scholarly journals Soil Nutrient Estimation and Mapping in Farmland Based on UAV Imaging Spectrometry

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3919
Author(s):  
Xiaoyu Yang ◽  
Nisha Bao ◽  
Wenwen Li ◽  
Shanjun Liu ◽  
Yanhua Fu ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Signal To Noise Ratio ◽  
Feature Selection Method ◽  
Soil Nutrient ◽  
Pso Algorithm ◽  
Support Vector ◽  
Least Squares Regression ◽  
Successive Projections Algorithm ◽  
Imaging Spectrometry ◽  
Rapid Acquisition

Soil nutrient is one of the most important properties for improving farmland quality and product. Imaging spectrometry has the potential for rapid acquisition and real-time monitoring of soil characteristics. This study aims to explore the preprocessing and modeling methods of hyperspectral images obtained from an unmanned aerial vehicle (UAV) platform for estimating the soil organic matter (SOM) and soil total nitrogen (STN) in farmland. The results showed that: (1) Multiplicative Scattering Correction (MSC) performed better in reducing image scattering noise than Standard Normal Variate (SNV) transformation or spectral derivatives, and it yielded a result with higher correlation and lower signal-to-noise ratio; (2) The proposed feature selection method combining Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling algorithm (CARS), could provide selective preference for hyperspectral bands. Exploiting this method, 24 and 22 feature bands were selected for SOM and STN estimation, respectively; (3) The particle swarm optimization (PSO) algorithm was employed to obtain optimized input weights and bias values of the extreme learning machine (ELM) model for more accurate prediction of SOM and STN. The improved PSO-ELM model based on the selected preference bands achieved higher prediction accuracy (R2 of 0.73 and RPD of 1.91 for SOM, R2 of 0.63, and RPD of 1.53 for STN) than support vector machine (SVM), partial least squares regression (PLSR), and the ELM model. This study provides an important guideline for monitoring soil nutrient for precision agriculture with imaging spectrometry.

Soil nutrient estimation and mapping in agriculture land based on improved ELM and UAV imaging spectrometry

2021 ◽  
Author(s):  
Nisha Bao ◽  
Xiaoyu Yang ◽  
Yue Cao
Keyword(s):  
Hyperspectral Image ◽  
Signal To Noise Ratio ◽  
Feature Selection Method ◽  
Soil Nutrient ◽  
Pso Algorithm ◽  
Correction Method ◽  
Successive Projections Algorithm ◽  
Imaging Spectrometry ◽  
Rapid Acquisition ◽  
Standard Normal Variate

<p>Soil nutrient is one of the most important properties to support farmland quality and product. Imaging spectrometry has the potential for rapid acquisition and real-time monitoring of soil characteristics. The goal of this study was to explore the preprocessing and modeling method of hyperspectral image acquired from UAV platform for soil organic matter (SOM) and soil total nitrogen (STN) content estimation in farmland. The results showed that: 1) Multiple Scattering Correction method performed better in reducing image scattering noise rather than Standard Normal Variate transformation or spectral derivatives with higher correlation and lower signal-to-noise ratio; 2) The proposed feature selection method, which was combined with Competitive Adaptive Reweighted Sampling algorithm (CARS) and Successive Projections Algorithm (SPA), could provide selective preference for hyperspectral bands with final 24 feature bands for SOM estimation and 22 feature bands for STN estimation; 3) The particle swarm optimization (PSO) algorithm was selected to optimize input weights and hidden biases of extreme learning machine (ELM)  model for SOM and STN prediction. The PSO-ELM model with input selective preference bands produced higher prediction accuracy with the R<sup>2</sup> of 0.73, RPD of 1.91 for SOM and R<sup>2</sup> of 0.63, RPD of 1.53 for STN respectively rather than ELM model. These outcomes provided a technical support for wider application of soil properties estimation using imaging spectrometry in agriculture precision monitoring and mapping.</p>

scholarly journals Analysis of Methanol Gasoline by ATR-FT-IR Spectroscopy

2019 ◽  
Vol 9 (24) ◽  
pp. 5336 ◽  
Author(s):  
Qi XIA ◽  
Lei-ming YUAN ◽  
Xiaojing CHEN ◽  
Liuwei MENG ◽  
Guangzao HUANG
Keyword(s):  
Quantitative Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Internal Combustion Engines ◽  
Quantitative Model ◽  
Least Square ◽  
Support Vector ◽  
Least Squares Regression ◽  
Transportation Industry ◽  
Successive Projections Algorithm

