Determination of viability of Retinispora (Hinoki cypress) seeds using FT-NIR spectroscopy

Wakholi, Collins; Mo, Changyeun; Oh, Mirae; Joo, Hye-Joon; Suh, Hyun Kwon; Cho, Byoung‐Kwan

doi:10.1016/j.infrared.2019.02.008

Cited by 13 publications

(12 citation statements)

References 15 publications

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“…The use of NIR spectroscopy methods combined with X-ray imaging can provide both seed chemical composition and physical integrity measurements [10,[14][15][16][17]. The NIR spectra comprise bands of higher wavelengths arising from overlapping absorptions corresponding to chemistry bond combinations such as C-H, O-H, and N-H [3].…”

Section: Discussionmentioning

confidence: 99%

“…FT-NIR spectroscopy is based on the absorption of electromagnetic radiation at wavelengths ranging from 780 to 2500 nm [13]. Therefore, it offers versatility for direct and simultaneous measurements of several constituents in seed samples [10,[14][15][16][17]. On the other hand, X-ray imaging is based on differences in X-ray attenuation in different types of tissues [18].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning for Seed Quality Classification: An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and X-ray Imaging

Medeiros

Silva

Ribeiro

et al. 2020

Sensors

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Optical sensors combined with machine learning algorithms have led to significant advances in seed science. These advances have facilitated the development of robust approaches, providing decision-making support in the seed industry related to the marketing of seed lots. In this study, a novel approach for seed quality classification is presented. We developed classifier models using Fourier transform near-infrared (FT-NIR) spectroscopy and X-ray imaging techniques to predict seed germination and vigor. A forage grass (Urochloa brizantha) was used as a model species. FT-NIR spectroscopy data and radiographic images were obtained from individual seeds, and the models were created based on the following algorithms: linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), random forest (RF), naive Bayes (NB), and support vector machine with radial basis (SVM-r) kernel. In the germination prediction, the models individually reached an accuracy of 82% using FT-NIR data, and 90% using X-ray data. For seed vigor, the models achieved 61% and 68% accuracy using FT-NIR and X-ray data, respectively. Combining the FT-NIR and X-ray data, the performance of the classification model reached an accuracy of 85% to predict germination, and 62% for seed vigor. Overall, the models developed using both NIR spectra and X-ray imaging data in machine learning algorithms are efficient in quickly, non-destructively, and accurately identifying the capacity of seed to germinate. The use of X-ray data and the LDA algorithm showed great potential to be used as a viable alternative to assist in the quality classification of U. brizantha seeds.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning for Seed Quality Classification: An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and X-ray Imaging

Medeiros

Silva

Ribeiro

et al. 2020

Sensors

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“…The partial least squares discriminant analysis (PLS-DA) technique was applied to develop classification models for the viable and nonviable Hinoki cypress seeds. Use of the PLS-DA model was previously reported in this study to determine the viability of Retinispora seeds using FT-NIR spectroscopy, 21 and thus, it is recommended because of its simplicity and high accuracy.…”

Section: Model Building and Analysismentioning

confidence: 99%

“…[17][18][19][20] In a previous study, Fourier transform near infrared (FT-NIR) spectroscopy was employed as a nondestructive technique to distinguish viable from nonviable seeds. 21 However, FT-NIR is a pointbased scanning technique and examines a relatively small area of the seed, therefore failing to provide spatial information that is important for many seed inspection applications. The alternative is to use HSI, which is a more feasible method for the large-scale discrimination of seeds based on their internal factors.…”

Section: Introductionmentioning

confidence: 99%

Determination of the viability of retinispora (Hinoki cypress) seeds using shortwave infrared hyperspectral imaging spectroscopy

Wakholi

Mohammad

Park

et al. 2020

Journal of Near Infrared Spectroscopy

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The combination of hyperspectral imaging with multivariate data analysis methods has recently been applied to develop a nondestructive technique, required to determine the seed viability of artificially aged vegetable and cereal seeds. In this study, the potential of shortwave infrared hyperspectral imaging to determine the viability of naturally aged seeds was investigated and thereafter a model for online seed sorting system was developed. The hyperspectral images of 400 Hinoki cypress tree seeds were acquired, and germination tests were conducted for viability confirmation, which indicated 31.5% of the viable seeds. Partial least square discriminant analysis models with 179 variables in the wavelength region of 1000–1800 nm were developed with a maximum model accuracy of 98.4% and 93.8% in both the calibration and validation sets, respectively. The partial least square discriminant analysis beta coefficient revealed the key wavelengths to differentiate viable from nonviable seeds, determined based on the differences in the chemical compositions of the seeds, including their lipid and fatty acid contents, which may control the germination ability of the seeds. The most effective wavelengths were selected using two model-based variable selection methods (i.e., the variable importance of projection (15 variables) and the successive projections algorithm (8 variables)) to develop the model. The successive projections algorithm wavelength selection method was considered to develop a viability model, and its application to the raw data resulted in a prediction accuracy of 94.7% in the calibration set and 92.2% in the validation set. These results demonstrate the potential of shortwave infrared hyperspectral imaging spectroscopy as a powerful nondestructive method to determine the viability of Hinoki cypress seeds. This method could be applied to develop an online seed sorting system for seed companies and nurseries.

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“…They operate in different optical ranges, are designed on a modular basis and have the ability not only to diagnose, but also to separate [3,4] seeds by spectrometric properties [5][6][7]. The bases of these devices are optoelectronic systems [8] allowing, depending on the specific optical index of the seed, to calculate its viability [9][10][11][12][13][14][15]. The speed of any information-computing system depends to a significant extent on the speed of its interface, especially in a situation where it is used to control physical and technical processes in real time [16].…”

Section: Introductionmentioning

confidence: 99%

How to Increase the Analog-to-Digital Converter Speed in Optoelectronic Systems of the Seed Quality Rapid Analyzer

et al. 2019

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This invention is relevant when working as part of optoelectronic systems, including non-destructive quality control of forest seeds. The possibility of synthesis of the ultrafast optical analog-to-digital converter (ADC) providing conversion of analog information to digital in the sub-GHz range is considered. The functional scheme of the optical ADC, containing technologically well-developed optical elements is given; the principle of operation is described in detail. The possibility of increasing the speed of the ADC to make it potentially possible for optical data processing schemes is shown.

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Determination of viability of Retinispora (Hinoki cypress) seeds using FT-NIR spectroscopy

Cited by 13 publications

References 15 publications

Machine Learning for Seed Quality Classification: An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and X-ray Imaging

Machine Learning for Seed Quality Classification: An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and X-ray Imaging

Determination of the viability of retinispora (Hinoki cypress) seeds using shortwave infrared hyperspectral imaging spectroscopy

How to Increase the Analog-to-Digital Converter Speed in Optoelectronic Systems of the Seed Quality Rapid Analyzer

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