Competitive Adaptive Reweighted Sampling Algorithm Research Articles

Hyperspectal imaging technology for phenotyping iron and boron deficiency in Brassica napus under greenhouse conditions

IntroductionThe micronutrient deficiency of iron and boron is a common issue affecting the growth of rapeseed (Brassica napus). In this study, a non-destructive diagnosis method for iron and boron deficiency in Brassica napus (genotype: Zhongshuang 11) using hyperspectral imaging technology was established.MethodsThe recognition accuracy was compared using the Fisher Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) recognition models. Recognition results showed that Multiple Scattering Correction (MSC) could be applied for the full band hyperspectral data processing, while the LDA models presented better performance on establishing the leaf iron and boron deficiency symptom recognition than the SVM models.ResultsThe recognition accuracy of the training set reached 96.67%, and the recognition rate of the prediction set could be 91.67%. To improve the model accuracy, the Competitive Adaptive Reweighted Sampling algorithm (CARS) was added to construct the MSC-CARS-LDA model. 33 featured wavelengths were selected via CARS. The recognition accuracy of the MSC-CARS-LDA training set was 100%, while the recognition accuracy of the MSC-CARS-LDA prediction set was 95.00%.DiscussionThis study indicates that, it is capable to identify the iron and boron deficiency in rapeseed using hyperspectral imaging technology.

Predictive modeling of milk adulteration with urea content using the gray wolf optimization algorithm and long and short-term memory network model

Traditional methods for the determining of urea contaminants in milk, including chromatography, spectrophotometry, and electrochemical processes, have drawbacks such as long testing times and sample destruction. However, hyperspectral imaging technology provides a fast, easy-to-operate, and real-time alternative. In this study, five preprocessing methods are employed, including standard scalar, standard normal variational, first derivative, multivariate scattering correction algorithm, and Savitzky-Golay smoothing. To further enhance the accuracy of predicting urea content in milk, a prediction optimization method based on the gray wolf optimization algorithm for the long short-term memory network was proposed, using a competitive adaptive reweighted sampling algorithm feature wavelength selection algorithm. In the optimized model in the test set experiments, the decision coefficient was improved by 0.56% to 0.9906, and root mean square error was reduced by 7.69% to 0.4996 compared to the long short-term memory network model. This study not only provides a theoretical basis but also presents a fast and nondestructive detection method for accurately predicting urea content in milk.

Application of terahertz Time-Domain spectroscopy and chemometrics-based whale optimization algorithm in PDE5 inhibitor detection

Combating the illicit use of PDE5 inhibitor drugs is a focal point in forensic science research. In order to achieve rapid identification of such drugs, this study applies terahertz time-domain spectroscopy combined with chemometrics to establish a fast and accurate detection method for PDE5 inhibitors. The optimal detection method is determined by comparing the spectral performance of three optical parameters, namely absorption coefficient, refractive index, and dielectric constant. Linear discriminant models based on different spectral parameters, whale optimization algorithm optimized extreme learning machine models, and whale optimization algorithm optimized random forest models are established. The effectiveness and performance of principal component analysis and competitive adaptive reweighted sampling algorithm for spectral feature data selection are also investigated. The PDE5 inhibitor identification model based on the competitive adaptive reweighted sampling - whale optimization algorithm - random forest (CARS-WOA-RF) model achieves an accuracy of 98.61%, and the identification model for two concentrations of Sildenafil achieves 100% accuracy. The results demonstrate that terahertz time-domain spectroscopy combined with chemometrics can effectively detect various common types of PDE5 inhibitor drugs and different concentrations.

Study on Dissipation Law of Pesticides in Cauliflower Based on Hyperspectral Image Technique