Methanol gasoline blends are a more economical, and environmentally friendly fuels than gasoline alone, and are widely used in the transportation industry. The content of methanol in methanol gasoline plays an important role in ensuring the quality of gasoline. In some solutions, due to the shortage of energy and illegal profits, the problem of gasoline adulteration and its fineness, has received more and more attention, which would seriously affect the operating condition and service life of internal combustion engines. Therefore, it is very important to identify the correct level of gasoline. However, the traditional detection method is complex and time-consuming. To this end, the feasibility of using attenuated total reflectance Fourier transform infrared (ATR-FTIR) methods coupled with chemometrics methods were investigated to quantitatively and qualitatively analyze methanol gasoline. The qualitative analysis result of partial least squares discriminant analysis (PLS-DA) obtained 100% and 98.66% accuracy in the calibration set and the prediction set, respectively. As for quantitative analysis; two regression algorithms of partial least squares regression (PLSR) and the least square support vector machine (LS-SVM), as well as two variables selection methods of the successive projections algorithm (UVE) competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE) were combined to establish the quantitative model. By comparing the performance of the optimal models; the UVE-PLSR model performed best with a residual predictive deviation (RPD) value of 6.420. The qualitative and quantitative analysis results demonstrate the feasibility of using ATR-FTIR spectra to detect the methanol in methanol gasoline. It is believed that the promising IR spectra will be widely used in gasoline energy quality control in the further.

scholarly journals The Soil Nutrient Digital Mapping for Precision Agriculture Cases in the Trans-Ural Steppe Zone of Russia Using Topographic Attributes

2021 ◽  
Vol 10 (4) ◽  
pp. 243
Author(s):  
Azamat Suleymanov ◽  
Evgeny Abakumov ◽  
Ruslan Suleymanov ◽  
Ilyusya Gabbasova ◽  
Mikhail Komissarov
Keyword(s):  
Spatial Distribution ◽  
Soil Properties ◽  
Soil Nutrients ◽  
Precision Agriculture ◽  
Large Scale ◽  
Soil Nutrient ◽  
Steppe Zone ◽  
Shuttle Radar Topography Mission ◽  
Coefficient Of Determination ◽  
Support Vector

Topographic features of territory have a significant impact on the spatial distribution of soil properties. This research is focused on digital soil mapping (DSM) of main agrochemical soil properties—values of soil organic carbon (SOC), nitrogen, potassium, calcium, magnesium, sodium, phosphorus, pH, and thickness of the humus-accumulative (AB) horizon of arable lands in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). The methods of multiple linear regression (MLR) and support vector machine (SVM) were used for the prediction of soil nutrients spatial distribution and variation. We used 17 topographic indices calculated using the SRTM (Shuttle Radar Topography Mission) digital elevation model. Results showed that SVM is the best method in predicting the spatial variation of all soil agrochemical properties with comparison to MLR. According to the coefficient of determination R2, the best predictive models were obtained for content of nitrogen (R2 = 0.74), SOC (R2 = 0.66), and potassium (R2 = 0.62). In our study, elevation, slope, and MMRTF (multiresolution ridge top flatness) index are the most important variables. The developed methodology can be used to study the spatial distribution of soil nutrients and large-scale mapping in similar landscapes.

scholarly journals Comparing the Potential of Near- and Mid-Infrared Spectroscopy in Determining the Freshness of Strawberry Powder from Freshly Available and Stored Strawberry

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Da Wang ◽  
Wenwen Wei ◽  
Yanhua Lai ◽  
Xiangzheng Yang ◽  
Shaojia Li ◽  
...  
Keyword(s):  
Least Squares ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Absolute Difference ◽  
Support Vector ◽  
Spectroscopic Techniques ◽  
Least Squares Regression ◽  
Successive Projections Algorithm ◽  
Mid Infrared ◽  
Mir Spectra

The quality of strawberry powder depends on the freshness of the fruit that produces the powder. Therefore, identifying whether the strawberry powder is made from freshly available, short-term stored, or long-term stored strawberries is important to provide consumers with quality-assured strawberry powder. Nevertheless, such identification is difficult by naked eyes, as the powder colours are very close. In this work, based on the measurement of near-infrared (NIR) spectroscopy and mid-infrared (MIR) spectra of strawberry powered, good classification results of 100.00% correct rates to distinguish whether the strawberry powder was made from freshly available or stored fruit was obtained. Furthermore, partial least squares regression and least squares support vector machines (LS-SVM) models were established based on NIR, MIR, and combination of NIR and MIR data with full variables or optimal variables of strawberry powder to predict the storage days of strawberries that produced the powder. Optimal variables were selected by successive projections algorithm (SPA), uninformation variable elimination, and competitive adaptive reweighted sampling, respectively. The best model was determined as the SPA-LS-SVM model based on MIR spectra, which had the residual prediction deviation (RPD) value of 11.198 and the absolute difference between root-mean-square error of calibration and prediction (AB_RMSE) value of 0.505. The results of this work confirmed the feasibility of using NIR and MIR spectroscopic techniques for rapid identification of strawberry powder made from freshly available and stored strawberry.

