scholarly journals Research of the spectral characteristics of healthy and fusarium-infected oat seeds of the variety “ZALP” with hyperspectral camera

2021 ◽  
Vol 285 ◽  
pp. 02015
Author(s):  
Maksim Moskovskiy ◽  
Stanislav Gerasimenko ◽  
Andrey Boiko ◽  
Sergey Vorobev
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging System ◽  
Spectral Characteristics ◽  
Central Zone ◽  
Spectral Lines ◽  
Hyperspectral Images ◽  
Genus Fusarium ◽  
Oat Seeds ◽  
The Russian Federation ◽  
Hyperspectral Camera

The paper presents the research of the spectral lines of healthy and fusarium-infected oat seeds using a hyperspectral imaging system. Hyperspectral images of healthy and diseased kernels were studied using the Specim IQ hyperspectral camera and Albedo 4.0.23 program (for subsequent processing). The spectral diagrams of the horizon (800 nm - 1000 nm) have been determined. They can identify the oat seeds of the ZALP variety which infected with pathogenic microflora by fungi of the genus Fusarium. Variety harvested in the central zone of the Russian Federation.

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.

Camouflage Target Identification Based on Hyperspectral Imaging System

2011 ◽  
Vol 135-136 ◽  
pp. 341-346
Author(s):  
Na Ding ◽  
Jiao Bo Gao ◽  
Jun Wang
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Target Identification ◽  
Spectral Characteristics ◽  
Tunable Filter ◽  
Fusion Algorithm ◽  
Infrared Band ◽  
Target Segmentation ◽  
Pseudo Color

A novel system of implementing target identification with hyperspectral imaging system based on acousto-optic tunable filter (AOTF) was proposed. The system consists of lens, AOTF, AOTF driver, CCD and image collection installation. Owing to the high spatial and spectral resolution, the system can operate in the spectral range from visible light to near infrared band. An experiment of detecting and recognizing of two different kinds of camouflage armets from background was presented. When the characteristic spectral wave bands are 680nm and 750nm, the two camouflage armets exhibit different spectral characteristic. The target camouflage armets in the hyperspectral images are distinct from background and the contrast of armets and background is increased. The image fusion, target segmentation and pick-up of those images with especial spectral characteristics were realized by the Hyperspectral Imaging System. The 600nm, 680nm, and 750nm images were processed by the Pseudo color fusion algorithm, thus the camouflage armets are more easily observed by naked eyes. Experimental results confirm that AOTF hyperspectral imaging system can acquire image of high contrast, and has the ability of detecting and identification camouflage objects.

scholarly journals Assessing the Spectral Characteristics of Dye- and Pigment-Based Inkjet Prints by VNIR Hyperspectral Imaging

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 603
Author(s):  
Lukáš Krauz ◽  
Petr Páta ◽  
Jan Kaiser
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Hyperspectral Image ◽  
Spectral Characteristics ◽  
Principal Component ◽  
Hyperspectral Image Processing ◽  
Different Types ◽  
The Difference ◽  
Hyperspectral Camera

Fine art photography, paper documents, and other parts of printing that aim to keep value are searching for credible techniques and mediums suitable for long-term archiving purposes. In general, long-lasting pigment-based inks are used for archival print creation. However, they are very often replaced or forged by dye-based inks, with lower fade resistance and, therefore, lower archiving potential. Frequently, the difference between the dye- and pigment-based prints is hard to uncover. Finding a simple tool for countrified identification is, therefore, necessary. This paper assesses the spectral characteristics of dye- and pigment-based ink prints using visible near-infrared (VNIR) hyperspectral imaging. The main aim is to show the spectral differences between these ink prints using a hyperspectral camera and subsequent hyperspectral image processing. Two diverse printers were exploited for comparison, a hobby dye-based EPSON L1800 and a professional pigment-based EPSON SC-P9500. The identical prints created via these printers on three different types of photo paper were recaptured by the hyperspectral camera. The acquired pixel values were studied in terms of spectral characteristics and principal component analysis (PCA). In addition, the obtained spectral differences were quantified by the selected spectral metrics. The possible usage for print forgery detection via VNIR hyperspectral imaging is discussed in the results.

scholarly journals A New Compact VNIR Hyperspectral Imaging System for Non-Invasive Analysis in the FineArt and Architecture Fields

2018 ◽  
pp. 69-74
Author(s):  
Marcello Picollo ◽  
Andrea Casini ◽  
Costanza Cucci ◽  
Jouni Jussila ◽  
Marco Poggesi ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Environmental Conditions ◽  
Imaging System ◽  
Non Invasive ◽  
Nm Range ◽  
Hyperspectral Camera

A new compact Specim IQ hyperspectral camera working in the 400-1000 nm range has been launched on the market. Its use in the investigation of different artworks and under diverse environmental conditions will be presented.

