Single kernel wheat hardness estimation using near infrared hyperspectral imaging

Erkinbaev, Chyngyz; Derksen, K.J.J.; Paliwal, Jitendra

doi:10.1016/j.infrared.2019.03.033

Cited by 49 publications

(26 citation statements)

References 21 publications

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“…ANNs have found its applications in detection of mechanical damage in mushrooms, single kernel wheat hardness estimation, cold injury in peaches, honey adulteration, prediction of firmness in kiwi fruit ( Rojas-Moraleda et al., 2017 ; Erkinbaev et al., 2019 ; Pan et al., 2015 ; Shafiee et al., 2016; Siripatrawan et al., 2011 ). ANNs have been widely used as a single algorithm machine learning tool in hyperspectral image analysis ( Table 1 ).…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

“…Besides, the studies indicated that only three layers (input, hidden and output) with varying neurons in each layer is enough to build a model with high accuracy. Hence, addition of more hidden layers may slightly increase the accuracy but will also increase the computational load ( Erkinbaev et al., 2019 ). The different type of transfer functions available for transfer of information from one layer to the other in a neural network are sigmoid function, linear transfer function, hyperbolic tangent function and logistic function.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

“… Study Wavelength range (nm) Spectral pre-processing Image processing ANN characteristics ANN Computational software Classification accuracy References Network type Network topology Training set: Validation set Input layer Hidden layer Output layer Number Nodes Number Nodes Number Nodes Detection of mechanical damage in mushrooms 900–1700 Savitzky-Golay Second derivative Harris corner detection algorithm Polak–Ribie're conjugate gradient Back propagation 101 – 01 30 05 – 80:20 MATLAB R2012b 91% Rojas-Moraleda et al. (2017) Estimation of wheat hardness (single kernel) 1000–2500 Savitzky-Golay first derivates (SGD1); mean centering (MC) and orthogonal signal correction (OSC) Image thresholding Two-layer Back Propagation neural Network (BPNN) 01 – 02 03 01 – 80:20 MATLAB 8.2 90% Erkinbaev et al. (2019) Detection of cold injury in peaches 400–1000 – – Back-propagation feed-forward neural network 01 420 01 03 01 02 80:20 – 96% Pan et al.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

“…The popularity and the powerful nature of PCA makes it the most widely used dimensionality reduction techniques in building robust classification models using hyperspectral data ( Rojas-Moraleda et al., 2017 ; Erkinbaev et al., 2019 ; He et al., 2020 ; Vu et al., 2016 ; Zhu et al., 2016; Dong et al., 2017 ). Konda Naganathan et al.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

See 3 more Smart Citations

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

Saha

Manickavasagan

2021

Current Research in Food Science

265

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Non-destructive testing techniques have gained importance in monitoring food quality over the years. Hyperspectral imaging is one of the important non-destructive quality testing techniques which provides both spatial and spectral information. Advancement in machine learning techniques for rapid analysis with higher classification accuracy have improved the potential of using this technique for food applications. This paper provides an overview of the application of different machine learning techniques in analysis of hyperspectral images for determination of food quality. It covers the principle underlying hyperspectral imaging, the advantages, and the limitations of each machine learning technique. The machine learning techniques exhibited rapid analysis of hyperspectral images of food products with high accuracy thereby enabling robust classification or regression models. The selection of effective wavelengths from the hyperspectral data is of paramount importance since it greatly reduces the computational load and time which enhances the scope for real time applications. Due to the feature learning nature of deep learning, it is one of the most promising and powerful techniques for real time applications. However, the field of deep learning is relatively new and need further research for its full utilization. Similarly, lifelong machine learning paves the way for real time HSI applications but needs further research to incorporate the seasonal variations in food quality. Further, the research gaps in machine learning techniques for hyperspectral image analysis, and the prospects are discussed.

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Section: Machine Learning Techniquesmentioning

confidence: 99%

Section: Machine Learning Techniquesmentioning

confidence: 99%

Section: Machine Learning Techniquesmentioning

confidence: 99%

Section: Machine Learning Techniquesmentioning

confidence: 99%

See 2 more Smart Citations

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

Saha

Manickavasagan

2021

Current Research in Food Science

265

View full text Add to dashboard Cite

show abstract

“…The analysis and processing of spectral data by an appropriate manifold learning algorithm can objectively and reliably reflect the overall information of the sample tested, which is characterized in a fast, nondestructive, and accurate manner. It has been widely used for testing the quality of seeds, (9,10) drugs, (11,12) the interior and exterior of fruit and vegetables, (13,14) and meat. (15,16) The hyperspectral imaging used in apple quality testing mainly focuses on species classification, (17,18) damage detection, (19,20) and internal component detection.…”

Section: Introductionmentioning

confidence: 99%

Detection of Apple Taste Information Using Model Based on Hyperspectral Imaging and Electronic Tongue Data

Liu¹,

Liu²,

Shin³

et al. 2020

Sensors and Materials

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Taste is one of the most important criteria for evaluating the quality of apples. In this study, a correlation model using hyperspectral images and quantitative taste information was built for the nondestructive detection of apple taste information. Firstly, the images of 90 sets of Aksu apples were collected by a hyperspectral image system and quantitative values of taste information (sourness and sweetness) were measured using an SA-402B electronic tongue. Secondly, to overcome the difficulties in obtaining the most representative wavelengths, a competitive adaptive reweighted sampling (CARS) algorithm was proposed to remove redundant information in the hyperspectral data. Then, 43 characteristic wavelengths corresponding to sourness and 22 characteristic wavelengths corresponding to sweetness were selected. Finally, particle swarm optimization (PSO) was used to dynamically optimize the kernel parameters and penalty factors of support vector regression (SVR). A PSO-SVR prediction model based on characteristic wavelengths was established. Upon comparing the performance of the prescreening and postscreening models, results showed that the CARS-PSO-SVR model achieved better prediction for apple taste information, for which the correlation coefficients (R 2) of sourness and sweetness were 0.81 and 0.887, and the root mean square errors of the prediction set (RMSEP) were 0.03 and 0.018, respectively.

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Recent developments on the contribution of glutenin and puroindoline proteins to improve wheat grain quality

Rai

Han

2022

Cereal Chem

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Background and Objectives: Wheat is most staple food crop for making different end products. The major factors determining the wheat flour end-use are glutenins and puroindolines. It is very important to identify the composition, variation, and functional characteristics of these key factors. Findings: The glutenin subunits comprise two subgroups-high molecular weight glutenin subunits (HMW-GS), and low molecular weight glutenin subunits (LMW-GS). Puroindolines genes depict a wide diversity of both PINA and PINB allelic forms. The prevalence and relative proportions of these types may vary depending on several genetic and environmental factors. Conclusion:Current review summarizes recently published research on the impact of glutenin genes governing gluten strength and puroindolines genes related to grain softness. It encompasses recent reports on the development of DNA-based molecular markers utilized to identify novel alleles associated with HMW-GS, LMW-GS, and puroindoline genes in diverse wheat genotypes.

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Single kernel wheat hardness estimation using near infrared hyperspectral imaging

Cited by 49 publications

References 21 publications

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review

Detection of Apple Taste Information Using Model Based on Hyperspectral Imaging and Electronic Tongue Data

Recent developments on the contribution of glutenin and puroindoline proteins to improve wheat grain quality

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