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.

scholarly journals Non-Destructive Trace Detection of Explosives Using Pushbroom Scanning Hyperspectral Imaging System

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 97 ◽  
Author(s):  
Siddharth Chaudhary ◽  
Sarawut Ninsawat ◽  
Tai Nakamura
Keyword(s):  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Imaging System ◽  
Classification Model ◽  
Support Vector ◽  
Trace Detection ◽  
Svm Model ◽  
Detection Of Explosives ◽  
Non Destructive ◽  
Hyperspectral Sensor

The aim of this study was to investigate the potential of the non-destructive hyperspectral imaging system (HSI) and accuracy of the model developed using Support Vector Machine (SVM) for determining trace detection of explosives. Raman spectroscopy has been used in similar studies, but no study has been published which is based on measurement of reflectance from hyperspectral sensor for trace detection of explosives. HSI used in this study has an advantage over existing techniques due to its combination of imaging system and spectroscopy, along with being contactless and non-destructive in nature. Hyperspectral images of the chemical were collected using the BaySpec hyperspectral sensor which operated in the spectral range of 400–1000 nm (144 bands). Image processing was applied on the acquired hyperspectral image to select the region of interest (ROI) and to extract the spectral reflectance of the chemicals which were stored as spectral library. Principal Component Analysis (PCA) and first derivative was applied to reduce the high dimensionality of the image and to determine the optimal wavelengths between 400 and 1000 nm. In total, 22 out of 144 wavelengths were selected by analysing the loadings of principal components (PC). SVM was used to develop the classification model. SVM model established on the whole spectrum from 400 to 1000 nm achieved an accuracy of 81.11%, whereas an accuracy of 77.17% with less computational load was achieved when SVM model was established on the optimal wavelengths selected. The results of the study demonstrate that the hyperspectral imaging system along with SVM is a promising tool for trace detection of explosives.

scholarly journals NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Identify Shrimp Freshness

2020 ◽  
Vol 10 (16) ◽  
pp. 5498
Author(s):  
Rongke Ye ◽  
Yingyi Chen ◽  
Yuchen Guo ◽  
Qingling Duan ◽  
Daoliang Li ◽  
...  
Keyword(s):  
Least Squares ◽  
Spectral Data ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Support Vector ◽  
Optimal Model ◽  
Model Combination ◽  
Modeling Analysis ◽  
Characteristic Wavelength ◽  
Discriminant Models

In this study, a hyperspectral imaging system of 866.4–1701.0 nm, combined with a variety of spectral processing methods were adopted to identify shrimp freshness. To gain the optimal model combination, three preprocessing methods (Savitzky-Golay first derivative (SG1), multivariate scatter correction (MSC), and standard normal variate (SNV)), three characteristic wavelength extraction algorithms (random frog algorithm (RFA), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS)), and four discriminant models (partial least squares discrimination analysis (PLS-DA), least squares support vector machine (LSSVM), random forest (RF), and extreme learning machine (ELM)) were employed for experimental study. First of all, due to the full wavelength modeling analysis, three preprocessing methods were utilized to preprocess the original spectral data. The analysis showed that the spectral data processed by the SNV method had the best performance among the four discriminant models. Secondly, due to the characteristic wavelength modeling analysis, three characteristic wavelength extraction algorithms were utilized to extract the characteristic wavelength of the SNV-processed spectral data. It was found that the CARS algorithm achieved the best performance among the three characteristic wavelength extraction algorithms, and the combining adoption of the ELM model and different characteristic wavelength extraction algorithms obtained the best results. Therefore, the model based on SNV-CARS-ELM obtained the best performance and was elected as the optimal model. Lastly, for accurately and explicitly displaying the refrigeration days of shrimps, the original hyperspectral images of shrimps were substituted into the SNV-CARS-ELM model, thus obtaining the general classification accuracy of 97.92%, and the object-wise method was used to visualize the classification results. As a result, the method proposed in this study can effectively detect the freshness of shrimps.

scholarly journals Detection of Sulfite Dioxide Residue on the Surface of Fresh-Cut Potato Slices Using Near-Infrared Hyperspectral Imaging System and Portable Near-Infrared Spectrometer

Molecules ◽  
2020 ◽  
Vol 25 (7) ◽  
pp. 1651 ◽  
Author(s):  
Xiulin Bai ◽  
Qinlin Xiao ◽  
Lei Zhou ◽  
Yu Tang ◽  
Yong He
Keyword(s):  
Sulfur Dioxide ◽  
Hyperspectral Imaging ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Imaging System ◽  
Principal Component ◽  
Support Vector ◽  
Svm Model ◽  
Fresh Cut ◽  
Potato Slices

