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 Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties

Molecules ◽  
2019 ◽  
Vol 24 (18) ◽  
pp. 3268 ◽  
Author(s):  
Zhu ◽  
Zhou ◽  
Gao ◽  
Bao ◽  
He ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Cotton Seed ◽  
Near Infrared ◽  
Principal Component ◽  
Critical Factor ◽  
Support Vector ◽  
Classification Models ◽  
Cotton Seeds ◽  
Better Than

Cotton seed purity is a critical factor influencing the cotton yield. In this study, near-infrared hyperspectral imaging was used to identify seven varieties of cotton seeds. Score images formed by pixel-wise principal component analysis (PCA) showed that there were differences among different varieties of cotton seeds. Effective wavelengths were selected according to PCA loadings. A self-design convolution neural network (CNN) and a Residual Network (ResNet) were used to establish classification models. Partial least squares discriminant analysis (PLS-DA), logistic regression (LR) and support vector machine (SVM) were used as direct classifiers based on full spectra and effective wavelengths for comparison. Furthermore, PLS-DA, LR and SVM models were used for cotton seeds classification based on deep features extracted by self-design CNN and ResNet models. LR and PLS-DA models using deep features as input performed slightly better than those using full spectra and effective wavelengths directly. Self-design CNN based models performed slightly better than ResNet based models. Classification models using full spectra performed better than those using effective wavelengths, with classification accuracy of calibration, validation and prediction sets all over 80% for most models. The overall results illustrated that near-infrared hyperspectral imaging with deep learning was feasible to identify cotton seed varieties.

scholarly journals Pola Penurunan Viabilitas dan Pengembangan Metode Pendugaan Vigor Daya Simpan Benih Pepaya (Carica Papaya L.)

2017 ◽  
Vol 7 (3) ◽  
pp. 146
Author(s):  
Astryani Rosyad ◽  
M. Rahmad Suhartanto ◽  
Abdul Qadir
Keyword(s):  
Moisture Content ◽  
Aging Time ◽  
Seed Quality ◽  
Initial Moisture Content ◽  
Accelerated Aging ◽  
Storage Period ◽  
Seed Storage ◽  
Seed Vigor ◽  
Develop Model ◽  
Papaya Seed