In order to rapidly and non-destructively detect the residual rate of emamectin benzoate+indoxacarb pesticides on cauliflower, a study was conducted using hyperspectral technology to investigate the dissipation law of this pesticide over time. Hyperspectral imaging was employed to capture spectral data from cauliflower samples with and without the pesticide, focusing on the region of interest. The spectral data, consisting of 216 bands (ranging from 950 nm to 1666 nm), were preprocessed using techniques such as Savitzky–Golay convolution smoothing (S-G), multivariate scattering correction (MSC), and standard normal variate (SNV). Next, characteristic spectra for each pesticide were extracted using the competitive adaptive reweighted sampling algorithm (CARS). This study utilized the partial least squares (PLS) algorithm to construct a discriminative model aimed at identifying pesticide residues on cauliflower. The accuracy of the hyperspectral imaging technique was validated by comparing the results with those obtained through chromatography. The PLS model, optimized using the SNV method, exhibited the highest discriminant accuracy, achieving a recognition rate of 100%. The residual rate of indoxacarb detected through hyperspectral technology closely corresponded to the results obtained through chromatography. It was found that the discrepancy in the half-life of pesticides as detected by hyperspectral and chromatographic methods is a mere 0.14 days. These findings highlight the potential of hyperspectral imaging technology for studying pesticide dissipation on cauliflower and detecting pesticide residues.

A quality detection method of corn based on spectral technology and deep learning model

Corn is an important food crop in the world. With economic development and population growth, the nutritional quality of corn is of great significance to high-quality breeding, scientific cultivation and fine management. Aiming at the problems of cumbersome steps, time-consuming and laborious, and low accuracy in the current research on corn quality detection. This paper proposes to combine near-infrared (NIR) spectroscopy technology with deep learning technology to build a corn quality detection model based on convolutional neural network (LeNet-5). The original spectral data were preprocessed by wavelet transform (WT) and multivariate scattering correction (MSC) to remove noise interference and spectral scattering information. The Competitive Adaptive Reweighted Sampling Algorithm (CARS) was applied to optimize the characteristic wavenumber and reduce redundant data. According to the optimized characteristic wave number, it was input into the constructed corn quality detection model for simulation test, and the average detection accuracy rate of the test set was 96.46%, the average precision rate was 95.42%, the average recall rate was 97.92%, the average F1score was 96.64%, and the average recognition time was 51.95 s. Compared with traditional machine learning models such as BP neural network, K Nearest Neighbor (KNN), Support Vector Machine (SVM), Generalized Linear Model (GLM), Linear Discriminant Analysis (LDA), and Naive Bayesian (NB), the deep learning LeNet-5 network model constructed in this paper has an average accuracy increase of 39.32%, and has a higher detection accuracy.

Nondestructive detection of nutritional parameters of pork based on NIR hyperspectral imaging technique

Nondestructive detection of the nutritional parameters of pork is of great importance. This study aimed to investigate the feasibility of applying hyperspectral image technology to detect the nutrient content and distribution of pork nondestructively. Hyperspectral cubes of 100 pork samples were collected using a line-scan hyperspectral system, the effects of different preprocessing methods on the modeling effects were compared and analyzed, the feature wavelengths of fat and protein were extracted, and the full-wavelength model was optimized using the regressor chains (RC) algorithm. Finally, pork's fat, protein, and energy value distributions were visualized using the best prediction model. The results showed that standard normal variate was more effective than other preprocessing methods, the feature wavelengths extracted by the competitive adaptive reweighted sampling algorithm had better prediction performance, and the protein model prediction performance was optimized after using the RC algorithm. The best prediction models were developed, with the correlation coefficient of prediction (RP) = 0.929, the root mean square error in prediction (RMSEP) = 0.699% and residual prediction deviation (RPD) = 2.669 for fat, and RP = 0.934, RMSEP = 0.603% and RPD = 2.586 for protein. The pseudo-color maps were helpful for the analysis of nutrient distribution in pork. Hyperspectral image technology can be a fast, nondestructive, and accurate tool for quantifying the composition and assessing the distribution of nutrients in pork.

Application of portable Vis-NIR spectroscopy for rapid detection of myoglobin in frozen pork

Myoglobin content is considered as a crucial index to evaluate the quality of frozen pork. In this study, a portable visible and near-infrared (Vis-NIR) spectrometer combined with chemometrics was used to detect myoglobin content in frozen pork. Metmyoglobin, deoxymyoglobin, oxymyoglobin, and total myoglobin were assessed spectrophotometrically. The raw Vis-NIR spectra of frozen pork samples were pre-processed using 1st derivatives (FD). Afterward, Synergy Interval Partial Least Square (Si-PLS) coupled Competitive Adaptive Reweighted Sampling algorithm (Si-CARS-PLS) was applied to select characteristic variables. The Si-CARS-PLS models revealed the probability of estimating myoglobin content in frozen pork, with predictive correlation coefficients (Rp) for metmyoglobin, deoxymyoglobin, oxymyoglobin, and total myoglobin as 0.9095, 0.9004, 0.8578, and 0.9133, respectively. The findings of this study showed that Vis-NIR spectroscopy coupled with Si-CARS-PLS is a promising method and offered a way forward for determining the myoglobin content in frozen pork.

