Selection of Optimal Hyperspectral Wavebands for Detection of Discolored, Diseased Rice Seeds

The inspection of rice grain that may be infected by seedborne disease is important for ensuring uniform plant stands in production fields as well as preventing proliferation of some seedborne diseases. The goal of this study was to use a hyperspectral imaging (HSI) technique to find optimal wavelengths and develop a model for detecting discolored, diseased rice seed infected by bacterial panicle blight (Burkholderia glumae), a seedborne pathogen. For this purpose, the HSI data spanning the visible/near-infrared wavelength region between 400 and 1000 nm were collected for 500 sound and discolored rice seeds. For selecting optimal wavelengths to use for detecting diseased seed, a sequential forward selection (SFS) method combined with various spectral pretreatments was employed. To evaluate performance based on optimal wavelengths, support vector machine (SVM) and linear and quadratic discriminant analysis (LDA and QDA) models were developed for detection of discolored seeds. As a result, the violet and red regions of the visible spectrum were selected as key wavelengths reflecting the characteristics of the discolored rice seeds. When using only two or only three selected wavelengths, all of the classification methods achieved high classification accuracies over 90% for both the calibration and validation sample sets. The results of the study showed that only two to three wavelengths are needed to differentiate between discolored, diseased and sound rice, instead of using the entire HSI wavelength regions. This demonstrates the feasibility of developing a low cost multispectral imaging technology based on these selected wavelengths for non-destructive and high-throughput screening of diseased rice seed.

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

Molecules ◽

10.3390/molecules24122227 ◽

2019 ◽

Vol 24 (12) ◽

pp. 2227 ◽

Cited By ~ 10

Author(s):

Xiantao He ◽

Xuping Feng ◽

Dawei Sun ◽

Fei Liu ◽

Yidan Bao ◽

...

Keyword(s):

Hyperspectral Imaging ◽

Near Infrared ◽

Seedling Emergence ◽

High Yield ◽

Support Vector ◽

Rice Seed ◽

Machine Model ◽

Online Application ◽

Rice Seeds ◽

Viable Seeds

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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Near-infrared Spectroscopy and Hyperspectral Imaging for Sugar Content Evaluation in Potatoes over Multiple Growing Seasons

Food Analytical Methods ◽

10.1007/s12161-020-01886-1 ◽

2020 ◽

Author(s):

Ahmed M Rady ◽

Daniel E Guyer ◽

Nicholas J Watson

Keyword(s):

Hyperspectral Imaging ◽

Near Infrared ◽

Multispectral Imaging ◽

Sucrose Concentration ◽

Sugar Content ◽

Imaging Systems ◽

Potato Tubers ◽

Forward Selection ◽

Concentration Data ◽

Growing Seasons

Abstract Sugar content is one of the most important properties of potato tubers as it directly affects their processing and the final product quality, especially for fried products. In this study, data obtained from spectroscopic (interactance and reflectance) and hyperspectral imaging systems were used individually or fused to develop non-cultivar nor growing season-specific regression and classification models for potato tubers based on glucose and sucrose concentration. Data was acquired over three growing seasons for two potato cultivars. The most influential wavelengths were selected from the imaging systems using interval partial least squares for regression and sequential forward selection for classification. Hyperspectral imaging showed the highest regression performance for glucose with a correlation coefficient (ratio of performance to deviation) or r(RPD) of 91.8(2.41) which increased to 94%(2.91) when the data was fused with the interactance data. The sucrose regression results had the highest accuracy using data obtained from the interactance system with r(RPD) values of 74.5%(1.40) that increased to 84.4%(1.82) when the data was fused with the reflectance data. Classification was performed to identify tubers with either high or low sugar content. Classification performance showed accuracy values as high as 95% for glucose and 80.1% for sucrose using hyperspectral imaging, with no noticeable improvement when data was fused from the other spectroscopic systems. When testing the robustness of the developed models over different seasons, it was found that the regression models had r(RPD) values of 55(1.19)–90.3%(2.34) for glucose and 35.8(1.07)–82.2%(1.29) for sucrose. Results obtained in this study demonstrate the feasibility of developing a rapid monitoring system using multispectral imaging and data fusion methods for online evaluation of potato sugar content.