<p>ABSTRACT<br />Information of seed quality during storage can be determined through the actual storage and storability vigor estimation. This study aimed at comparing effective accelerated aging method<br />between physical and chemical, and studying the seed deterioration during storage in ambient (T =28-30 0C, RH=75-78%) and AC (T =18-20 0C, RH =51-60%) condition with three levels of initial moisture content (8-10%, 10-12%, and 12-14%) for 20 weeks. The final objective of this research<br />was to develop model for storability vigor of papaya seed. Two experiments, accelerated aging and seed storage were conducted at Seed Laboratory, Department of Agronomy and Horticulture, Bogor Agricultural University from October 2015 to May 2016. A completely randomized design with nested factors and four replications was applied to both experiments. The results showed that physical accelerated aging using IPB 77-1 MMM machine was more effective than chemical accelerated aging using IPB 77-1 MM machine for papaya seed. The viability of seed stored in AC condition remained high until the end of the storage period, whereas it declined at 16 week storage period in the ambient condition. The viability of seed with initial moisture content of 12-14% declined faster than that of initial moisture content of 8-10% after 18 week storage periode. The model used to estimate the storability vigor of papaya seed accurately was the equation y = a + b expcx where y : storability vigor estimation, x : aging time and a,b,c : constant value. Simulation of storability vigor estimation with constant value of a, b, c and input of aging time can estimate storability seed vigor in actual storage.<br />Keywords: accelerated aging, IPB 77-1 MM machine, IPB 77-1 MMM machine, seed storage, simulation</p><p>ABSTRAK<br />Informasi mutu benih selama penyimpanan dapat diketahui melalui penyimpanan secara aktual dan pendugaan vigor daya simpan. Penelitian ini bertujuan untuk membandingkan metode<br />pengusangan cepat yang efektif antara fisik dengan kimia serta mempelajari pola penurunan viabilitas benih selama penyimpanan aktual pada kondisi simpan kamar (suhu =28-30 0C, RH =75-78%) dan AC (suhu =18-20 0C, RH =51-60%) dengan tiga tingkat kadar air awal (8-10%, 10-12%, dan 12-14%) selama 20 minggu. Tujuan akhirnya adalah membangun model vigor daya simpan benih pepaya. Penelitian pengusangan cepat dan penyimpanan dilakukan pada bulan Oktober 2015 sampai Mei 2016 di Laboratorium Benih, Departemen Agronomi dan Hortikultura, Institut Pertanian<br />Bogor. Kedua penelitian menggunakan rancangan acak lengkap tersarang dengan empat ulangan. Hasil penelitian menunjukkan bahwa pengusangan cepat secara fisik dengan alat IPB 77-1 MMM lebih efektif daripada pengusangan kimia dengan alat IPB 77-1 MM untuk benih pepaya. Viabilitas benih yang disimpan pada kondisi AC tetap tinggi hingga akhir periode simpan, sedangkan pada kondisi kamar penurunan viabilitas dimulai pada periode simpan 16 minggu. Benih yang disimpan dengan tingkat KA awal sebesar 12-14% lebih cepat mengalami penurunan viabilitas mulai periode simpan 18 minggu dibandingkan dengan benih dengan KA awal 8-10%. Hasil penelitian juga menunjukkan terdapat korelasi yang erat antara pola kemunduran benih pada pengusangan cepat dan penyimpanan aktual, sehingga model pendugaan vigor daya simpan (y) berdasarkan waktu pengusangan (x) dapat disusun dengan persamaan y = a + b expcx. Simulasi pendugaan vigor daya simpan dengan nilai konstanta a, b, dan c serta input waktu pengusangan dapat menduga vigor daya simpan benih selama penyimpanan aktual.<br />Kata kunci: alat IPB 77-1 MM, alat IPB 77-1 MMM, pengusangan cepat, penyimpanan benih,<br />simulasi</p>

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 Identification of Weeds Based on Hyperspectral Imaging and Machine Learning

2021 ◽  
Vol 11 ◽  
Author(s):  
Yanjie Li ◽  
Mahmoud Al-Sarayreh ◽  
Kenji Irie ◽  
Deborah Hackell ◽  
Graeme Bourdot ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hyperspectral Imaging ◽  
Support Vector ◽  
Classification Models ◽  
Imaging Data ◽  
Weed Species ◽  
Automated Identification ◽  
Standard Normal Variate ◽  
Californian Thistle ◽  
Ranunculus Acris

Weeds can be major environmental and economic burdens in New Zealand. Traditional methods of weed control including manual and chemical approaches can be time consuming and costly. Some chemical herbicides may have negative environmental and human health impacts. One of the proposed important steps for providing alternatives to these traditional approaches is the automated identification and mapping of weeds. We used hyperspectral imaging data and machine learning to explore the possibility of fast, accurate and automated discrimination of weeds in pastures where ryegrass and clovers are the sown species. Hyperspectral images from two grasses (Setaria pumila [yellow bristle grass] and Stipa arundinacea [wind grass]) and two broad leaf weed species (Ranunculus acris [giant buttercup] and Cirsium arvense [Californian thistle]) were acquired and pre-processed using the standard normal variate method. We trained three classification models, namely partial least squares-discriminant analysis, support vector machine, and Multilayer Perceptron (MLP) using whole plant averaged (Av) spectra and superpixels (Sp) averaged spectra from each weed sample. All three classification models showed repeatable identification of four weeds using both Av and Sp spectra with a range of overall accuracy of 70–100%. However, MLP based on the Sp method produced the most reliable and robust prediction result (89.1% accuracy). Four significant spectral regions were found as highly informative for characterizing the four weed species and could form the basis for a rapid and efficient methodology for identifying weeds in ryegrass/clover pastures.