Sodium pyrosulfite is a browning inhibitor used for the storage of fresh-cut potato slices. Excessive use of sodium pyrosulfite can lead to sulfur dioxide residue, which is harmful for the human body. The sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentrations of sodium pyrosulfite solution was classified by near-infrared hyperspectral imaging (NIR-HSI) system and portable near-infrared (NIR) spectrometer. Principal component analysis was used to analyze the object-wise spectra, and support vector machine (SVM) model was established. The classification accuracy of calibration set and prediction set were 98.75% and 95%, respectively. Savitzky–Golay algorithm was used to recognize the important wavelengths, and SVM model was established based on the recognized important wavelengths. The final classification accuracy was slightly less than that based on the full spectra. In addition, the pixel-wise spectra extracted from NIR-HSI system could realize the visualization of different samples, and intuitively reflect the differences among the samples. The results showed that it was feasible to classify the sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentration of sodium pyrosulfite solution by NIR spectra. It provided an alternative method for the detection of sulfur dioxide residue on the surface of fresh-cut potato slices.

scholarly journals Remote Hyperspectral Imaging Acquisition and Characterization for Marine Litter Detection

2021 ◽  
Vol 13 (13) ◽  
pp. 2536
Author(s):  
Sara Freitas ◽  
Hugo Silva ◽  
Eduardo Silva
Keyword(s):  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Imaging System ◽  
Spectral Response ◽  
Ground Truth ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Observation Data ◽  
Marine Litter ◽  
Comparison Of The Results

This paper addresses the development of a remote hyperspectral imaging system for detection and characterization of marine litter concentrations in an oceanic environment. The work performed in this paper is the following: (i) an in-situ characterization was conducted in an outdoor laboratory environment with the hyperspectral imaging system to obtain the spatial and spectral response of a batch of marine litter samples; (ii) a real dataset hyperspectral image acquisition was performed using manned and unmanned aerial platforms, of artificial targets composed of the material analyzed in the laboratory; (iii) comparison of the results (spatial and spectral response) obtained in laboratory conditions with the remote observation data acquired during the dataset flights; (iv) implementation of two different supervised machine learning methods, namely Random Forest (RF) and Support Vector Machines (SVM), for marine litter artificial target detection based on previous training. Obtained results show a marine litter automated detection capability with a 70–80% precision rate of detection in all three targets, compared to ground-truth pixels, as well as recall rates over 50%.

Quantitative Analysis of Cadmium Content in Tomato Leaves Based on Hyperspectral Image and Feature Selection

2018 ◽  
Vol 34 (5) ◽  
pp. 789-798 ◽  
Author(s):  
Yuechun Zhang ◽  
Jun Sun ◽  
Junyan Li ◽  
Xiaohong Wu ◽  
Chunmei Dai
Keyword(s):  
Feature Selection ◽  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Imaging System ◽  
Population Analysis ◽  
Cadmium Stress ◽  
Hyperspectral Images ◽  
Cadmium Content ◽  
Variable Combination ◽  
Non Destructive

Abstract.In order to ensure that safe and healthy tomatoes can be provided to people, a method for quantitative determination of cadmium content in tomato leaves based on hyperspectral imaging technology was put forward in this study. Tomato leaves with seven cadmium stress gradients were studied. Hyperspectral images of all samples were firstly acquired by the hyperspectral imaging system, then the spectral data were extracted from the hyperspectral images. To simplify the model, three algorithms of competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA) and bootstrapping soft shrinkage (BOSS) were used to select the feature wavelengths ranging from 431 to 962 nm. Final results showed that BOSS can improve prediction performance and greatly reduce features when compared with the other two selection methods. The BOSS model got the best accuracy in calibration and prediction with R2c of 0.9907 and RMSEC of 0.4257mg/kg, R2p of 0.9821, and RMSEP of 0.6461 mg/kg. Hence, the method of hyperspectral technology combined with the BOSS feature selection is feasible for detecting the cadmium content of tomato leaves, which can potentially provide a new method and thought for cadmium content detection of other crops. Keywords: Feature selection, Hyperspectral image technology, Non-destructive analysis, Regression model, Tomato leaves.

scholarly journals Semantic feature extraction method for hyperspectral crop classification

2021 ◽  
Vol 23 (1) ◽  
pp. 387
Author(s):  
M. C. Girish Baabu ◽  
Padma M. C.
Keyword(s):  
Feature Extraction ◽  
Hyperspectral Image ◽  
Feature Representation ◽  
Classification Model ◽  
Support Vector ◽  
Spectral Correlation ◽  
Feature Extraction Method ◽  
Mixed Pixel ◽  
Computational Overhead ◽  
Crop Classification