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Deteksi Cepat Viabilitas Benih Padi Menggunakan Gelombang Near Infrared dan Model Jaringan Saraf Tiruan

Jurnal Penelitian Pertanian Tanaman Pangan ◽

10.21082/jpptp.v33n2.2014.p77-86 ◽

2014 ◽

Vol 33 (2) ◽

pp. 77

Author(s):

Jonni Firdaus ◽

Rokhani Hasbullah ◽

Usman Ahmad ◽

M. Rahmad Suhartanto

Keyword(s):

Fatty Acids ◽

Free Fatty Acids ◽

Soluble Protein ◽

Input Data ◽

Near Infrared ◽

Seed Viability ◽

Accelerated Aging ◽

Rice Seed ◽

Ann Models ◽

Rice Seeds

Viability is an important component of seed quality, which could be detained by germinating the seeds. Currently testing the seed viability of rice takes a long time (5-14 days), so it becomes a limiting factor in the seed production process. An alternative method for rapid seed viability detection is using the Near Infrared (NIR) spectra and using artificial neural network (ANN) as a data processing system. This research was aimed to study the use of NIR spectra and ANN to predict the viability of rice seeds. NIR reflectance (1,000-2,500 nm) of a Ciherang rice seed samples (40 grams), was used as the input data to develop the ANN model. A total of 60 samples were subjected to accelerated aging to obtain various levels of germination. The development of ANN models was done through calibration and validation of NIR spectra to the viability parameters. As ANN input, NIR reflectance of seed sample was given pretreatment data such as normalization, first derivative, second derivative, standard normal variate (SNV) and principal component analysis (PCA). The results showed that longer accelerated aging caused a decrease in seed viability. This was also indicated by the decrease in soluble protein and an increase in free fatty acids. The intensity of the NIR absorbance spectra also showed the same in the absorption region of soluble protein and free fatty acids. The best ANN models to predict the germination was 10PC-5-3 ANN with the SNV NIR reflectance used as the input data. Coefisien correlation of the validation was 0.8947, the value of ratio performance deviation was 2.2359 and the standard error performance was 9.9233%. The use of NIR spectra and ANN was potentially useful to perdict the viability of rice seeds more rapidly.

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Multiangular spectra of tree bark for common boreal tree species in Europe

Silva Fennica ◽

10.14214/sf.10331 ◽

2020 ◽

Vol 54 (4) ◽

Author(s):

Jussi Juola ◽

Aarne Hovi ◽

Miina Rautiainen

Keyword(s):

Tree Species ◽

Near Infrared ◽

Wavelength Region ◽

Tree Height ◽

Interspecific Variation ◽

Stem Bark ◽

Tree Bark ◽

Support Vector ◽

Tree Structures ◽

Infrared Wavelength

Despite the importance of spectral properties of woody tree structures, they are seldom represented in research related to forests, remote sensing, and reflectance modeling. This study presents a novel imaging multiangular measurement set-up that utilizes a mobile handheld hyperspectral camera (Specim IQ, 400â1000 nm), and can measure stem bark spectra in a controlled laboratory setting. We measured multiangular reflectance spectra of silver birch ( Roth), Scots pine ( L.) and Norway spruce ( (L.) Karst.) stem bark, and demonstrated the potential of using bark spectra in identifying tree species using a Support Vector Machine (SVM) based approach. Intraspecific reflectance variability was the lowest in visible (400â700 nm), and the highest in near-infrared (700â1000 nm) wavelength regions. Interspecific variation was the largest in the red, red-edge and near-infrared spectral bands. Spatial variation of reflectance along the tree height and different sides of the stem (north and south) were found. Both birch and pine had increased reflectance in the forward-scattering directions for visible to near-infrared wavelength regions, whilst spruce displayed the same only for the visible wavelength region. In addition, spruce had increased reflectance in the backward-scattering directions. In spite of the intraspecific variations, SVM could identify tree species with 88.8% overall accuracy when using pixel-specific spectra, and with 97.2% overall accuracy when using mean spectra per image. Based on our results it is possible to identify common boreal tree species based on their stem bark spectra using images from mobile hyperspectral cameras.Betula pendulaPinus sylvestrisPicea abies

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Near-Infrared Hyperspectral Imaging (NIR-HSI) for Nondestructive Prediction of Anthocyanins Content in Black Rice Seeds

Applied Sciences ◽

10.3390/app11114841 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4841

Author(s):

Hanim Z. Amanah ◽

Collins Wakholi ◽

Mukasa Perez ◽

Mohammad Akbar Faqeerzada ◽

Salma Sultana Tunny ◽

...