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.

A Novel Estimation Method of Fatigue Using EEG Based on KPCA-SVM and Complexity Parameters

2013 ◽  
Vol 373-375 ◽  
pp. 965-969 ◽  
Author(s):  
Yi Jun Xiong ◽  
Rong Zhang ◽  
Chong Zhang ◽  
Xiao Lin Yu
Keyword(s):  
Estimation Method ◽  
Principal Component ◽  
Approximate Entropy ◽  
Mental Fatigue ◽  
Kernel Principal Component Analysis ◽  
Experimental Result ◽  
Support Vector ◽  
Classification Models ◽  
Eeg Data ◽  
Nonlinear Features

In this study, Kolmogorov complexity (KC) and approximate Entropy (AE) were adopted to characterize the irregularity and complexity of EEG data. Fifty subjects were instructed to perform two different mental tasks to induce two kinds of fatigue states. Then the Kernel Principal Component Analysis (KPCA) and Support Vector Machine (SVM) are combined to differentiate these two states. The KPCA was used to extract nonlinear features from the complexity parameters of EEG and to effectively reduce the dimensionality of the feature vectors. SVM was used to classify two fatigue states. The experimental result shows that complexity parameters are significantly decreased as the fatigue level increases, which suggests that the proposed parameters can be used to characterize mental fatigue level. Furthermore, compared with several typical classification models, the joint method KPCA-SVM can achieve higher classification accuracy (85%) of mental fatigue with less training and classifying time, which indicates that KPCA-SVM is suitable for the estimation of mental fatigue.

scholarly journals Rapid and Nondestructive On-Site Classification Method for Consumer-Grade Plastics Based on Portable NIR Spectrometer and Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yinglin Yang ◽  
Xin Zhang ◽  
Jianwei Yin ◽  
Xiangyang Yu
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Diffuse Reflectance Spectrum ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Site Classification ◽  
Classification Models ◽  
K Nearest Neighbor

The classification of plastic waste before recycling is of great significance to achieve effective recycling. In order to achieve rapid, nondestructive, and on-site detection, a portable near-infrared spectrometer was used in this study to obtain the diffuse reflectance spectrum for both standard and commercial plastics made by ABS, PC, PE, PET, PP, PS, and PVC. After applying a series of pretreatments, the principal component analysis (PCA) was used to analyze the cluster trend. K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural network (BPNN) classification models were developed and evaluated, respectively. The result showed that different plastics could be well separated in top three principal components space after pretreatment, and the classification models performed excellent classification results and high generalization capability. This study indicated that the portable NIR spectrometer, integrated with chemometrics, could achieve excellent performance and has great potential in the field of commercial plastic identification.

scholarly journals Identifying Freshness of Spinach Leaves Stored at Different Temperatures Using Hyperspectral Imaging

Foods ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 356 ◽  
Author(s):  
Zhu ◽  
Feng ◽  
Zhang ◽  
Bao ◽  
He
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Principal Component ◽  
Imaging Systems ◽  
Support Vector ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Different Temperatures ◽  
Learning Machine ◽  
Spinach Leaves

Spinach is prone to spoilage in the course of preservation. Spinach leaves stored at different temperatures for different durations will have varying degrees of freshness. In order to monitor the freshness of spinach leaves during storage, a rapid and non-destructive method—hyperspectral imaging technology—was applied in this study. Visible near-infrared reflectance (Vis-NIR) (380–1030 nm) and near-infrared reflectance (NIR) (874–1734 nm) hyperspectral imaging systems were used. Spinach leaves preserved at different temperatures with different durations (0, 3, 6, 9 days at 4 °C and 0, 1, 2 days at 20 °C) were studied. Principal component analysis (PCA) was adopted as a qualitative analysis method. The second-order derivative spectra were utilized to select effective wavelengths. Partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and extreme learning machine (ELM) were used to build models based on full spectra and effective wavelengths. All three models achieved good results, with accuracies above 92% for both Vis-NIR spectra and NIR spectra. ELM obtained the best results, with all accuracies reaching 100%. The overall results indicate the possibility of the freshness identification of spinach preserved at different temperatures for different durations using two kinds of hyperspectral imaging systems.