<span>Hyperspectral imaging (HSI) is composed of several hundred of narrow bands (NB) with high spectral correlation and is widely used in crop classification; thus induces time and space complexity, resulting in high computational overhead and Hughes phenomenon in processing these images. Dimensional reduction technique such as band selection and feature extraction plays an important part in enhancing performance of hyperspectral image classification. However, existing method are not efficient when put forth in noisy and mixed pixel environment with dynamic illumination and climatic condition. Here the proposed Sematic Feature Representation based HSI (SFR-HSI) crop classification method first employ Image Fusion (IF) method for finding meaningful features from raw HSI spectrally. Second, to extract inherent features that keeps spatially meaningful representation of different crops by eliminating shading elements. Then, the meaningful feature set are used for training using Support vector machine (SVM). Experiment outcome shows proposed HSI crop classification model achieves much better accuracies and Kappa coefficient performance. </span>

scholarly journals Application of hyperspectral imaging system to discriminate different diets of live Rainbow trout (Oncorhynchus mykiss)

2018 ◽  
Author(s):  
Mohammadmehdi Saberioon ◽  
Petr Cisar ◽  
Laurent Labbé ◽  
Pavel Souček ◽  
Pablo Pelissier
Keyword(s):  
Rainbow Trout ◽  
Oncorhynchus Mykiss ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Machine Learning Algorithms ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Classification Models ◽  
Fish Skin ◽  
Classification Rate

The main aim of this study was to evaluate the feasibility of hyperspectral imagery for determining the influence of different diets on fish skin. Rainbow trout (Oncorhynchus mykiss) were fed either a commercial based diet (N= 80) or a 100 % plant-based diet (N = 80). Hyperspectral images were made using a push-broom hyperspectral imaging system in the spectral region of 394-1009 nm. All images were calibrated using dark and white reference and the average spectral data from the region of interest were extracted. Six spectral pre-treatment methods were used, including Savitzky-Golay (SG), First Derivative(FD), Second Derivative (SD), Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC) then a support vector machine (SVM) with linear kernel was applied to establish the classification models. Additionally, the Genetic algorithm (GA) was used to select optimal wavelengths to reduce the high dimensionality from hyperspectral images in order to decrease the computational costs and simplify the classification models. Overall classification models established from full wavelengths and selected wavelengths showed the good performance (Correct Classification Rate (CCR) = 0.871, Kappa = 0.741) when coupled with SG. The overall results indicate that the integration of Vis/NIR hyperspectral imaging system and machine learning algorithms has promise for discriminating different diets based on the live fish skin.

scholarly journals Classification of Rice Leaf Blast Severity Using Hyperspectral Imaging

2021 ◽  
Author(s):  
Guosheng Zhang ◽  
Tongyu Xu ◽  
Youwen Tian ◽  
Shuai Feng ◽  
Dongxue Zhao ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Spectral Reflectance ◽  
Support Vector ◽  
Growth Stages ◽  
Leaf Blast ◽  
Full Spectrum ◽  
Rice Leaf ◽  
Svm Model ◽  
Rice Leaf Blast ◽  
Different Growth Stages

Abstract Background: Hyperspectral imaging is an emerging technology applied in plant disease research, including disease detection, multiple disease identification, disease severity assessment, and disease resistance evaluation. Rice leaf blast is prevalent all over the world and is a serious threat to rice yield and quality. In this paper, the standard deviation (STD) of the spectral reflectance of whole leaves was calculated and a support vector machine (SVM) model was built to classify the degree of rice leaf blast at different growth stages.Results: The classification accuracy of the full-spectrum-based SVM model at jointing stage, booting stage and heading stage was 94.44%, 81.58% and 80.48%, respectively. The corresponding macro recall values were 0.9714, 0.715 and 0.79. The average STD of the spectral reflectance of the whole leaf differed not only within samples with different disease grades, but also those with the same disease level. Conclusion: The STD of the spectral reflectance of whole leaf could be utilized to classify the rice leaf blast degree at different growth stages. The classification method was derived from physiological phenomena that were visible to the naked eye, making it more intuitive and convincing.

scholarly journals Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects

2020 ◽  
Vol 10 (8) ◽  
pp. 2851
Author(s):  
Quoc Thien Pham ◽  
Nai-Shang Liou
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging System ◽  
Skin Condition ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Novel Object ◽  
Object Rotation ◽  
Surface Defect Detection ◽  
Ann Models ◽  
Rotation Scan

A novel object rotation hyperspectral imaging system with the wavelength range of 468–950 nm for investigating round-shaped fruits was developed. This system was used to obtain the reflection spectra of jujubes for the application of surface defect detection. Compared to the traditional linear scan system, which can scan about 49% of jujube surface in one scan pass, this novel object rotation scan system can scan 95% of jujube surface in one scan pass. Six types of jujube skin condition, including rusty spots, decay, white fungus, black fungus, cracks, and glare, were classified by using hyperspectral data. Support vector machine (SVM) and artificial neural network (ANN) models were used to differentiate the six jujube skin conditions. Classification effectiveness of models was evaluated based on confusion matrices. The percentage of classification accuracy of SVM and ANN models were 97.3% and 97.4%, respectively. The object rotation scan method developed for this study could be used for other round-shaped fruits and integrated into online hyperspectral investigation systems.

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