Keyword(s):

Hyperspectral Imaging ◽

Near Infrared ◽

Prediction Models ◽

Partial Least Square ◽

Mixed Data ◽

Rice Seed ◽

Black Rice ◽

Sorting Machine ◽

Two Samples ◽

Rice Seeds

Anthocyanins are an important micro-component that contributes to the quality factors and health benefits of black rice. Anthocyanins concentration and compositions differ among rice seeds depending on the varieties, growth conditions, and maturity level at harvesting. Chemical composition-based seeds inspection on a real-time, non-destructive, and accurate basis is essential to establish industries to optimize the cost and quality of the product. Therefore, this research aimed to evaluate the feasibility of near-infrared hyperspectral imaging (NIR-HSI) to predict the content of anthocyanins in black rice seeds, which will open up the possibility to develop a sorting machine based on rice micro-components. Images of thirty-two samples of black rice seeds, harvested in 2019 and 2020, were captured using the NIR-HSI system with a wavelength of 895–2504 nm. The spectral data extracted from the image were then synchronized with the rice anthocyanins reference value analyzed using high-performance liquid chromatography (HPLC). For comparison, the seed samples were ground into powder, which was also captured using the same NIR-HSI system to obtain the data and was then analyzed using the same method. The model performance of partial least square regression (PLSR) of the seed sample developed based on harvesting time, and mixed data revealed the model consistency with R2 over 0.85 for calibration datasets. The best prediction models for 2019, 2020, and mixed data were obtained by applying standard normal variate (SNV) pre-processing, indicated by the highest coefficient of determination (R2) of 0.85, 0.95, 0.90, and the lowest standard error of prediction (SEP) of 0.11, 0.17, and 0.16 mg/g, respectively. The obtained R2 and SEP values of the seed model were comparable to the result of powder of 0.92–0.95 and 0.09–0.15 mg/g, respectively. Additionally, the obtained beta coefficients from the developed model were used to generate seed chemical images for predicting anthocyanins in rice seed. The root mean square error (RMSE) value for seed prediction evaluation showed an acceptable result of 0.21 mg/g. This result exhibits the potential of NIR-HSI to be applied in a seed sorting machine based on the anthocyanins content.

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Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning

RSC Advances ◽

10.1039/d0ra06938h ◽

2020 ◽

Vol 10 (72) ◽

pp. 44149-44158

Author(s):

Yong Yang ◽

Jianping Chen ◽

Yong He ◽

Feng Liu ◽

Xuping Feng ◽

...

Keyword(s):

Hyperspectral Imaging ◽

Transfer Learning ◽

Significant Role ◽

Near Infrared ◽

Seed Vigor ◽

Rice Production ◽

Rice Seed ◽

Rice Seeds

Rice seed vigor plays a significant role in determining the quality and quantity of rice production.

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First Report of Burkholderia glumae Isolated from Symptomless Rice Seeds in China

Plant Disease ◽

10.1094/pdis-91-10-1363b ◽

2007 ◽

Vol 91 (10) ◽

pp. 1363-1363 ◽

Cited By ~ 18

Author(s):

J. Luo ◽

G. Xie ◽

B. Li ◽

X. Lihui

Keyword(s):

