Does spatial region of interest (ROI) matter in multispectral and hyperspectral imaging of segmented wheat kernels?

2021 ◽  
Vol 212 ◽  
pp. 106-114
Author(s):  
Stephen R. Delwiche ◽  
Insuck Baek ◽  
Moon S. Kim
Keyword(s):  
Hyperspectral Imaging ◽  
Region Of Interest ◽  
Spatial Region ◽  
Wheat Kernels

scholarly journals NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Study the Residues of Different Concentrations of Omethoate on Wheat Grain Surface

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3147 ◽  
Author(s):  
Liu Zhang ◽  
Zhenhong Rao ◽  
Haiyan Ji
Keyword(s):  
Feature Extraction ◽  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Imaging System ◽  
Support Vector ◽  
Multivariate Methods ◽  
Model Combination ◽  
Modeling Analysis ◽  
Svm Model ◽  
Wheat Kernels

In this study, a hyperspectral imaging system of 866.4–1701.0 nm was selected and combined with multivariate methods to identify wheat kernels with different concentrations of omethoate on the surface. In order to obtain the optimal model combination, three preprocessing methods (standard normal variate (SNV), Savitzky–Golay first derivative (SG1), and multivariate scatter correction (MSC)), three feature extraction algorithms (successive projections algorithm (SPA), random frog (RF), and neighborhood component analysis (NCA)), and three classifier models (decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM)) were applied to make a comparison. Firstly, based on the full wavelengths modeling analysis, it was found that the spectral data after MSC processing performed best in the three classifier models. Secondly, three feature extraction algorithms were used to extract the feature wavelength of MSC processed data and based on feature wavelengths modeling analysis. As a result, the MSC–NCA–SVM model performed best and was selected as the best model. Finally, in order to verify the reliability of the selected model, the hyperspectral image was substituted into the MSC–NCA–SVM model and the object-wise method was used to visualize the image classification. The overall classification accuracy of the four types of wheat kernels reached 98.75%, which indicates that the selected model is reliable.

Hyperspectral-imaging-based techniques applied to wheat kernels characterization

2012 ◽  
Author(s):  
Silvia Serranti ◽  
Daniela Cesare ◽  
Giuseppe Bonifazi
Keyword(s):  
Hyperspectral Imaging ◽  
Wheat Kernels

Estimation of rice protein content before harvest using ground-based hyperspectral imaging and region of interest analysis

2017 ◽  
Vol 19 (4) ◽  
pp. 721-734 ◽  
Author(s):  
Hiroyuki Onoyama ◽  
Chanseok Ryu ◽  
Masahiko Suguri ◽  
Michihisa Iida
Keyword(s):  
Protein Content ◽  
Hyperspectral Imaging ◽  
Region Of Interest ◽  
Rice Protein

Hyperspectral imaging for detection of black tip damage in wheat kernels

2009 ◽  
Author(s):  
Stephen R. Delwiche ◽  
I-Chang Yang ◽  
Moon S. Kim
Keyword(s):  
Hyperspectral Imaging ◽  
Wheat Kernels

Detecting Fusarium head blight in wheat kernels using hyperspectral imaging

2015 ◽  
Vol 131 ◽  
pp. 65-76 ◽  
Author(s):  
Jayme G.A. Barbedo ◽  
Casiane S. Tibola ◽  
José M.C. Fernandes
Keyword(s):  
Fusarium Head Blight ◽  
Hyperspectral Imaging ◽  
Head Blight ◽  
Wheat Kernels
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