A non-destructive determination of protein content in potato flour noodles using near-infrared hyperspectral imaging technology

Hyperspectral imaging (HSI) with the near-infrared (NIR) bands (900–2250 nm) was employed to investigate the non-destructive prediction of protein content in potato flour noodles. The protein content (8.906–10.515 %) of 120 potato flour noodles was studied to establish the prediction model. Partial least squares regression (PLSR) model was established based on the spectra of potato flour noodles to predict the protein content showing high performance. After optimization, orthogonal signal correction (OSC) was used to preprocess the original spectra, and the competitive adaptive reweighted sampling algorithm (CARS) was chosen to select characteristic wavelengths, therefore, OSC-CARS-PLSR was established. Next, 77 samples were selected as the calibration set, and the remaining 38 samples were used as the prediction set. The coefficient of determination (R2) and the root mean square error (RMSE) were used to evaluate the performance of the model. OSC-CARS-PLSR showed a high performance with R2 values of 0.9606 and 0.8925 and RMSE values of 0.070 % and 0.1385 % in the calibration set and prediction set, respectively. The visualization image was used to identify protein distribution in potato flour noodles. Overall, the results indicate that HSI technology could accurately predict the protein content in potato flour noodles providing a rapid and non-destructive method to detect protein and other compositions in grains and foods.

Detection of tomato water stress based on terahertz spectroscopy.

China's tomato cultivation area is nearly 15 thousand km2, and its annual tomato output is about 55 million tons, accounting for 7% of its total vegetable production. Because of the high drought sensitivity of tomatoes, water stress inhibits their nutrient uptake, leading to a decrease in tomato quality and yield. Therefore, the rapid, accurate and non-destructive detection of water status is important for scientifically and effectively managing tomato water and fertilizer, improving the efficiency of water resource utilization, and safeguarding tomato yield and quality. Because of the extreme sensitivity of terahertz spectroscopy to water, we proposed a tomato leaf moisture detection method based on terahertz spectroscopy and made a preliminary exploration of the relationship between tomato water stress and terahertz spectral data. Tomato plants were grown at four levels of water stress. Fresh tomato leaves were sampled at fruit set, moisture content was calculated, and spectral data were collected through a terahertz time-domain spectroscope. The raw spectral data were smoothed using the Savitzky-Golay algorithm to reduce interference and noise. Then the data were divided by the Kennard-Stone algorithm and the sample set was partitioned based on the joint X-Y distance (SPXY) algorithm into a calibration set and a prediction set at a ratio of 3:1. SPXY was found to be the better approach for sample division. On this basis, the stability competitive adaptive re-weighted sampling algorithm was used to extract the feature frequency bands of moisture content, and a multiple linear regression model of leaf moisture content was established under the single dimensions of power, absorbance and transmittance. The absorbance model was the best, with a prediction set correlation coefficient of 0.9145 and a root mean square error of 0.1199. To further improve the modeling accuracy, we used a support vector machine (SVM) to establish a tomato moisture fusion prediction model based on the fusion of three-dimensional terahertz feature frequency bands. As water stress intensified, the power and absorbance spectral values both declined, and both were significantly and negatively correlated with leaf moisture content. The transmittance spectral value increased gradually with the intensification of water stress, showing a significant positive correlation. The SVM-based three-dimensional fusion prediction model showed a prediction set correlation coefficient of 0.9792 and a root mean square error of 0.0531, indicating that it outperformed the three single-dimensional models. Hence, terahertz spectroscopy can be applied to the detection of tomato leaf moisture content and provides a reference for tomato moisture detection.

