Development of a Near‐Infrared Imaging System for Determination of Rice Moisture

Lin, Luke; Lu, Faming; Chang, Yung-Chiung

doi:10.1094/cc-83-0498

Cited by 13 publications

(7 citation statements)

References 18 publications

(17 reference statements)

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“…In addition, the detections of vitality and vigor for seeds of oat [17], muskmelon [18], soybean [19,20] and watermelon [21] were developed with the HSI technique. Previous studies have shown the potential of using HSI coupled with multivariate data analysis for the detection of internal conditions of rice seeds, such as origin [22], variety [23,24,25], nitrogen content [26], moisture content [27] and heavy metal concentration [28]. To the best of our knowledge, many studies were conducted only for vitality detection of artificially aged seed, and, so far, no study has been carried out to detect the vitality of rice seeds under natural ageing conditions by using HSI, even though the results obtained from natural ageing seeds were more consistent with the actual situation.…”

Section: Introductionmentioning

confidence: 99%

Rapid and Nondestructive Measurement of Rice Seed Vitality of Different Years Using Near-Infrared Hyperspectral Imaging

Feng

Sun

et al. 2019

Molecules

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Seed vitality is one of the primary determinants of high yield that directly affects the performance of seedling emergence and plant growth. However, seed vitality may be lost during storage because of unfavorable conditions, such as high moisture content and temperatures. It is therefore vital for seed companies as well as farmers to test and determine seed vitality to avoid losses of any kind before sowing. In this study, near-infrared hyperspectral imaging (NIR-HSI) combined with multiple data preprocessing methods and classification models was applied to identify the vitality of rice seeds. A total of 2400 seeds of three different years: 2015, 2016 and 2017, were evaluated. The experimental results show that the NIR-HSI technique has great potential for identifying vitality and vigor of rice seeds. When detecting the seed vitality of the three different years, the extreme learning machine model with Savitzky–Golay preprocessing could achieve a high classification accuracy of 93.67% by spectral data from only eight wavebands (992, 1012, 1119, 1167, 1305, 1402, 1629 and 1649 nm), which could be developed for a fast and cost-effective seed-sorting system for industrial online application. When identifying non-viable seeds from viable seeds of different years, the least squares support vector machine model coupled with raw data and selected wavelengths of 968, 988, 1204, 1301, 1409, 1463, 1629, 1646 and 1659 nm achieved better classification performance (94.38% accuracy), and could be adopted as an optimal combination to identify non-viable seeds from viable seeds.

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Section: Introductionmentioning

confidence: 99%

Rapid and Nondestructive Measurement of Rice Seed Vitality of Different Years Using Near-Infrared Hyperspectral Imaging

Feng

Sun

et al. 2019

Molecules

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“…Hyperspectral imaging technology is a new non-destructive test method which combines imaging information and spectral information [9,10,11,12,13]. Hyperspectral imaging can obtain the chemical information of heterogeneous samples and the spatial distribution of chemical components [14,15,16,17,18,19,20]. The hyperspectral imaging can be used to study the quality of seeds.…”

Section: Introductionmentioning

confidence: 99%

Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging

et al. 2018

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Seed aging during storage is irreversible, and a rapid, accurate detection method for seed vigor detection during seed aging is of great importance for seed companies and farmers. In this study, an artificial accelerated aging treatment was used to simulate the maize kernel aging process, and hyperspectral imaging at the spectral range of 874–1734 nm was applied as a rapid and accurate technique to identify seed vigor under different accelerated aging time regimes. Hyperspectral images of two varieties of maize processed with eight different aging duration times (0, 12, 24, 36, 48, 72, 96 and 120 h) were acquired. Principal component analysis (PCA) was used to conduct a qualitative analysis on maize kernels under different accelerated aging time conditions. Second-order derivatization was applied to select characteristic wavelengths. Classification models (support vector machine−SVM) based on full spectra and optimal wavelengths were built. The results showed that misclassification in unprocessed maize kernels was rare, while some misclassification occurred in maize kernels after the short aging times of 12 and 24 h. On the whole, classification accuracies of maize kernels after relatively short aging times (0, 12 and 24 h) were higher, ranging from 61% to 100%. Maize kernels with longer aging time (36, 48, 72, 96, 120 h) had lower classification accuracies. According to the results of confusion matrixes of SVM models, the eight categories of each maize variety could be divided into three groups: Group 1 (0 h), Group 2 (12 and 24 h) and Group 3 (36, 48, 72, 96, 120 h). Maize kernels from different categories within one group were more likely to be misclassified with each other, and maize kernels within different groups had fewer misclassified samples. Germination test was conducted to verify the classification models, the results showed that the significant differences of maize kernel vigor revealed by standard germination tests generally matched with the classification accuracies of the SVM models. Hyperspectral imaging analysis for two varieties of maize kernels showed similar results, indicating the possibility of using hyperspectral imaging technique combined with chemometric methods to evaluate seed vigor and seed aging degree.

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“…Previous research studies on rice have used the near-infrared reflectance spectroscopy (NIRS) to predict various characteristics of rice products, such as apparent amylose content (e.g., Delwiche et al 1995;Shi 2004, 2007), protein content (e.g., Rittiron et al 2005;Sohn et al 2004), moisture content (e.g., Kawamura and Itoh 1999;Lin et al 2006), amino acid (e.g., Wu et al 2002), starch quality (e.g., Bao et al 2001Bao et al , 2007, cooking time (e.g., Sinelli et al 2006), and alcoholic degree (e.g., Yu et al 2006) with high accuracy. Accordingly, NIRS is not limit to predict the quality estimation of rice but also was a useful tool for the measurement milling quality.…”

Section: Introductionmentioning

confidence: 99%

Milling quality assessment of Khao Dok Mali 105 milled rice by near-infrared reflectance spectroscopy technique

Srikham

Noomhorm

2015

J Food Sci Technol

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The objective of this study was to assess milling quality of Khao Dok Mali 105 milled rice. The physicochemical properties of milled rice can be analyzed in values of milling quality as degree of milling (DOM), surface lipid content (SLC), color as L*, a*and b*, and the whiteness index using various kinds of measuring instrument. However in this study, the calibration models were developed using only the near infrared reflectance spectroscopy (NIRS) to determine the various physicochemical properties at wavelengths between 1,100 and 2,500 nm. The signal pretreatment and partial least square (PLS) regression were used to validate the models for the measurement of DOM, SLC, L*, a*, b* and whiteness index. Six calibration models of these properties were optimized based on the cross validation correlations (Rcv), standard error of cross validation (SECV), the external validation correlations (Rv) and standard error of prediction (SEP). It was found that the Rcv for these six models were 0.98, 0.99, 0.98, 0.95, 0.85 and 0.98 while the Rv for those were 0.99, 0.99, 0.98, 0.96, 0.88 and 0.99, the correlation were closed to 1 which shown that the agreement between data and the models were satisfied. In the meantime, the SECV and the SEP of these six calibration models shew less fluctuation of data and models. As the results, the calibration models developed in this study can be used to predict the milling quality of KDML 105 milled rice. The relationships between DOM and the five parameters were also investigated in this study.

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Development of a Near‐Infrared Imaging System for Determination of Rice Moisture

Cited by 13 publications

References 18 publications

Rapid and Nondestructive Measurement of Rice Seed Vitality of Different Years Using Near-Infrared Hyperspectral Imaging

Rapid and Nondestructive Measurement of Rice Seed Vitality of Different Years Using Near-Infrared Hyperspectral Imaging

Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging

Milling quality assessment of Khao Dok Mali 105 milled rice by near-infrared reflectance spectroscopy technique

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