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

Molecules ◽  
2019 ◽  
Vol 24 (12) ◽  
pp. 2227 ◽  
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.

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

2019 ◽  
Vol 9 (5) ◽  
pp. 1027 ◽  
Author(s):  
Insuck Baek ◽  
Moon Kim ◽  
Byoung-Kwan Cho ◽  
Changyeun Mo ◽  
Jinyoung Barnaby ◽  
...  
Keyword(s):  
High Throughput Screening ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Wavelength Region ◽  
Support Vector ◽  
Rice Grain ◽  
Rice Seed ◽  
Forward Selection ◽  
Burkholderia Glumae ◽  
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.

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

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.

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

RSC Advances ◽  
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.

scholarly journals Identification of peat type and humification by laboratory VNIR/SWIR hyperspectral imaging of peat profiles with focus on fen-bog transition in aapa mires

2020 ◽  
Author(s):  
L. Granlund ◽  
M. Keinänen ◽  
T. Tahvanainen
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Support Vector ◽  
High Resolution Imaging ◽  
Factors Affecting ◽  
Vector Machines ◽  
Resolution Imaging ◽  
Shortwave Infrared ◽  
Type Classification ◽  
Wide Potential

Abstract Aims Hyperspectral imaging (HSI) has high potential for analysing peat cores, but methodologies are deficient. We aimed for robust peat type classification and humification estimation. We also explored other factors affecting peat spectral properties. Methods We used two laboratory setups: VNIR (visible to near-infrared) and SWIR (shortwave infrared) for high resolution imaging of intact peat profiles with fen-bog transitions. Peat types were classified with support vector machines, indices were developed for von Post estimation, and K-means clustering was used to analyse stratigraphic patterns in peat quality. With separate experiments, we studied spectral effects of drying and oxidation. Results Despite major effects, oxidation and water content did not impede robust HSI classification. The accuracy between Carex peat and Sphagnum peat in validation was 80% with VNIR and 81% with SWIR data. The spectral humification indices had accuracies of 82% with VNIR and 56%. Stratigraphic HSI patterns revealed that 36% of peat layer shifts were inclined by over 20 degrees. Spectral indices were used to extrapolate visualisations of element concentrations. Conclusions HSI provided reliable information of basic peat quality and was useful in visual mapping, that can guide sampling for other analyses. HSI can manage large amounts of samples to widen the scope of detailed analysis beyond single profiles and it has wide potential in peat research beyond the exploratory scope of this paper. We were able to confirm the capacity of HSI to reveal shifts of peat quality, connected to ecosystem-scale change.

scholarly journals Discrimination of CRISPR/Cas9-induced mutants of rice seeds using near-infrared hyperspectral imaging

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Xuping Feng ◽  
Cheng Peng ◽  
Yue Chen ◽  
Xiaodan Liu ◽  
Xujun Feng ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Induced Mutants ◽  
Rice Seeds

scholarly journals Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5481 ◽  
Author(s):  
Beatriz Martinez ◽  
Raquel Leon ◽  
Himar Fabelo ◽  
Samuel Ortega ◽  
Juan F. Piñeiro ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Brain Cancer ◽  
Near Infrared ◽  
Relevant Information ◽  
Support Vector ◽  
Algorithm Optimization ◽  
Spectral Bands ◽  
Tumor Identification ◽  
Spectral Ranges

Hyperspectral imaging (HSI) is a non-ionizing and non-contact imaging technique capable of obtaining more information than conventional RGB (red green blue) imaging. In the medical field, HSI has commonly been investigated due to its great potential for diagnostic and surgical guidance purposes. However, the large amount of information provided by HSI normally contains redundant or non-relevant information, and it is extremely important to identify the most relevant wavelengths for a certain application in order to improve the accuracy of the predictions and reduce the execution time of the classification algorithm. Additionally, some wavelengths can contain noise and removing such bands can improve the classification stage. The work presented in this paper aims to identify such relevant spectral ranges in the visual-and-near-infrared (VNIR) region for an accurate detection of brain cancer using in vivo hyperspectral images. A methodology based on optimization algorithms has been proposed for this task, identifying the relevant wavelengths to achieve the best accuracy in the classification results obtained by a supervised classifier (support vector machines), and employing the lowest possible number of spectral bands. The results demonstrate that the proposed methodology based on the genetic algorithm optimization slightly improves the accuracy of the tumor identification in ~5%, using only 48 bands, with respect to the reference results obtained with 128 bands, offering the possibility of developing customized acquisition sensors that could provide real-time HS imaging. The most relevant spectral ranges found comprise between 440.5–465.96 nm, 498.71–509.62 nm, 556.91–575.1 nm, 593.29–615.12 nm, 636.94–666.05 nm, 698.79–731.53 nm and 884.32–902.51 nm.

scholarly journals Treatment of rice seeds with thiamethoxam: reflections on physiological performance

