounts but little difference (Luo et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Fast Identification of Soybean Seed Varieties Using Laser-Induced Breakdown Spectroscopy Combined With Convolutional Neural Network

Liu

et al. 2021

Front. Plant Sci.

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Soybean seed purity is a critical factor in agricultural products, standardization of seed quality, and food processing. In this study, laser-induced breakdown spectroscopy (LIBS) as an effective technology was successfully used to identify ten varieties of soybean seeds. We improved the traditional sample preparation scheme for LIBS. Instead of grinding and squashing, we propose a time-efficient method by pressing soybean seeds into rubber sand filled with culture plates through a ruler to ensure a relatively uniform surface height. In our experimental scheme, three LIBS spectra were finally collected for each soybean seed. A majority vote based on three spectra was applied as the final decision judging the attribution of a single soybean seed. The results showed that the support vector machine (SVM) obtained the optimal identification accuracy of 90% in the prediction set. In addition, PCA-ResNet (propagation coefficient adaptive ResNet) and PCSA-ResNet (propagation coefficient synchronous adaptive ResNet) were designed based on typical ResNet structure by changing the way of self-adaption of propagation coefficients. Combined with a new form of input data called spectral matrix, PCSA-ResNet obtained the optimal performance with the discriminate accuracy of 91.75% in the prediction set. T-distributed stochastic neighbor embedding (t-SNE) was used to visualize the clustering process of the extracted features by PCSA-ResNet. For the interpretation of the good performance of PCSA-ResNet coupled with the spectral matrix, saliency maps were further applied to visually show the pixel positions of the spectral matrix that had a significant influence on the discrimination results, indicating that the content and proportion of elements in soybean seeds could reflect the variety differences.

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Identification of soybean varieties by terahertz spectroscopy and integrated learning method

Cited by 22 publications

References 17 publications

Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology

Identification of Soybean Seed Varieties Based on Hyperspectral Imaging Technology

Applications of THz Spectral Imaging in the Detection of Agricultural Products

Fast Identification of Soybean Seed Varieties Using Laser-Induced Breakdown Spectroscopy Combined With Convolutional Neural Network

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