Detection of viability of soybean seed based on fluorescence hyperspectra and CARS‐SVM‐AdaBoost model

Li, Yating; Sun, Jun; Wu, Xiaohong; Chen, Quansheng; Lu, Bing; Dai, Chunxia

doi:10.1111/jfpp.14238

Cited by 36 publications

(22 citation statements)

References 37 publications

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“…In the current study, the greatest autofluorescence-spectral signals shown in non-aged soybean seeds corroborates previous studies of Li et al 32 who verified that the average spectral fluorescence of viable soybean seeds (by 365 nm excitation) was higher in relation to non-viable seeds. Moreover, we developed models based on ANN, SVM- l and LDA algorithms and full excitation-emission combinations, and the models presented an excellent performance for soybean seed quality classification (0.99 accuracy).…”

Section: Discussionsupporting

confidence: 92%

Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality

Silva

Oliveira

Carvalho

et al. 2021

Sci Rep

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In the agricultural industry, advances in optical imaging technologies based on rapid and non-destructive approaches have contributed to increase food production for the growing population. The present study employed autofluorescence-spectral imaging and machine learning algorithms to develop distinct models for classification of soybean seeds differing in physiological quality after artificial aging. Autofluorescence signals from the 365/400 nm excitation-emission combination (that exhibited a perfect correlation with the total phenols in the embryo) were efficiently able to segregate treatments. Furthermore, it was also possible to demonstrate a strong correlation between autofluorescence-spectral data and several quality indicators, such as early germination and seed tolerance to stressful conditions. The machine learning models developed based on artificial neural network, support vector machine or linear discriminant analysis showed high performance (0.99 accuracy) for classifying seeds with different quality levels. Taken together, our study shows that the physiological potential of soybean seeds is reduced accompanied by changes in the concentration and, probably in the structure of autofluorescent compounds. In addition, altering the autofluorescent properties in seeds impact the photosynthesis apparatus in seedlings. From the practical point of view, autofluorescence-based imaging can be used to check modifications in the optical properties of soybean seed tissues and to consistently discriminate high-and low-vigor seeds.

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Section: Discussionsupporting

confidence: 92%

Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality

Silva

Oliveira

Carvalho

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Competitive adaptive reweighted sampling (CARS; Li et al, 2019;Huo et al, 2019) was a variable selection method that imitated the principle of "survival of the fittest." In this method, the variable of each wavelength was taken as a single individual one by one.…”

Section: Competitive Adaptive Reweighted Samplingmentioning

confidence: 99%

Identification of Lycium barbarum varieties based on hyperspectral imaging technique and competitive adaptive reweighted sampling‐whale optimization algorithm‐support vector machine

Tang

Sun

Yao

et al. 2020

J Food Process Engineering

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In order to distinguish different varieties of Lycium barbarum effectively, a fast nondestructive detection method based on hyperspectral imaging technology was proposed. Six varieties of L. barbarum were selected as the research objects to obtain hyperspectral images. With the whole L. barbarum taken as the object, the region of interest was obtained by threshold segmentation, and the average spectra value of the image points of a single L. barbarum was extracted as the spectral data of the sample. Initially, standard normalized variate was used to preprocess the original spectral data. Furthermore, compared with other methods, competitive adaptive reweighted sampling (CARS) was chosen to extract the characteristic wavelengths. Additionally, the model of support vector machine (SVM) was set. The results showed that the SVM model based on CARS had the best classification effect. The training set accuracy was 100%, and the prediction set accuracy was 85%. Finally, in order to improve the classification accuracy, the whale optimization algorithm (WOA) was introduced. The accuracy of training set and prediction set obtained by WOA‐SVM model were 89.44 and 88.33% respectively. Therefore, it was feasible to use hyperspectral imaging technology combined with CARS‐WOA‐SVM model to identify different varieties of L. barbarum.

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“…CARS (Cao et al, 2020;Li et al, 2019) is a method of characteristic wavelength selection based on Monte Carlo sampling and PLS regression coefficients. In CARS, first, the corresponding PLS model is established Note: "k" represented the number of modal; "a," "b," "c," "d," "e," and "f" represented one of the modals; and Monte Carlo was used to evaluate the identification accuracy.…”

Section: Feature Extractionmentioning

confidence: 99%

Identification of fumigated and dyedLycium barbarumby hyperspectral imaging technology

Tang

Sun

et al. 2021

J Food Process Engineering

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In view of the adulteration of Lycium barbarum with chemicals and dyes in the market, hyperspectral imaging technology is proposed to identify fumigated and dyed specimens. The hyperspectral data were preprocessed by visual molecular dynamics (VMD) to reduce the influence of noise. Furthermore, competitive adaptive reweighted sampling (CARS) was used to select the characteristic bands from the preprocessed spectral data. Since the classification effect of the traditional support vector machine (SVM) model was influenced by the kernel parameters c and g, the slime mold algorithm was adopted to optimize SVM. The optimal c and g were set to 93.3603 and 4.5652, respectively. The results showed that the VMD-CARS-SVM had higher prediction accuracy, and the accuracy of training and testing sets were 98.2 and 96.7% separately.

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Detection of viability of soybean seed based on fluorescence hyperspectra and CARS‐SVM‐AdaBoost model

Cited by 36 publications

References 37 publications

Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality

Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality

Identification of Lycium barbarum varieties based on hyperspectral imaging technique and competitive adaptive reweighted sampling‐whale optimization algorithm‐support vector machine

Identification of fumigated and dyedLycium barbarumby hyperspectral imaging technology

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