2014 ◽  
Vol 36 (4) ◽  
pp. 458-464 ◽  
Author(s):  
Andreia da Silva Almeida ◽  
Cristiane Deuner ◽  
Carolina Terra Borges ◽  
Géri Eduardo Meneghello ◽  
Adilson Jauer ◽  
...  
Keyword(s):  
Defense Mechanisms ◽  
Seed Quality ◽  
Seedling Emergence ◽  
Accelerated Aging ◽  
Cold Test ◽  
Rice Seed ◽  
Seedling Length ◽  
Rice Seeds ◽  
Growing Conditions ◽  
System Length

Thiamethoxam is a systemic insecticide that is transported within the plant through its cells and can activate various physiological reactions such as protein expression. These proteins interact with defense mechanisms against stress in adverse growing conditions. The objective of this study was to evaluate the effect of thiamethoxam in rice seeds and the potential benefits that it can provide. Two experiments were carried out and, in both, seeds were treated with commercial product containing 350 g of thiamethoxam active ingredient per liter of product, at doses 0, 100, 200, 300 and 400 mL.100 kg-1 of seeds: 1) it was conducted with three lots of IRGA BR 424 cultivar rice seeds, which were submitted to the following laboratory tests: germination, cold test, accelerated aging test, as well as field assessment: total seedling length, root system length, number of panicles and productivity; 2) four lots of IRGA BR 424 cultivar rice seeds, two high and two low-vigor, were subjected to the following tests: germination, cold test and greenhouse seedling emergence test. Thiamethoxam rice seed treatment positively favors the seed quality.

scholarly journals ELECTRICAL CONDUCTIVITY AND ETHANOL RELEASE TO ASSESS RED RICE SEED VIGOR1

2021 ◽  
Vol 34 (4) ◽  
pp. 791-798
Author(s):  
RAFAEL MARANI BARBOSA ◽  
MATHEUS ANDRÉ DE JESUS ◽  
RAFAELA ALVES PEREIRA ◽  
GEDEON ALMEIDA GOMES JUNIOR
Keyword(s):  
Electrical Conductivity ◽  
Efficient Method ◽  
Seedling Emergence ◽  
Accelerated Aging ◽  
Seed Vigor ◽  
Rice Seed ◽  
Seed Sample ◽  
Red Rice ◽  
Germination Test ◽  
Rice Seeds

ABSTRACT To evaluate seed vigor, electrical conductivity and ethanol tests are fast and efficient methodologies. They have the potential to be used in several species, such as red rice. However, there are no protocols or information about their efficiency. Thus, the objective was to evaluate the efficiency, and define parameters of execution for electrical conductivity and ethanol tests, to evaluate the vigor of red rice seeds. The study was conducted using four lots of ‘BRS 901’ red rice, which was subjected to a germination test, as well as first count, accelerated aging, and field seedling emergence tests. The electrical conductivity test was performed with 25, 50, and 100 seeds soaked in 50 mL and 75 mL of water, at 25 °C and 30 °C, for 3, 6, 20, and 24 hours, respectively. The ethanol test was performed with 50 and 100 seeds soaked in a volume of water equivalent to 1.0, 1.5, 2.0, 2.5, and 3.0× the mass of the seed sample. To assess the vigor of red rice seeds, the electrical conductivity test is an efficient method when conducted with 50 seeds soaked in 50 mL of water at 25 °C for 20 hours. Meanwhile, the ethanol test is most effective when performed with 50 seeds, in a volume of water that is 2.5× the mass of the sample, at 40 °C for 24 hours.

scholarly journals Distinction of surgically resected gastrointestinal stromal tumor by near-infrared hyperspectral imaging

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Daiki Sato ◽  
Toshihiro Takamatsu ◽  
Masakazu Umezawa ◽  
Yuichi Kitagawa ◽  
Kosuke Maeda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gastrointestinal Stromal Tumor ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Submucosal Tumor ◽  
Stromal Tumor ◽  
Training Data ◽  
Support Vector ◽  
Gastric Gist ◽  
Mucosal Layer

AbstractThe diagnosis of gastrointestinal stromal tumor (GIST) using conventional endoscopy is difficult because submucosal tumor (SMT) lesions like GIST are covered by a mucosal layer. Near-infrared hyperspectral imaging (NIR-HSI) can obtain optical information from deep inside tissues. However, far less progress has been made in the development of techniques for distinguishing deep lesions like GIST. This study aimed to investigate whether NIR-HSI is suitable for distinguishing deep SMT lesions. In this study, 12 gastric GIST lesions were surgically resected and imaged with an NIR hyperspectral camera from the aspect of the mucosal surface. Thus, the images were obtained ex-vivo. The site of the GIST was defined by a pathologist using the NIR image to prepare training data for normal and GIST regions. A machine learning algorithm, support vector machine, was then used to predict normal and GIST regions. Results were displayed using color-coded regions. Although 7 specimens had a mucosal layer (thickness 0.4–2.5 mm) covering the GIST lesion, NIR-HSI analysis by machine learning showed normal and GIST regions as color-coded areas. The specificity, sensitivity, and accuracy of the results were 73.0%, 91.3%, and 86.1%, respectively. The study suggests that NIR-HSI analysis may potentially help distinguish deep lesions.

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