Non‐destructive evaluation of bacteria‐infected watermelon seeds using visible/near‐infrared hyperspectral imaging

Lee, Hoonsoo; Kim, Moon S.; Song, Yu-Rim; Oh, Chang‐Sik; Lim, Hyoun–Sub; Lee, Wang‐Hee; Kang, Jum-Soon; Cho, Byoung‐Kwan

doi:10.1002/jsfa.7832

Cited by 43 publications

(27 citation statements)

References 51 publications

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“…Because of these advantages, HSI has been successfully applied in various tasks involving seeds, such as classication of seed varieties, detection of seed vigor, identication of seed diseases. [8][9][10] As far as we know, no research has reported the application of HSI on varieties identication of oat seeds.…”

Section: Introductionmentioning

confidence: 99%

Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network

Zhang

et al. 2019

RSC Adv.

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Section: Introductionmentioning

confidence: 99%

Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network

Zhang

et al. 2019

RSC Adv.

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“…The characteristics of batch detection extend its application in the modern seed industry. Recently, HSI has been used in quality assessment of agricultural seeds, such as for variety and geographical origin identification [ 14 , 15 ], viability and vigor prediction [ 16 ], bacterial and fungal infection [ 17 , 18 ], etc. As for variety discrimination, the number of seeds should be sufficient to contain a broad variation, which will consequently result in high computational cost.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive and Rapid Variety Discrimination and Visualization of Single Grape Seed Using Near-Infrared Hyperspectral Imaging Technique and Multivariate Analysis

et al. 2018

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Hyperspectral images in the spectral range of 874–1734 nm were collected for 14,015, 14,300 and 15,042 grape seeds of three varieties, respectively. Pixel-wise spectra were preprocessed by wavelet transform, and then, spectra of each single grape seed were extracted. Principal component analysis (PCA) was conducted on the hyperspectral images. Scores for images of the first six principal components (PCs) were used to qualitatively recognize the patterns among different varieties. Loadings of the first six PCs were used to identify the effective wavelengths (EWs). Support vector machine (SVM) was used to build the discriminant model using the spectra based on the EWs. The results indicated that the variety of each single grape seed was accurately identified with a calibration accuracy of 94.3% and a prediction accuracy of 88.7%. An external validation image of each variety was used to evaluate the proposed model and to form the classification maps where each single grape seed was explicitly identified as belonging to a distinct variety. The overall results indicated that a hyperspectral imaging (HSI) technique combined with multivariate analysis could be used as an effective tool for non-destructive and rapid variety discrimination and visualization of grape seeds. The proposed method showed great potential for developing a multi-spectral imaging system for practical application in the future.

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“…SVM is a supervised machine learning method, which is efficient to deal with linear and nonlinear data for classification and regression. For classification issues, SVM maps the original data into new feature spaces [ 31 , 32 , 33 ]. According to linearly separable data, a simple linear classifier can be constructed.…”

Section: Methodsmentioning

confidence: 99%

Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging

et al. 2018

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Different varieties of raisins have different nutritional properties and vary in commercial value. An identification method of raisin varieties using hyperspectral imaging was explored. Hyperspectral images of two different varieties of raisins (Wuhebai and Xiangfei) at spectral range of 874–1734 nm were acquired, and each variety contained three grades. Pixel-wise spectra were extracted and preprocessed by wavelet transform and standard normal variate, and object-wise spectra (sample average spectra) were calculated. Principal component analysis (PCA) and independent component analysis (ICA) of object-wise spectra and pixel-wise spectra were conducted to select effective wavelengths. Pixel-wise PCA scores images indicated differences between two varieties and among different grades. SVM (Support Vector Machine), k-NN (k-nearest Neighbors Algorithm), and RBFNN (Radial Basis Function Neural Network) models were built to discriminate two varieties of raisins. Results indicated that both SVM and RBFNN models based on object-wise spectra using optimal wavelengths selected by PCA could be used for raisin variety identification. The visualization maps verified the effectiveness of using hyperspectral imaging to identify raisin varieties.

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Non‐destructive evaluation of bacteria‐infected watermelon seeds using visible/near‐infrared hyperspectral imaging

Abstract: The results of the present study show that it is possible to use the Vis/NIR hyperspectral imaging system for detecting bacteria-infected watermelon seeds. © 2016 Society of Chemical Industry.

Cited by 43 publications

References 51 publications

Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network

Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network

Non-Destructive and Rapid Variety Discrimination and Visualization of Single Grape Seed Using Near-Infrared Hyperspectral Imaging Technique and Multivariate Analysis

Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging

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