Large Scale ◽

Oryza Sativa L ◽

Identification System ◽

Rice Seed ◽

Burkholderia Glumae ◽

First Report ◽

Microbial Identification ◽

Seedling Rot ◽

Two Samples ◽

Rice Seeds

Burkholderia glumae causes grain rot and seedling rot of rice (Oryza sativa L.). It is seedborne and has caused severe damage in Japan (1). Since 1997, efforts have been made to detect the pathogen in rice seeds in China (2), where no typical symptoms have been observed in the rice paddy fields. Isolation from 623 symptomless rice seed samples yielded two samples, originally produced in Hainan province, with possible B. glumae (0.32%). Six bacterial strains isolated from these two samples showed characteristics similar to those of the standard reference strain of B. glumae, LMG 1837T from Belgium, in phenotypic tests including the Biolog identification system (version 4.2; Hayward, CA), pathogenicity tests, and gas chromatographic analysis of fatty acid methyl esters (FAMEs) using the Microbial identification System (MIDI Company, Newark, DE) with aerobic bacterial library (TABA50). All strains were gram-negative aerobic rods, 1.5 to 2.5 μm × 0.5 to 0.7μm, and had 1 to 7 polar flagella. No green fluorescent diffusible pigment was produced on King's medium B. Colonies were gray-white, slightly raised with smooth margins, and appeared within 3 days on nutrient agar. A hypersensitive reaction was observed on tobacco cv. Benshi 24 h after inoculation. All isolates were identified as B. glumae with Biolog similarity of 0.68 to 0.87 and FAMEs similarity of 0.65 to 0.91. Identification as B. glumae was confirmed by polymerase chain reaction (PCR) (3) primers BG1: 5′-ACACGGAACACCTGGGTA-3′; and BG2: 5′-TCGCTCTCCCGAAGAGAT-3′. Inoculation of intact plants of cv. Jiayue with cell suspensions containing 108 CFU/ml of the six strains individually produced seedling rot and grain rot symptoms. The bacterium was reisolated from symptomatic rice plants. B. glumae was first reported from Japan as the cause of grain rot of rice in 1967 (1) and was isolated from symptomatic rice seeds in 1987 in Taiwan, China. To our knowledge, this is the first report of B. glumae being isolated from healthy-looking rice seeds in China. This indicates that the pathogen is already in the mainland of China and there is a risk of a seedling rot outbreak if rice seedlings are raised indoors on a large scale for transplantation as it is in Japan. References: (1) T. Kurita and H. Tabei. Ann. Phytopathol. Soc. Jpn. 33:111,1967. (2) G. L. Xie et al. Acta Phytopathol. Sin. 32:114, 2002. (3) M. Yukiko et al. Int. J. Syst. Evol. Microbiol. 56:1031, 2006.

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Blueberry fruit detection by Bayesian classifier and support vector machine based on visible to near-infrared multispectral imaging

10.13031/2013.41911 ◽

2012 ◽

Author(s):

Ce Yang ◽

Won Suk Lee

Keyword(s):

Support Vector Machine ◽

Near Infrared ◽

Multispectral Imaging ◽

Bayesian Classifier ◽

Support Vector

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Application of hyperspectral imaging of peat profiles to the case of fen-bog transition in aapa mires

10.5194/egusphere-egu2020-17715 ◽

2020 ◽

Author(s):

Lars Granlund ◽

Teemu Tahvanainen ◽

Markku Keinänen

Keyword(s):

Remote Sensing ◽

Hyperspectral Imaging ◽

Method Development ◽

Near Infrared ◽

Multispectral Imaging ◽

High Sensitivity ◽

Aerial Image ◽

Support Vector ◽

Infrared Range ◽

Ecosystem Structure

Hyperspectral imaging (HSI) is a promising precision tool for analysing chronological peat strata from vegetation transitions in peatlands. We explored the potential of HSI in identifying transitions in peat-forming vegetation and degree of peat humification. The changes in aapa mire complexes during recent decades have been assessed by various remote sensing methods (aerial image time series, satellite data and high-resolution UAV multispectral imaging) and HSI methods have been developed to support the data from other sources. Rapid growth of Sphagnum mosses over string-patterned aapa mires in the north-boreal zone has immense significance, since it can alter ecosystem structure and functions such as carbon sequestration. HSI is well suited for analysis of recent ecosystem changes, since it can be applied for large sample sets with extremely fine spatial detail. Additionally, peat layers have complex 3D structures that can be overlooked by other sampling methods.The HSI data was collected in laboratory conditions with two spectral imaging cameras, covering the visible to near-infrared range (VNIR 400-1000 nm), short-wave infrared range (SWIR, 1000-2500 nm). We used various methods such as PCA, k-means clustering and support vector machines for both quantitative and qualitative analysis of peat. &#160;Our analyses revealed detailed spectral changes that matched with transitions in peat quality and composition. Methodological issues unique to peat samples, such as the effect of oxidation and water content, were assessed for method development. We also used HSI to estimate quality changes that would easily be overlooked or only found by most laborious conventional techniques, like high-frequency microscopic counting of plant remains. Here, the spectral results can be used to guide sampling for microscopic routines, for example.Results with Carex and Sphagnum peat proved that efficient image-based classification methods for identifying peat transitions can be developed. Our SVM models in the VNIR and SWIR regions were able to distinguish Sphagnum and Carex peat with overall accuracy of validation 80 % and 81 %, respectively. We also developed simple NDI indices for the estimation of von Post humification index that worked with accuracy of 86 % and 59 % for VNIR and SWIR, respectively. In combination with data collected from other sources (remote sensing, ground-truthing, conventional laboratory analysis), peat spectral analyses give strong inference of changes. In our study system, results indicate high sensitivity of northern aapa mires to ecosystem-scale changes.

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