scholarly journals Can Trunk Acceleration Differentiate Stroke Patient Gait Patterns Using Time- and Frequency-Domain Features?

2021 ◽  
Vol 11 (4) ◽  
pp. 1541
Author(s):  
Wei-Chun Hsu ◽  
Tommy Sugiarto ◽  
Ying-Yi Liao ◽  
Yi-Jia Lin ◽  
Fu-Chi Yang ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Signal To Noise Ratio ◽  
Feature Selection Method ◽  
Measurement Unit ◽  
Support Vector ◽  
Gait Patterns ◽  
Lower Back ◽  
Trunk Acceleration ◽  
Stroke Group ◽  
Single Sensor

This study classified the gait patterns of normal and stroke participants by using time- and frequency-domain features obtained from data provided by an inertial measurement unit sensor placed on the subject’s lower back (L5). Twenty-three participants were included and divided into two groups: healthy group (young and older adults) and stroke group. Time- and frequency-domain features from an accelerometer were extracted, and a feature selection method comprising statistical analysis and signal-to-noise ratio (SNR) calculation was used to reduce the number of features. The features were then used to train four Support Vector Machine (SVM) kernels, and the results were subsequently compared. The quadratic SVM kernel had the highest accuracy (93.46%), as evaluated through cross-validation. Moreover, when different datasets were used on model testing, both the quadratic and cubic kernels showed the highest accuracy (96.55%). These results demonstrated the effectiveness of this study’s classification method in distinguishing between normal and stroke gait patterns, with only using a single sensor placed on the L5.

scholarly journals An Efficient Feature Selection Strategy Based on Multiple Support Vector Machine Technology with Gene Expression Data

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Ying Zhang ◽  
Qingchun Deng ◽  
Wenbin Liang ◽  
Xianchun Zou
Keyword(s):  
Gene Expression ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Feature Selection Method ◽  
Pso Algorithm ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Grid Search ◽  
Expression Data

The application of gene expression data to the diagnosis and classification of cancer has become a hot issue in the field of cancer classification. Gene expression data usually contains a large number of tumor-free data and has the characteristics of high dimensions. In order to select determinant genes related to breast cancer from the initial gene expression data, we propose a new feature selection method, namely, support vector machine based on recursive feature elimination and parameter optimization (SVM-RFE-PO). The grid search (GS) algorithm, the particle swarm optimization (PSO) algorithm, and the genetic algorithm (GA) are applied to search the optimal parameters in the feature selection process. Herein, the new feature selection method contains three kinds of algorithms: support vector machine based on recursive feature elimination and grid search (SVM-RFE-GS), support vector machine based on recursive feature elimination and particle swarm optimization (SVM-RFE-PSO), and support vector machine based on recursive feature elimination and genetic algorithm (SVM-RFE-GA). Then the selected optimal feature subsets are used to train the SVM classifier for cancer classification. We also use random forest feature selection (RFFS), random forest feature selection and grid search (RFFS-GS), and minimal redundancy maximal relevance (mRMR) algorithm as feature selection methods to compare the effects of the SVM-RFE-PO algorithm. The results showed that the feature subset obtained by feature selection using SVM-RFE-PSO algorithm results has a better prediction performance of Area Under Curve (AUC) in the testing data set. This algorithm not only is time-saving, but also is capable of extracting more representative and useful genes.

