Development of simplified models for the nondestructive testing of rice with husk starch content using hyperspectral imaging technology

2019 ◽  
Vol 11 (46) ◽  
pp. 5910-5918 ◽  
Author(s):  
Zhehao Zhang ◽  
Xiang Yin ◽  
Chengye Ma
Keyword(s):  
Nondestructive Testing ◽  
Hyperspectral Imaging ◽  
Predictive Models ◽  
Imaging System ◽  
Starch Content ◽  
Simplified Models ◽  
Rice Varieties ◽  
Imaging Technology

In this study, we aimed to establish the predictive models of the starch content in rice (with husk) using a hyperspectral imaging system (HSI) for a collection of 87 different rice varieties in China.

scholarly journals Research on Camouflage Recognition in Simulated Operational Environment Based on Hyperspectral Imaging Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Donge Zhao ◽  
Shuyan Liu ◽  
Xuefeng Yang ◽  
Yayun Ma ◽  
Bin Zhang ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Spatial Information ◽  
Imaging System ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Practical Significance ◽  
Support Vector ◽  
Operational Environment ◽  
Imaging Technology ◽  
Training Samples

Hyperspectral imaging technology can obtain the spatial information and spectral information of the simulated operational background and its camouflage materials at the same time and identify and classify them according to their differences. In this paper, we collected the hyperspectral images (400–1000 nm) of the desert background, jungle background, desert camouflage netting, jungle camouflage netting, and jungle camouflage clothing through the hyperspectral imaging system, and the samples were preprocessed by denoising and black-and-white correction. Then, we analysed the region of interest (ROI) of the training samples by principal component analysis (PCA). After the pixels in the region of interest and their surrounding areas were averaged, 60% of the data was used as the training samples, and the remaining 40% was used as the test samples. According to their similarities and differences between them and referenced spectrum, the models of classification were established by combining the Naive Bayes (NB) algorithm, K-nearest neighbour (KNN) algorithm, random forest (RF) algorithm, and support vector machine (SVM) algorithm. The results show that among the four models, SVM model has the highest accuracy of classification and the recognition rate of jungle camouflage clothing is the highest. This study verifies the scientific and feasibility of hyperspectral imaging technology for camouflage identification and classification in a simulated operational environment, which has some practical significance.

Mapping of fatty acids composition in shelled almonds analysed in bulk using a Hyperspectral Imaging system

LWT ◽  
2021 ◽  
Vol 138 ◽  
pp. 110678
Author(s):  
Irina Torres ◽  
Dolores Pérez-Marín ◽  
Miguel Vega-Castellote ◽  
María-Teresa Sánchez
Keyword(s):  
Fatty Acids ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Fatty Acids Composition

scholarly journals Quality inspection of nectarine based on hyperspectral imaging technology

2021 ◽  
Vol 9 (1) ◽  
pp. 350-357
Author(s):  
Feng-Hua Huang ◽  
Yan-Hong Liu ◽  
XinYi Sun ◽  
Hua Yang
Keyword(s):  
Hyperspectral Imaging ◽  
Quality Inspection ◽  
Imaging Technology

Determination of starch content in adulterated fresh cheese using hyperspectral imaging

2018 ◽  
Vol 21 ◽  
pp. 14-19 ◽  
Author(s):  
Abel Barreto ◽  
J.P. Cruz-Tirado ◽  
Raúl Siche ◽  
Roberto Quevedo
Keyword(s):  
Hyperspectral Imaging ◽  
Starch Content ◽  
Fresh Cheese

Adaptive and automatic red blood cell counting method based on microscopic hyperspectral imaging technology

2017 ◽  
Vol 19 (12) ◽  
pp. 124014 ◽  
Author(s):  
Xi Liu ◽  
Mei Zhou ◽  
Song Qiu ◽  
Li Sun ◽  
Hongying Liu ◽  
...  
Keyword(s):  
Blood Cell ◽  
Hyperspectral Imaging ◽  
Red Blood Cell ◽  
Cell Counting ◽  
Counting Method ◽  
Imaging Technology ◽  
Blood Cell Counting
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