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 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 Detecting Bacterial Biofilms Using Fluorescence Hyperspectral Imaging and Various Discriminant Analyses

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2213
Author(s):  
Ahyeong Lee ◽  
Saetbyeol Park ◽  
Jinyoung Yoo ◽  
Jungsook Kang ◽  
Jongguk Lim ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Food Processing ◽  
Nearest Neighbor ◽  
Hyperspectral Image ◽  
Uv Light ◽  
Hyperspectral Images ◽  
K Nearest Neighbor ◽  
E Coli ◽  
Discriminant Analyses

Biofilms formed on the surface of agro-food processing facilities can cause food poisoning by providing an environment in which bacteria can be cultured. Therefore, hygiene management through initial detection is important. This study aimed to assess the feasibility of detecting Escherichia coli (E. coli) and Salmonella typhimurium (S. typhimurium) on the surface of food processing facilities by using fluorescence hyperspectral imaging. E. coli and S. typhimurium were cultured on high-density polyethylene and stainless steel coupons, which are the main materials used in food processing facilities. We obtained fluorescence hyperspectral images for the range of 420–730 nm by emitting UV light from a 365 nm UV light source. The images were used to perform discriminant analyses (linear discriminant analysis, k-nearest neighbor analysis, and partial-least squares discriminant analysis) to identify and classify coupons on which bacteria could be cultured. The discriminant performances of specificity and sensitivity for E. coli (1–4 log CFU·cm−2) and S. typhimurium (1–6 log CFU·cm−2) were over 90% for most machine learning models used, and the highest performances were generally obtained from the k-nearest neighbor (k-NN) model. The application of the learning model to the hyperspectral image confirmed that the biofilm detection was well performed. This result indicates the possibility of rapidly inspecting biofilms using fluorescence hyperspectral images.

scholarly journals Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits

2011 ◽  
Vol 29 (No. 6) ◽  
pp. 595-602 ◽  
Author(s):  
Q. Lü ◽  
M.-j. Tang ◽  
J.-r. Cai ◽  
J.-w. Zhao ◽  
S. Vittayapadung
Keyword(s):  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Detection System ◽  
Image Data ◽  
Principal Component ◽  
Quality Analysis ◽  
Economic Losses ◽  
Hyperspectral Image Data ◽  
Multispectral Detection ◽  
Non Destructive

It is necessary to develop a non-destructive technique for kiwifruit quality analysis because the machine injury could lower the quality of fruit and incur economic losses. Bruises are not visible externally owing to the special physical properties of kiwifruit peel.We proposed the hyperspectral imaging technique to inspect the hidden bruises on kiwifruit. The Vis/NIR (408–1117 nm) hyperspectral image data was collected. Multiple optimal wavelength (682, 723, 744, 810, and 852 nm) images were obtained using principal component analysis on the high dimension spectral image data (wavelength range from 600 nm to 900 nm). The bruise regions were extracted from the component images of the five waveband images using RBF-SVM classification. The experimental results showed that the error of hidden bruises detection on fruits by means of hyperspectral imaging was 12.5%. It was concluded that the multiple optimal waveband images could be used to constructs a multispectral detection system for hidden bruises on kiwifruits.

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 Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

2020 ◽  
Vol 10 (3) ◽  
pp. 1173 ◽  
Author(s):  
Zhiqi Hong ◽  
Yong He
Keyword(s):  
Hyperspectral Imaging ◽  
Nutritional Value ◽  
Geographical Origin ◽  
Principal Component ◽  
Hyperspectral Images ◽  
Imaging Systems ◽  
Support Vector ◽  
Spectral Ranges ◽  
Tea Leaf ◽  
Non Destructive

Longjing tea is one of China’s protected geographical indication products with high commercial and nutritional value. The geographical origin of Longjing tea is an important factor influencing its commercial and nutritional value. Hyperspectral imaging systems covering the two spectral ranges of 380–1030 nm and 874–1734 nm were used to identify a single tea leaf of Longjing tea from six geographical origins. Principal component analysis (PCA) was conducted on hyperspectral images to form PCA score images. Differences among samples from different geographical origins were visually observed from the PCA score images. Support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) models were built using the full spectra at the two spectral ranges. Decent classification performances were obtained at the two spectral ranges, with the overall classification accuracy of the calibration and prediction sets over 84%. Furthermore, prediction maps for geographical origins identification of Longjing tea were obtained by applying the SVM models on the hyperspectral images. The overall results illustrate that hyperspectral imaging at both spectral ranges can be applied to identify the geographical origin of single tea leaves of Longjing tea. This study provides a new, rapid, and non-destructive alternative for Longjing tea geographical origins identification.

scholarly journals Spatial–Spectral Evidence of Glare Influence on Hyperspectral Acquisitions