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 Optimal Wavelength Selection for Hyperspectral Imaging Evaluation on Vegetable Soybean Moisture Content during Drying

2019 ◽  
Vol 9 (2) ◽  
pp. 331
Author(s):  
Peng Yu ◽  
Min Huang ◽  
Min Zhang ◽  
Bao Yang
Keyword(s):  
Hyperspectral Imaging ◽  
Multispectral Imaging ◽  
Imaging System ◽  
Hyperspectral Images ◽  
Least Squares Regression ◽  
Wavelength Selection ◽  
Successive Projections Algorithm ◽  
Imaging Evaluation ◽  
Optimal Wavelength

Hyperspectral imaging technology is a promising technique for nondestructive quality evaluation of dried products. In order to realize real-time, online inspection of quality of dried products, it is necessary to determine a few important wavelengths from hyperspectral images for developing a multispectral imaging system. This study presents a binary firework algorithm (BFWA) for selecting the optimal wavelengths from hyperspectral images for moisture evaluation of dried soybean. Hyperspectral images over the spectral region 400–1000 nm, were acquired for 270 dried soybean samples, and mean reflectance was calculated from hyperspectral images for each wavelength. After selecting 12 important wavelengths using BFWA, a moisture prediction model was developed using partial least squares regression (PLSR). The PLSR model with BFWA achieved a prediction accuracy of R p = 0.966 and R M S E P = 5.105 % , which is better than those of successive projections algorithm ( R p = 0.932 and R M S E P = 7.329 % ), and the uninformative viable elimination algorithm ( R p = 0.928 and R M S E P = 7.416 % ). The results obtained by BFWA were more stable, with a smaller standard deviation of R p and R M S E P than those of the genetic algorithm. The BFWA method provides an effective mean for optimal wavelength selection to predict the quality of soybeans during drying.

scholarly journals Hyperspectral Imaging from a Multipurpose Floating Platform to Estimate Chlorophyll-a Concentrations in Irrigation Pond Water

2020 ◽  
Vol 12 (13) ◽  
pp. 2070
Author(s):  
Geonwoo Kim ◽  
Insuck Baek ◽  
Matthew D. Stocker ◽  
Jaclyn E. Smith ◽  
Andrew L. Van Tassell ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Principal Component ◽  
Hyperspectral Images ◽  
Water Sampling ◽  
Floating Platform ◽  
Irrigation Pond

This study provides detailed information about the use of a hyperspectral imaging system mounted on a motor-driven multipurpose floating platform (MFP) for water quality sensing and water sampling, including the spatial and spectral calibration for the camera, image acquisition and correction procedures. To evaluate chlorophyll-a concentrations in an irrigation pond, visible/near-infrared hyperspectral images of the water were acquired as the MFP traveled to ten water sampling locations along the length of the pond, and dimensionality reduction with correlation analysis was performed to relate the image data to the measured chlorophyll-a data. About 80,000 sample images were acquired by the line-scan method. Image processing was used to remove sun-glint areas present in the raw hyperspectral images before further analysis was conducted by principal component analysis (PCA) to extract three key wavelengths (662 nm, 702 nm, and 752 nm) for detecting chlorophyll-a in irrigation water. Spectral intensities at the key wavelengths were used as inputs to two near-infrared (NIR)-red models. The determination coefficients (R2) of the two models were found to be about 0.83 and 0.81. The results show that hyperspectral imagery from low heights can provide valuable information about water quality in a fresh water source.

scholarly journals Research of the spectral characteristics of healthy and fusarium-infected wheat seeds variety Moskovskaya 56 by hyperspectral spectroscopy

2021 ◽  
Vol 285 ◽  
pp. 02016
Author(s):  
Maksim Moskovskiy ◽  
Maksim Litvinov ◽  
Andrey Boiko ◽  
Sergey Maklakov
Keyword(s):  
Winter Wheat ◽  
Hyperspectral Imaging ◽  
Spectral Characteristics ◽  
Spectral Lines ◽  
General Picture ◽  
Wheat Grains ◽  
Spectroscopic Identification ◽  
Soft Winter Wheat ◽  
Grain Surface ◽  
Wheat Seeds

In this paper, a spectroscopic identification method is considered for determining the maxima and minima of spectral lines to identify pathogenic microflora in grain seeds. The paper presents the justification for the application of the method of hyperspectral imaging, in order to identify the disease fusarium in the seeds of soft winter wheat. Based on the graphs, it can see the general picture of the influence of the disease fusarium on wheat grains. There is a general decrease in the reflectivity of the grain surface. The strongest deviation of spectral lines is observed in the limit from 660 nm to 900 nm.

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.

Detection of Defect on Navel Orange Using Hyperspectral Reflectance Image

2011 ◽  
Vol 320 ◽  
pp. 569-573
Author(s):  
Jing Li ◽  
Long Xue ◽  
Mu Hua Liu ◽  
Xiao Wang ◽  
Chun Sheng Luo
Keyword(s):  
Principal Component Analysis ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Principal Component ◽  
Multispectral Image ◽  
Hyperspectral Images ◽  
Navel Orange ◽  
Hyperspectral Reflectance ◽  
Combined Use ◽  
Translation Stage

A hyperspectral imaging system for detecting defect on navel orange was demonstrated. The hyperspectral imaging system, which was a line-scan imaging system, consisted of a hyperspectral camera, a halogen lighting unit, a computer and a translation stage. The imaging system operated from 400 to 1000nm. Principal component analysis (PCA) was performed using the hyperspectral images data (from 500 to 700nm); 2nd principal component (PC) image exhibited differential responses between normal and defect spots on the surface of navel orange. The combined use of the PC-2 images demonstrated the detection of defect spots with minimal false positives. Based on the PC-2 weighing coefficients, the dominant wavelengths were 528,529,530,673,674 and 675nm. This research demonstrated the potential of multispectral image for online applications for detection of defect on navel oranges.

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