Coal seam in-situ inorganic analysis based on least angle regression and competitive adaptive reweighted sampling algorithm by XRF–visNIR fusion

The fusion of X-ray fluorescence spectroscopy (XRF) and visible near infrared spectroscopy (visNIR) has been widely used in geological exploration. The outer product analysis (OPA) has a good effect in the fusion. The dimension of the spectral matrix obtained by OPA is large, and the Competitive Adaptive Reweighted Sampling (CARS) cannot cover the whole spectrum. As a result, the selected variables by the method are inconsistent each time. In this paper, a new feature variable screening method is proposed, which uses the Least Angle Regression (LAR) to select the high dimensional spectral matrix first, and then uses CARS to complete the secondary selection of the spectral matrix, forming the LAR-CARS algorithm. The purpose is to make the sampling method cover all the spectral data. XRF and visNIR tests were carried out on three cores in two boreholes, and a cross-validation set, validation set and a test set were established by combining the results of wavelength dispersion X-ray fluorescence spectrometer and ITRAX Core scanner in the laboratory. The quantitative model was established with the Extreme Gradient Boosting (XGBoost) and LAR-CARS was compared to these other algorithms (LAR, Successive Projections Algorithm, Monte Carlo uninformative variables elimination and CARS). The results showed that the RMSEP values of the models established by the LAR-CARS for six rock-forming elements (Si, Al, K, Ca, Fe, Ti) were relatively small, and the RPD ranges from 1.424 to 2.514. All these results show that the high-dimensional matrix formed by XRF and visNIR integration combined with LAR-CARS can be used for quantitative analysis of rock forming elements in in-situ coal seam cores, and the analysis results can be used as the basis for judging lithology. The research will provide necessary technical support for digital mine construction.

Nondestructive testing of the quality of different grades of creamy strawberries based on Laida algorithm

Rapid nondestructive testing of fruit quality is one of the hotspots in food industry research. This paper studied the quality detection of different grades of cream strawberries. Preprocessing methods such as detrending, moving average smoothing (MA), and standard normal variables (SNV) were used to eliminate spectral data errors. The competitive adaptive reweighted sampling algorithm (CARS), successive projection algorithm (SPA), the combination of the above two algorithms (CARS+SPA), and the self-programming algorithm based on the Laida criterion (collectively referred to as the Laida algorithm) were used to reduce the dimension of data, and a partial least squares regression prediction model was established. The results show that when the Laida algorithm predicted the soluble solid content (SSC), total acid (TA), and vitamin C (VC) content of strawberries, the prediction set correlation coefficients were 0.919, 0.931, and 0.907, respectively, the values of RPD were 3.15, 4.00, and 3.44, respectively, the relative errors of the predicted values and the measured values were 3.93%, 5.74%, and 3.69%, respectively. Practical applications This shows that it is feasible to use the Laida algorithm to extract the characteristic wavelengths and establish a prediction model for the SSC, TA, and VC content in cream strawberries. This study can provide a theoretical basis for the development of a rapid detection instrument for the quality of creamy strawberries.

Rapid detection method of Pleurotus eryngii mycelium based on near infrared spectral characteristics

Edible fungus is a large fungus with edible and medicinal value. Rapid detection of mycelium phenotypic characteristics is of great significance for edible fungus breeding and intelligent cultivation. Traditional method based on experienced observation easily led to make mistakes on distinguishing the growth stages, which impacted on the yield and quality of edible fungus. Therefore, in view of the lack of accurate and efficient detection technology during the growth stages of Pleurotus eryngii mycelium, a rapid detection method of Pleurotus eryngii mycelium at different growth stages is proposed based on the characteristics of near-infrared spectroscopy. First, the spectral data of mycelium of Pleurotus eryngii at six different growth stages were scanned. Second, the multivariate scattering correction method (MSC) was used to pre-process the raw spectral data, and then the competitive adaptive reweighted sampling algorithm (CARS) was adopted to detect the characteristic wave number of the effective variables for Pleurotus eryngii mycelium. In addition, the mathematical model between the mycelium of Pleurotus eryngii and the characteristic wave number of near-infrared spectrum was established by using feed forward neural network (BP). Finally, and the coding vector output by the network was used to detect to the growth stages. The results showed that the BP neural network structure of MSC-CARS-BP detection model was 86–85-85–95-6, and the accuracy of identifying different growth stages of Pleurotus eryngii mycelium was 99.67%. The research results could provide a new idea and technical support for the rapid detection of Pleurotus eryngii mycelium at different growth stages.

