Development of sugarcane and trash identification system in sugar production using hyperspectral imaging

2020 ◽  
Vol 28 (3) ◽  
pp. 133-139
Author(s):  
Kittipon Aparatana ◽  
Khwantri Saengprachatanarug ◽  
Yoshinari Izumikawa ◽  
Shinya Nakamura ◽  
Eizo Taira
Keyword(s):  
Hyperspectral Imaging ◽  
Multivariate Analyses ◽  
Multiple Linear Regression Model ◽  
Principal Component ◽  
Support Vector ◽  
Identification System ◽  
Sugar Production ◽  
Classification Rate ◽  
Chlorophyll Absorption ◽  
Reflectance Data

Classification and differentiation of clean sugarcane from trash (green sugarcane leaf, dry sugarcane leaf, stone, and soil) are important for the sugar payment system at a sugar mill. Currently, the methods used to do this are manual and subjective. Therefore, this study is aimed at accurately differentiating clean sugarcane from trash by using hyperspectral imaging with multivariate analyses. Samples containing sugarcane billets and trash mixed in a ratio of 18:38 were analyzed in this study. The reflectance data of the samples were analyzed in the wavelength range of 400–1000 nm via principal component analysis (PCA). The PCA model was capable of identifying all of the clean sugarcane and trash samples. The spectral loadings of the PCA model show that the sugarcane and trash samples are easily identifiable based on the color (visible light) of each class, water absorption (approximately 970 nm), and chlorophyll absorption (approximately 680 nm). Based on the characteristic wavelengths of the PCA loading peaks, over 90% of the sugarcane and trash samples were differentiated using a multiple linear regression model. Sugarcane and trash are classified by using partial least-squares discriminant analysis and support vector machine models. For all wavelengths, the classification rate is 92.9% and 98.2%, respectively. This shows that sugarcane and trash can be accurately classified and differentiated by using hyperspectral imaging and multivariate analyses.

scholarly journals Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics

2021 ◽  
Vol 11 (19) ◽  
pp. 9124
Author(s):  
Hongzhe Jiang ◽  
Liancheng Ye ◽  
Xingpeng Li ◽  
Minghong Shi
Keyword(s):  
Hyperspectral Imaging ◽  
Nearest Neighbor ◽  
Characteristic Curve ◽  
Principal Component ◽  
Hyperspectral Images ◽  
Variety Identification ◽  
Support Vector ◽  
Identification System ◽  
K Nearest Neighbor ◽  
Chinese Walnut

Chinese walnuts have extraordinary nutritional and organoleptic qualities, and counterfeit Chinese walnut products are pervasive in the market. The aim of this study was to investigate the feasibility of hyperspectral imaging (HSI) technique to accurately identify and visualize Chinese walnut varieties. Hyperspectral images of 400 Chinese walnuts including 200 samples of Ningguo variety and 200 samples of Lin’an variety were acquired in range of 400–1000 nm. Spectra were extracted from representative regions of interest (ROIs), and principal component analysis (PCA) of spectra showed that the characteristic second principal component (PC2) was potentially effective in variety identification. The PC transformation was also conducted to hyperspectral images to make an exploratory visualization according to pixel-wise PC scores. Three different modeling methods including partial least squares-discriminant analysis (PLS-DA), k-nearest neighbor (KNN), and support vector machine (SVM) were individually employed to develop classification models. Results indicated that raw full spectra constructed PLS-DA model performed best with correct classification rates (CCRs) of 97.33%, 95.33%, and 92.00% in calibration, cross-validation, and prediction sets, respectively. Successful projects algorithm (SPA), competitive adaptive reweighted sampling (CARS), and PC loadings were individually used for effective wavelengths selection. Subsequently, simplified PLS-DA model based on wavelengths selected by CARS yielded the best 96.33%, 95.67% and 91.00% CCRs in the three sets. This optimal CARS-PLS-DA model acquired a sensitivity of 93.62%, a specificity of 88.68%, the area under the receiver operating characteristic curve (AUC) value of 0.91, and Kappa coefficient of 0.82 in prediction set. Classification maps were finally generated by classifying the varieties of each pixel in multispectral images at CARS-selected wavelengths, and the general variety was then readily discernible. These results demonstrated that features extracted from HSI had outstanding ability, and could be applied as a reliable tool for the further development of an on-line identification system for Chinese walnut variety.

scholarly journals Chemometrics in Tandem with Hyperspectral Imaging for Detecting Authentication of Raw and Cooked Mutton Rolls