scholarly journals Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics

2019 ◽  
Vol 9 (19) ◽  
pp. 4119 ◽  
Author(s):  
Yidan Bao ◽  
Chunxiao Mi ◽  
Na Wu ◽  
Fei Liu ◽  
Yong He
Keyword(s):  
Hyperspectral Imaging ◽  
Large Scale ◽  
Scatter Correction ◽  
Principal Component ◽  
Support Vector ◽  
Wheat Grain ◽  
Successive Projections Algorithm ◽  
Accurate Identification ◽  
Wheat Varieties

The classification of wheat grain varieties is of great value because its high purity is the yield and quality guarantee. In this study, hyperspectral imaging combined with the chemometric methods was applied to explore and implement the varieties classification of wheat seeds. The hyperspectral images of all the samples covering 874–1734 nm bands were collected. Exploratory analysis was first carried out while using principal component analysis (PCA) and linear discrimination analysis (LDA). Spectral preprocessing methods including standard normal variate (SNV), multiplicative scatter correction (MSC), and wavelet transform (WT) were introduced, and their effects on discriminant models were studied to eliminate the interference of instrumental and environmental factors. PCA loading, successive projections algorithm (SPA), and random frog (RF) were applied to extract feature wavelengths for redundancy elimination owing to the possibility of existing redundant spectral information. Classification models were developed based on full wavelengths and feature wavelengths using LDA, support vector machine (SVM), and extreme learning machine (ELM). This optimal model was finally utilized to generate visualization map to observe the classification performance intuitively. When comparing with other models, ELM based on full wavelengths achieved the best accuracy up to 91.3%. The overall results suggested that hyperspectral imaging was a potential tool for the rapid and accurate identification of wheat varieties, which could be conducted in large-scale seeds classification and quality detection in modern seed industry.

scholarly journals Discrimination of Chrysanthemum Varieties Using Hyperspectral Imaging Combined with a Deep Convolutional Neural Network

Molecules ◽  
2018 ◽  
Vol 23 (11) ◽  
pp. 2831 ◽  
Author(s):  
Na Wu ◽  
Chu Zhang ◽  
Xiulin Bai ◽  
Xiaoyue Du ◽  
Yong He
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Hyperspectral Imaging ◽  
Evaluation System ◽  
Principal Component ◽  
Deep Convolutional Neural Network ◽  
Support Vector ◽  
Training Set ◽  
Derivative Method ◽  
Discriminant Models

Rapid and accurate discrimination of Chrysanthemum varieties is very important for producers, consumers and market regulators. The feasibility of using hyperspectral imaging combined with deep convolutional neural network (DCNN) algorithm to identify Chrysanthemum varieties was studied in this paper. Hyperspectral images in the spectral range of 874–1734 nm were collected for 11,038 samples of seven varieties. Principal component analysis (PCA) was introduced for qualitative analysis. Score images of the first five PCs were used to explore the differences between different varieties. Second derivative (2nd derivative) method was employed to select optimal wavelengths. Support vector machine (SVM), logistic regression (LR), and DCNN were used to construct discriminant models using full wavelengths and optimal wavelengths. The results showed that all models based on full wavelengths achieved better performance than those based on optimal wavelengths. DCNN based on full wavelengths obtained the best results with an accuracy close to 100% on both training set and testing set. This optimal model was utilized to visualize the classification results. The overall results indicated that hyperspectral imaging combined with DCNN was a very powerful tool for rapid and accurate discrimination of Chrysanthemum varieties. The proposed method exhibited important potential for developing an online Chrysanthemum evaluation system.

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