A fuzzy gaussian rank aggregation ensemble feature selection method for microarray data

2021 ◽  
Vol 24 (4) ◽  
pp. 289-301
Author(s):  
B. Venkatesh ◽  
J. Anuradha
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Performance Metrics ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Selection Methods

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods.

scholarly journals Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 419 ◽  
Author(s):  
Dongdong Du ◽  
Jun Wang ◽  
Bo Wang ◽  
Luyi Zhu ◽  
Xuezhen Hong
Keyword(s):  
Prediction Models ◽  
Extraction Methods ◽  
Oxide Semiconductor ◽  
Soluble Solids ◽  
Support Vector ◽  
Least Squares Regression ◽  
Accuracy Rate ◽  
Linear Discriminant ◽  
The Difference ◽  
Ripe Stage

Postharvest kiwifruit continues to ripen for a period until it reaches the optimal “eating ripe” stage. Without damaging the fruit, it is very difficult to identify the ripeness of postharvest kiwifruit by conventional means. In this study, an electronic nose (E-nose) with 10 metal oxide semiconductor (MOS) gas sensors was used to predict the ripeness of postharvest kiwifruit. Three different feature extraction methods (the max/min values, the difference values and the 70th s values) were employed to discriminate kiwifruit at different ripening times by linear discriminant analysis (LDA), and results showed that the 70th s values method had the best performance in discriminating kiwifruit at different ripening stages, obtaining a 100% original accuracy rate and a 99.4% cross-validation accuracy rate. Partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) were employed to build prediction models for overall ripeness, soluble solids content (SSC) and firmness. The regression results showed that the RF algorithm had the best performance in predicting the ripeness indexes of postharvest kiwifruit compared with PLSR and SVM, which illustrated that the E-nose data had high correlations with overall ripeness (training: R2 = 0.9928; testing: R2 = 0.9928), SSC (training: R2 = 0.9749; testing: R2 = 0.9143) and firmness (training: R2 = 0.9814; testing: R2 = 0.9290). This study demonstrated that E-nose could be a comprehensive approach to predict the ripeness of postharvest kiwifruit through aroma volatiles.

scholarly journals Identification and authentication for wireless transmission security based on RF-DNA fingerprint

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xueli Wang ◽  
Yufeng Zhang ◽  
Hongxin Zhang ◽  
Xiaofeng Wei ◽  
Guangyuan Wang
Keyword(s):  
Radio Frequency ◽  
Classification Accuracy ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Wireless Transmission ◽  
Dna Fingerprint ◽  
Support Vector ◽  
Multiple Discriminant Analysis ◽  
Security Issue ◽  
Rf Devices

Abstract For wireless transmission, radio-frequency device anti-cloning has become a major security issue. Radio-frequency distinct native attribute (RF-DNA) fingerprint is a developing technology to find the difference among RF devices and identify them. Comparing with previous research, (1) this paper proposed that mean (μ) feature should be added into RF-DNA fingerprint. Thus, totally four statistics (mean, standard deviation, skewness, and kurtosis) were calculated on instantaneous amplitude, phase, and frequency generated by Hilbert transform. (2) We first proposed using the logistic regression (LR) and support vector machine (SVM) to recognize such extracted fingerprint at different signal-to-noise ratio (SNR) environment. We compared their performance with traditional multiple discriminant analysis (MDA). (3) In addition, this paper also proposed to extract three sub-features (amplitude, phase, and frequency) separately to recognize extracted fingerprint under MDA. In order to make our results more universal, additive white Gaussian noise was adopted to simulate the real environment. The results show that (1) mean feature conducts an improvement in the classification accuracy, especially in low SNR environment. (2) MDA and SVM could successfully identify these RF devices, and the classification accuracy could reach 94%. Although the classification accuracy of LR is 89.2%, it could get the probability of each class. After adding a different noise, the recognition accuracy is more than 80% when SNR≥5 dB using MDA or SVM. (3) Frequency feature has more discriminant information. Phase and amplitude play an auxiliary but also pivotal role in classification recognition.

Median Morphological Filter Design Based on the PSO Algorithm

2011 ◽  
Vol 128-129 ◽  
pp. 181-184
Author(s):  
You Lian Zhu ◽  
Cheng Huang
Keyword(s):  
Filter Design ◽  
Design Method ◽  
Signal To Noise Ratio ◽  
Pso Algorithm ◽  
Morphological Operations ◽  
Morphological Filter ◽  
Signal To Noise ◽  
Noise Ratio ◽  
The Cost ◽  
Filter Design Method

Design of morphological filter greatly depends on morphological operations and structuring elements selection. A filter design method used median closing morphological operation is proposed to enhance the image denoising ability and the PSO algorithm is introduced for structural elements selecting. The method takes the peak value signal-to-noise ratio (PSNR) as the cost function and may adaptively build unit structuring elements with zero square matrix. Experimental results show the proposed method can effectively remove impulse noise from a noisy image, especially from a low signal-to-noise ratio (SNR) image; the noise reduction performance has obvious advantages than the other.

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