Building kinetic models to determine moisture content in apples and predicting shelf life based on spectroscopy

AbstractMoisture content (MC) directly affects the storage of apples. This study explored the prediction of MC and the shelf life of apples stored at room temperature. Using wavelengths selected by the competitive adaptive reweighted sampling algorithm and chemical kinetic reactions, a quantitative prediction model based on optical fiber spectroscopy and a kinetic model were established to determine the MC of apples stored at room temperature. The shelf life of apples was predicted based on the developed quantitative prediction and kinetic models. The determination coefficients of the calibration and prediction of the quantitative prediction model were determined as .9810 and .9686, with root mean square errors of .0020 and .0022, respectively, and a residual predictive deviation of 6.55. In addition, the correlation coefficient of the kinetic model was derived as .9957, with a root mean square error of .0011. Results demonstrate that when the MC of the fresh apples exceeds 85% and the initial MC of apples stored at room temperature equals 87%, the freshness preservation time of the apples should not exceed 28 days. The proposed quantitative and kinetic models are able to rapidly and accurately monitor the MC of apples, and the real‐time prediction of apple shelf life can be realized according to the difference in the spectral information pre‐ and post‐storage at room temperature.Practical ApplicationThis study provides a method for the determination of moisture content (MC) in apples and the prediction of shelf life based on kinetic models. According to the proposed method, enterprises can not only quickly and accurately detect the MC of apples to determine whether they are fresh, but can also easily predict the shelf life of apples to determine whether they are safe.

A robot system for the autodetection and classification of apple internal quality attributes

Soluble solids content (SSC) is an important index of apple internal quality. To invent a more flexible and efficient method of apple internal quality detection and classification, a robot system for the autodetection and classification of apple internal quality attributes was developed. Visible and near infrared (Vis/NIR) spectroscopy is a promising technology for the nondestructive detection of the internal quality attributes of apples. The end effector of the robot system mainly carried the Vis/NIR spectra collection module and gripping mechanism. The Vis/NIR spectrum was collected when the end effector gripped the apple. Single shot multibox detector (SSD) target detection algorithm was applied to process the images and calculate the position of the apple, which greatly reduced the low accuracy of apple identification caused by light intensity and complex backgrounds, and the speed was approximately 0.055 s per frame. In comparing different modeling results, the normalized spectral ratio (NSR) pretreatment combined with the competitive adaptive reweighted sampling algorithm (CARS) obtained the best modeling result, with Rc and Rcv values of 0.979 and 0.969 and RMSEC and RMSECV values of 0.335 % and 0.385 %, respectively. The classification accuracy of independent validation was 90.0 % with Rp and RMSEP values of 0.952 and 0.393 %. The robot system required approximately 5.200 s to complete a classification for each sample. The results showed feasibility of the robot system to detect the internal quality attributes of apples.

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

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.

Application of Visible/Near Infrared Spectrometers to Quickly Detect the Nitrogen, Phosphorus, and Potassium Content of Chemical Fertilizers

The rapid determination of nitrogen, phosphorus, potassium and other major nutrient elements is an important technical guarantee in the quality control of chemical fertilizers. In this study, a small visible spectrometer and a small near-infrared spectrometer were used to collect spectrum information of 33 different common chemical fertilizers including compound fertilizers, blended fertilizers and controlled-release fertilizers. The 550~950 nm and 1050~1640 nm spectra with stable signals were intercepted as the analysis spectrum, and the competitive adaptive reweighted sampling algorithm (CARS) was used to select 161, 229, and 161 spectral characteristic wavelengths for the three nutrient contents of N, P2O5, and K2O respectively. The partial least squares (PLS) and extreme learning machine (ELM) models of N, P2O5, and K2O were established based on the 550~950 nm waveband, 1050~1640 nm waveband, full spectrum, and characteristic wavelength, respectively. The coefficient of determination (R2), root mean square error (RMSE), and residual predictive deviation (RPD) were used to evaluate the effect of the model. With the optimal prediction models, the values of Rp2 for N, P2O5, and K2O were 0.989, 0.963, 0.981, and for RPD were 9.71, 5.09, 7.29, respectively. The research results show that Vis/NIR spectroscopy can predict the content of nitrogen, phosphorus, and potassium nutrients in fertilizers, and the near-infrared band from 1050 nm to 1640 nm has a better prediction effect. The characteristic wavelength selection reduces the spectral variables by 9/10, and the performance of the model based on the characteristic wavelength is close to that of the full-spectrum model.