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4374
Author(s):  
Alberto Signoroni ◽  
Mauro Conte ◽  
Alice Plutino ◽  
Alessandro Rizzi
Keyword(s):  
Hyperspectral Image ◽  
Imaging System ◽  
Reflectance Spectra ◽  
Hyperspectral Images ◽  
Imaging Systems ◽  
Light Sources ◽  
Optical Phenomenon ◽  
Lighting Conditions ◽  
First Time ◽  
Glare Effect

Glare is an unwanted optical phenomenon which affects imaging systems with optics. This paper presents for the first time a set of hyperspectral image (HSI) acquisitions and measurements to verify how glare affects acquired HSI data in standard conditions. We acquired two ColorCheckers (CCs) in three different lighting conditions, with different backgrounds, different exposure times, and different orientations. The reflectance spectra obtained from the imaging system have been compared to pointwise reference measures obtained with contact spectrophotometers. To assess and identify the influence of glare, we present the Glare Effect (GE) index, which compares the contrast of the grayscale patches of the CC in the hyperspectral images with the contrast of the reference spectra of the same patches. We evaluate, in both spatial and spectral domains, the amount of glare affecting every hyperspectral image in each acquisition scenario, clearly evidencing an unwanted light contribution to the reflectance spectra of each point, which increases especially for darker pixels and pixels close to light sources or bright patches.

Evaluating the Potential of Clonal Selection Optimization Algorithm to Hyperspectral Image Feature Selection

2012 ◽  
Vol 500 ◽  
pp. 799-805 ◽  
Author(s):  
Farhad Samadzadegan ◽  
Shahin Rahmatollahi Namin ◽  
Mohammad Ali Rajabi
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Hyperspectral Image ◽  
Clonal Selection ◽  
Fitness Function ◽  
Hyperspectral Images ◽  
Image Feature ◽  
Spectral Bands ◽  
Selection Algorithms ◽  
Selection Of

The great number of captured near spectral bands in hyperspectral images causes the curse of dimensionality problem and results in low classification accuracy. The feature selection algorithms try to overcome this problem by limiting the input space dimensions of classification for hyperspectral images. In this paper, immune clonal selection optimization algorithm is used for feature selection. Also one of the fastest Artificial Immune classification algorithms is used to compute fitness function of the feature selection. The comparison of the feature selection results with genetic algorithm shows the clonal selection’s higher performance to solve selection of features.

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 Current Advances in Hyperspectral Face Recognition

2020 ◽  
Author(s):  
Rizwan Qureshi ◽  
Muhammad Uzair ◽  
Anam Zahra
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Document Image ◽  
Hyperspectral Images ◽  
Future Research ◽  
Research Directions ◽  
Future Research Directions ◽  
The Cost

Hyperspectral imaging systems are well established, for satellite, remote sensing and geosciences applications. Recently, the reduction in the cost of hyperspectral sensors and increase in the imaging speed has attracted computer vision scientists to apply hyperspectral imaging to ground based computer vision problems such as material classification, agriculture, chemistry and document image analysis. Hyperspectral imaging has also been explored for face recognition; to tackle the issues of pose and illumination variations by exploiting the richer spectral information of hyperspectral images. In this article, we present a detailed review on the potential of hyperspectral imaging for face recognition. We present hyperspectral image aquisition process and discuss key preprocessing challenges. We also discuss hyperspectral face recognition databases and techniques for feature extraction from the hyperspectral images. Potential future research directions are also highlighted

scholarly journals Use of infrared hyperspectral imaging as an aid for paint identification

2016 ◽  
Author(s):  
A. Polak ◽  
T. Kelman ◽  
P. Murray ◽  
S. Marshall ◽  
D. Stothard ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Classification Problem ◽  
Extensive Study ◽  
Electromagnetic Spectrum ◽  
New Techniques ◽  
Art Authentication ◽  
Infrared Spectral ◽  
Non Destructive

Art authentication is a complicated process that often requires the extensive study of high value objects. Although a series of non-destructive techniques is already available for art scientists, new techniques, extending current possibilities, are still required. In this paper, the use of a novel mid-infrared tunable imager is proposed as an active hyperspectral imaging system for art work analysis. The system provides access to a range of wavelengths in the electromagnetic spectrum (2500–3750 nm) which are otherwise difficult to access using conventional hyperspectral imaging (HSI) equipment. The use of such a tool could be beneficial if applied to the paint classification problem and could help analysts map the diversity of pigments within a given painting. The performance of this tool is demonstrated and compared with a conventional, off-the-shelf HSI system operating in the near infrared spectral region (900–1700 nm). Various challenges associated with laser-based imaging are demonstrated and solutions to these challenges as well as the results of applying classification algorithms to datasets captured using both HSI systems are presented. While the conventional HSI system provides data in which more pigments can be accurately classified, the result of applying the proposed laser-based imaging system demonstrates the validity of this technique for application in art authentication tasks.

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