Foods ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 2127
Author(s):  
Hongzhe Jiang ◽  
Yi Yang ◽  
Minghong Shi
Keyword(s):  
Hyperspectral Imaging ◽  
Meat Products ◽  
Principal Component ◽  
Support Vector ◽  
Meat Industry ◽  
Successive Projections Algorithm ◽  
Classification Rate ◽  
Meat Species ◽  
Spectral Principal Component Analysis ◽  
Cooked Meat

Authentication assurance of meat or meat products is critical in the meat industry. Various methods including DNA- or protein-based techniques are accurate for assessing meat authenticity, however, they are destructive, expensive, or laborious. This study explores the feasibility of chemometrics in tandem with hyperspectral imaging (HSI) for identifying raw and cooked mutton rolls substitution by pork and duck rolls. Raw or cooked samples (n = 180) of three meat species were prepared to collect hyperspectral images in range of 400–1000 nm. Spectra were extracted from representative regions of interest (ROIs), and spectral principal component analysis (PCA) revealed that PC1 and PC2 were effective for the identification. Different methods including standard normal variable (SNV), first and second derivatives, and normalization were individually employed for spectral preprocessing, and modeling methods of partial least squares-discriminant analysis (PLS-DA) and support vector machines (SVM) were also individually applied to develop classification models for both the raw and the cooked. Results showed that PLS-DA model developed by raw spectra presented the highest 100% correct classification rate (CCR) of success in all sets. After that, effective wavelengths selected by successive projections algorithm (SPA) built optimal simplified models which didn’t influence the modeling results compared with full spectra regardless of the meat roll states. Therefore, SPA-PLS-DA models were subsequently used to visualize the raw and cooked meat rolls classification. As a consequence, the general meat species of both raw and cooked meat rolls were readily discernible in pixel-wise manner by generating classification maps. The results showed that HSI combined with chemometrics can be used to identify the authentication of raw and cooked mutton rolls substituted by pork and duck rolls accurately. This promising methodology provides a reference which can be extended to the classification or grading of other meat rolls.

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

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3078 ◽  
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.

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 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.

Influence of the Intra-Modal Facial Information for an Identification Approach

2014 ◽  
pp. 318-342
Author(s):  
Carlos M. Travieso ◽  
Marcos del Pozo-Baños ◽  
Jaime R. Ticay-Rivas ◽  
Jesús B. Alonso
Keyword(s):  
Principal Component ◽  
Component Analysis ◽  
Multimodal Fusion ◽  
Support Vector ◽  
Identification System ◽  
Facial Features ◽  
Classifier Systems ◽  
Lighting Conditions ◽  
Facial Information ◽  
Identification Approach

This chapter presents a comprehensive study on the influence of the intra-modal facial information for an identification approach. It was developed and implemented a biometric identification system by merging different intra-multimodal facial features: mouth, eyes, and nose. The Principal Component Analysis, Independent Component Analysis, and Discrete Cosine Transform were used as feature extractors. Support Vector Machines were implemented as classifier systems. The recognition rates obtained by multimodal fusion of three facial features has reached values above 97% in each of the databases used, confirming that the system is adaptive to images from different sources, sizes, lighting conditions, etc. Even though a good response has been shown when the three facial traits were merged, an acceptable performance has been shown when merging only two facial features. Therefore, the system is robust against problems in one isolate sensor or occlusion in any biometric trait. In this case, the success rate achieved was over 92%.

The recognition of plastic bottle using linear multi hierarchical SVM classifier

2021 ◽  
pp. 1-14
Author(s):  
LiHua Cai ◽  
Jin Cao ◽  
MingQiang Wang ◽  
Ta Zhou ◽  
HaiFeng Fang
Keyword(s):  
Loss Function ◽  
Deep Structure ◽  
Principal Component ◽  
Classification Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Input Matrix ◽  
Classification Rate ◽  
Pet Bottles ◽  
Combination Methods

Both classification rate and accuracy are crucial for the recyclable PET bottles, and the existing combination methods of SVM all simply use SVM as the unit classifier, ignoring the improvement of SVM’s classification performance in the training process of deep learning. A linear multi hierarchical deep structure based on Support Vector Machine (SVM) is proposed to cover this problem. A novel definition of the input matrix in each layer enhances the optimization of Lagrange multipliers in Sequential Minimal Optimization (SMO) algorithm, thus the datapoint in maximum interval of SVM hyperplane could be recognized, improving the classification performance of SVM classifier in this layer. The loss function defined in this paper could control the depth of Linear Multi Hierarchical SVM (LMHSVM), the generalization parameters are added in the loss function and the input matrix to enhance the generalization performance of LMHSVM. The process of creating Bottle dataset by Histogram of Oriented Gradient (HOG) and Principal Component Analysis (PCA) is introduced meanwhile, reducing the data size of bottles. Experiments are conducted on LMHSVM and multiple typical classification algorithms with Bottle dataset and UCI datasets, the results indicated that LMHSVM has excellent classification performances than FNN classifier, LIBSVM (Gaussian) and GFS-AdaBoost-C in KEEL.

