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

10.7287/peerj.preprints.26562v1 ◽

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.

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Identification of Leaf-Scale Wheat Powdery Mildew (Blumeria graminis f. sp. Tritici) Combining Hyperspectral Imaging and an SVM Classifier

Plants ◽

10.3390/plants9080936 ◽

2020 ◽

Vol 9 (8) ◽

pp. 936 ◽

Cited By ~ 2

Author(s):

Jinling Zhao ◽

Yan Fang ◽

Guomin Chu ◽

Hao Yan ◽

Lei Hu ◽

...

Keyword(s):

Powdery Mildew ◽

Hyperspectral Imaging ◽

Blumeria Graminis ◽

Machine Learning Algorithms ◽

Plant Diseases ◽

Identification Accuracy ◽

Hyperspectral Images ◽

Support Vector ◽

Svm Classifier ◽

Measurement Technology

Powdery mildew (PM, Blumeria graminis f. sp. tritici) is a devastating disease for wheat growth and production. It is highly meaningful that the disease severities can be objectively and accurately identified by image visualization technology. In this study, an integral method was proposed based on a hyperspectral imaging dataset and machine learning algorithms. The disease severities of wheat leaves infected with PM were quantitatively identified based on hyperspectral images and image segmentation techniques. A technical procedure was proposed to perform the identification and evaluation of leaf-scale wheat PM, specifically including three primary steps of the acquisition and preprocessing of hyperspectral images, the selection of characteristic bands, and model construction. Firstly, three-dimensional reduction algorithms, namely principal component analysis (PCA), random forest (RF), and the successive projections algorithm (SPA), were comparatively used to select the bands that were most sensitive to PM. Then, three diagnosis models were constructed by a support vector machine (SVM), RF, and a probabilistic neural network (PNN). Finally, the best model was selected by comparing the overall accuracies. The results show that the SVM model constructed by PCA dimensionality reduction had the best result, and the classification accuracy reached 93.33% by a cross-validation method. There was an obvious improvement of the identification accuracy with the model, which achieved an 88.00% accuracy derived from the original hyperspectral images. This study can provide a reference for accurately estimating the disease severity of leaf-scale wheat PM and other plant diseases by non-contact measurement technology.

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Hyperspectral Imaging Using Laser Excitation for Fast Raman and Fluorescence Hyperspectral Imaging for Sorting and Quality Control Applications

Journal of Imaging ◽

10.3390/jimaging4100110 ◽

2018 ◽

Vol 4 (10) ◽

pp. 110 ◽

Cited By ~ 4

Author(s):

Florian Gruber ◽

Philipp Wollmann ◽

Wulf Grählert ◽

Stefan Kaskel

Keyword(s):

Quality Control ◽

Hyperspectral Imaging ◽

Lead Zirconate Titanate ◽

Imaging System ◽

Measurement Data ◽

Machine Learning Algorithms ◽

Area Measurement ◽

Large Area ◽

Sorting Tasks

A hyperspectral measurement system for the fast and large area measurement of Raman and fluorescence signals was developed, characterized and tested. This laser hyperspectral imaging system (Laser-HSI) can be used for sorting tasks and for continuous quality monitoring. The system uses a 532 nm Nd:YAG laser and a standard pushbroom HSI camera. Depending on the lens selected, it is possible to cover large areas (e.g., field of view (FOV) = 386 mm) or to achieve high spatial resolutions (e.g., 0.02 mm). The developed Laser-HSI was used for four exemplary experiments: (a) the measurement and classification of a mixture of sulphur and naphthalene; (b) the measurement of carotenoid distribution in a carrot slice; (c) the classification of black polymer particles; and, (d) the localization of impurities on a lead zirconate titanate (PZT) piezoelectric actuator. It could be shown that the measurement data obtained were in good agreement with reference measurements taken with a high-resolution Raman microscope. Furthermore, the suitability of the measurements for classification using machine learning algorithms was also demonstrated. The developed Laser-HSI could be used in the future for complex quality control or sorting tasks where conventional HSI systems fail.

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Identification of Maize Kernel Vigor under Different Accelerated Aging Times Using Hyperspectral Imaging

Molecules ◽

10.3390/molecules23123078 ◽

2018 ◽

Vol 23 (12) ◽

pp. 3078 ◽

Cited By ~ 11

Author(s):

Lei Feng ◽

Susu Zhu ◽

Chu Zhang ◽

Yidan Bao ◽

Xuping Feng ◽

...

Keyword(s):