Research on Inversion Model of Cultivated Soil Moisture Content Based on Hyperspectral Imaging Analysis

Based on hyperspectral imaging technology, rapid and efficient prediction of soil moisture content (SMC) can provide an essential basis for the formulation of precise agricultural programs (e.g., forestry irrigation and environmental management). To build an efficient inversion model of SMC, this paper collected 117 cultivated soil samples from the Chair Hill area and tested them using the GaiaSorter hyperspectral sorter. The collected soil reflectance dataset was preprocessed by wavelet transform, before the combination of competitive adaptive reweighted sampling algorithm and successive projections algorithm (CARS-SPA) was used to select the bands optimally. Seven wavelengths of 695, 711, 736, 747, 767, 778, and 796 nm were selected and used as the factors of the SMC inversion model. The popular linear regression algorithm was employed to construct this model. The result indicated that the inversion model established by the multiple linear regression algorithm (the predicted R2 was 0.83 and the RMSE was 0.0078) was feasible and highly accurate, indicating it could play an important role in predicting SMC of cultivated soils over a large area for agricultural irrigation and remote monitoring of crop yields.

Real-Time and Online Inspection of Multiple Pork Quality Parameters Using Dual-Band Visible/Near-Infrared Spectroscopy

The real-time and online inspection of pork quality is urgently necessary in the meat industry. In the present work, an online inspection system that can detect multiple quality parameters of pork simultaneously based on dual-band visible/near-infrared spectroscopy was developed. Specifically, a tungsten halogen lamp and ring light guide were used for illumination and a laser sensing unit was integrated with height adjustment and in-position recognition units, whereby stable dual-band spectral information was obtained from pork samples, resolving the inspection inaccuracy problem induced by unsuitable light sources and nonuniform sample thicknesses. Then, partial least squares regression models for color (L*, a*, and b*), pH, total volatile basic nitrogen content, fat, protein, cooking loss, tenderness, and moisture content were established based on spectra after different pretreatments. To further improve the prediction accuracy and stability, an improved competitive adaptive reweighted sampling algorithm was used to identify the optimum characteristic variables of each parameter, and simplified prediction models were established with the correlation coefficients Rp greater than 0.9 for all the aforementioned attributes except for moisture (Rp = 0.881). The results show that the inspection system combined with the spectral processing algorithm can realize rapid, nondestructive, and simultaneous detection of multiple quality parameters and can be readily applied for practical and industrial real-time, online inspection and grading of pork quality.

Remote sensing of regional-scale maize lodging using multitemporal GF-1 images

Lodging stress affects the yield, quality, and mechanical harvesting capacity of maize, thereby resulting in a reduction in maize production. The timely monitoring of maize lodging assists in determining the extent and degree of the effects. This study proposed a method for this purpose using Gaofen-1 (GF-1) optical satellite images, which can be significant for lodging field management, production assessments, and implementation of the related remedial measures. The evaluation index of the lodging grade was constructed by measuring the proportion and angle of maize lodging after the lodging incident. Variations in spectral reflectance and vegetation index were calculated before and after lodging. The competitive adaptive reweighted sampling algorithm was used to screen the optimal combination of variables sensitive to the evaluation index of the maize lodging grade. A remote sensing monitoring model was established by using random forest (RF) and partial least squares (PLS). Results show that the maize lodging monitoring model established by RF is better than that of PLS. The accuracy of monitoring the lodging damage using the GF-1 images reaches 79%. Meanwhile, the classification map of the lodging grade we constructed using an RF model is consistent with the actual lodging area. Hence, this method can effectively characterize the intensity of lodging stress and can also be used to assess the range of damages caused by regional-scale lodging.

Near-infrared-based Identification of Walnut Oil Authenticity

Near-infrared spectroscopy (NIR) was employed to determine the quality of walnut oil. Multivariate scattering correction (MSC) and combination of standard normal variate and de-trend (SNV-DT) were separately used for preprocessing. Successive projection algorithm (SPA) and combination of competitive adaptive reweighted sampling algorithm and partial least squares method (CARS-PLS) were separately used for extraction of characteristic wavelengths. Particle swarm optimization (PSO) and genetic algorithm (GA) methods were separately used for parameter optimization. Eventually, quantitative prediction models for impurities contents in walnut oil were established by support vector machine (SVM). After analysis of the prediction results derived from these models, an optimal candidate was obtained.