scholarly journals Support Vector Machine for Behavior-Based Driver Identification System

2010 ◽  
Vol 2010 ◽  
pp. 1-11 ◽  
Author(s):  
Huihuan Qian ◽  
Yongsheng Ou ◽  
Xinyu Wu ◽  
Xiaoning Meng ◽  
Yangsheng Xu
Keyword(s):  
Support Vector Machine ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Identification System ◽  
Dynamic Features ◽  
Experimental Platform ◽  
Driving Behaviors ◽  
The Individual ◽  
Driver Identification

We present an intelligent driver identification system to handle vehicle theft based on modeling dynamic human behaviors. We propose to recognize illegitimate drivers through their driving behaviors. Since human driving behaviors belong to a dynamic biometrical feature which is complex and difficult to imitate compared with static features such as passwords and fingerprints, we find that this novel idea of utilizing human dynamic features for enhanced security application is more effective. In this paper, we first describe our experimental platform for collecting and modeling human driving behaviors. Then we compare fast Fourier transform (FFT), principal component analysis (PCA), and independent component analysis (ICA) for data preprocessing. Using machine learning method of support vector machine (SVM), we derive the individual driving behavior model and we then demonstrate the procedure for recognizing different drivers by analyzing the corresponding models. The experimental results of learning algorithms and evaluation are described.

A REAL TIME FACE CLASSIFICATION AND COUNTING SYSTEM

2008 ◽  
Vol 05 (01) ◽  
pp. 1-10 ◽  
Author(s):  
XINYU WU ◽  
YONGSHENG OU ◽  
HUIHUAN QIAN ◽  
YANGSHENG XU
Keyword(s):  
Real Time ◽  
Principal Component ◽  
Classification Problem ◽  
Component Analysis ◽  
Support Vector ◽  
Feature Generation ◽  
Training Process ◽  
Classification Rate ◽  
Counting System ◽  
Face Images

This paper introduces a face detection, classification and counting system that is robust and works in real-time. It tracks multiple people, which is useful for face counting. The classification problem is defined as differentiating and then classifying the front of a face into Asian or non-Asian categories. The first step, is principal component analysis (PCA) for feature generation and independent component analysis (ICA) for feature extraction. Then, we employ support vector machine (SVM) for the training process and combine different SVM classifiers to create new classifiers, which improves the classification rate. Based on this, we can count the number of Asians and non-Asians. Experiments show that our system achieves a classification rate of 82.5% based on a database containing 750 face images from FERET.

scholarly journals Comparative Analysis of Classifiers for Developing an Adaptive Computer-Assisted EEG Analysis System for Diagnosing Epilepsy

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Malik Anas Ahmad ◽  
Yasar Ayaz ◽  
Mohsin Jamil ◽  
Syed Omer Gillani ◽  
Muhammad Babar Rasheed ◽  
...  
Keyword(s):  
Principal Component ◽  
Ground Truth ◽  
Performance Comparison ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Computer Assisted ◽  
Discrete Wavelet ◽  
Eeg Analysis ◽  
Classification Rate ◽  
Analysis System

Computer-assisted analysis of electroencephalogram (EEG) has a tremendous potential to assist clinicians during the diagnosis of epilepsy. These systems are trained to classify the EEG based on the ground truth provided by the neurologists. So, there should be a mechanism in these systems, using which a system’s incorrect markings can be mentioned and the system should improve its classification by learning from them. We have developed a simple mechanism for neurologists to improve classification rate while encountering any false classification. This system is based on taking discrete wavelet transform (DWT) of the signals epochs which are then reduced using principal component analysis, and then they are fed into a classifier. After discussing our approach, we have shown the classification performance of three types of classifiers: support vector machine (SVM), quadratic discriminant analysis, and artificial neural network. We found SVM to be the best working classifier. Our work exhibits the importance and viability of a self-improving and user adapting computer-assisted EEG analysis system for diagnosing epilepsy which processes each channel exclusive to each other, along with the performance comparison of different machine learning techniques in the suggested system.

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