Aging Time ◽

Hyperspectral Imaging ◽

Accelerated Aging ◽

Principal Component ◽

Seed Vigor ◽

Support Vector ◽

Classification Models ◽

Maize Kernel ◽

Seed Aging ◽

Maize Kernels

Seed aging during storage is irreversible, and a rapid, accurate detection method for seed vigor detection during seed aging is of great importance for seed companies and farmers. In this study, an artificial accelerated aging treatment was used to simulate the maize kernel aging process, and hyperspectral imaging at the spectral range of 874–1734 nm was applied as a rapid and accurate technique to identify seed vigor under different accelerated aging time regimes. Hyperspectral images of two varieties of maize processed with eight different aging duration times (0, 12, 24, 36, 48, 72, 96 and 120 h) were acquired. Principal component analysis (PCA) was used to conduct a qualitative analysis on maize kernels under different accelerated aging time conditions. Second-order derivatization was applied to select characteristic wavelengths. Classification models (support vector machine−SVM) based on full spectra and optimal wavelengths were built. The results showed that misclassification in unprocessed maize kernels was rare, while some misclassification occurred in maize kernels after the short aging times of 12 and 24 h. On the whole, classification accuracies of maize kernels after relatively short aging times (0, 12 and 24 h) were higher, ranging from 61% to 100%. Maize kernels with longer aging time (36, 48, 72, 96, 120 h) had lower classification accuracies. According to the results of confusion matrixes of SVM models, the eight categories of each maize variety could be divided into three groups: Group 1 (0 h), Group 2 (12 and 24 h) and Group 3 (36, 48, 72, 96, 120 h). Maize kernels from different categories within one group were more likely to be misclassified with each other, and maize kernels within different groups had fewer misclassified samples. Germination test was conducted to verify the classification models, the results showed that the significant differences of maize kernel vigor revealed by standard germination tests generally matched with the classification accuracies of the SVM models. Hyperspectral imaging analysis for two varieties of maize kernels showed similar results, indicating the possibility of using hyperspectral imaging technique combined with chemometric methods to evaluate seed vigor and seed aging degree.

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

Sensors ◽

10.3390/s19143147 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3147 ◽

Cited By ~ 6

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.

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

Applied Sciences ◽

10.3390/app10031173 ◽

2020 ◽

Vol 10 (3) ◽

pp. 1173 ◽

Cited By ~ 2

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.

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

Applied Engineering in Agriculture ◽

10.13031/aea.12679 ◽

2018 ◽

Vol 34 (5) ◽

pp. 789-798 ◽

Cited By ~ 3

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.

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

Sensors ◽

10.3390/s19010097 ◽

2018 ◽

Vol 19 (1) ◽

pp. 97 ◽

Cited By ~ 4

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.

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Studies on the infectivity of Diplostomum spathaceum in rainbow trout (Oncorhynchus mykiss)

Journal of Helminthology ◽

10.1017/s0022149x0001066x ◽

1991 ◽

Vol 65 (3) ◽

pp. 169-178 ◽

Cited By ~ 40

Author(s):

S. K. Whyte ◽

C. J. Secombes ◽

L. H. Chappell

Keyword(s):

Rainbow Trout ◽

Oncorhynchus Mykiss ◽

Entire Body ◽

Fish Skin ◽

Tail Region ◽

Routes Of Administration ◽

Rainbow Trout Oncorhynchus Mykiss ◽

Diplostomum Spathaceum

ABSTRACTThe infectivity of Diplostomum spathaceum (Digenea: Trematoda) cercariae to rainbow trout and the efficacy of the diplostomule migration to the lens following different routes of administration was examined. The optimum age of infectivity for cercariae was between 0–5 h after liberation from the snail and for intraperitoneally injected diplostomules, 5 h post-transformation in vitro through fish skin. After exposure of the entire fish body or head to cercariae, metacercariae first appeared in the lens at 5 h and their numbers gradually increased until 22 h. Following exposure of the tail region of rainbow trout to cercariae, metacercariae first appeared in the lens at 14 h. Significantly more metacercariae established in the lens of fish following exposure of the fish head compared with the tail region; 40% of penetrating cercariae reached the lens of fish following exposure of the head or entire body, 20% of cercariae or diplostomules injected either intraperitoneally, intramuscularly or intracardially reached the lens while only 5% of cercariae established as metacercariae following exposure of the tail region.

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Recognition of Maize Phenology in Sentinel Images with Machine Learning

Sensors ◽

10.3390/s22010094 ◽

2021 ◽

Vol 22 (1) ◽

pp. 94

Author(s):

Alvaro Murguia-Cozar ◽

Antonia Macedo-Cruz ◽

Demetrio Salvador Fernandez-Reynoso ◽

Jorge Arturo Salgado Transito

Keyword(s):

Machine Learning ◽

Satellite Image ◽

Spatial Association ◽

Machine Learning Algorithms ◽

Support Vector ◽

Classification Models ◽

Phenological Stages ◽

Area Index ◽

Stage Of Development ◽

Sentinel 2

The scarcity of water for agricultural use is a serious problem that has increased due to intense droughts, poor management, and deficiencies in the distribution and application of the resource. The monitoring of crops through satellite image processing and the application of machine learning algorithms are technological strategies with which developed countries tend to implement better public policies regarding the efficient use of water. The purpose of this research was to determine the main indicators and characteristics that allow us to discriminate the phenological stages of maize crops (Zea mays L.) in Sentinel 2 satellite images through supervised classification models. The training data were obtained by monitoring cultivated plots during an agricultural cycle. Indicators and characteristics were extracted from 41 Sentinel 2 images acquired during the monitoring dates. With these images, indicators of texture, vegetation, and colour were calculated to train three supervised classifiers: linear discriminant (LD), support vector machine (SVM), and k-nearest neighbours (kNN) models. It was found that 45 of the 86 characteristics extracted contributed to maximizing the accuracy by stage of development and the overall accuracy of the trained classification models. The characteristics of the Moran’s I local indicator of spatial association (LISA) improved the accuracy of the classifiers when applied to the L*a*b* colour model and to the near-infrared (NIR) band. The local binary pattern (LBP) increased the accuracy of the classification when applied to the red, green, blue (RGB) and NIR bands. The colour ratios, leaf area index (LAI), RGB colour model, L*a*b* colour space, LISA, and LBP extracted the most important intrinsic characteristics of maize crops with regard to classifying the phenological stages of the maize cultivation. The quadratic SVM model was the best classifier of maize crop phenology, with an overall accuracy of 82.3%.

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