scholarly journals Assessing Waterlogging Stress Level of Winter Wheat from Hyperspectral Imagery Based on Harmonic Analysis

2021 ◽  
Vol 14 (1) ◽  
pp. 122
Author(s):  
Feifei Yang ◽  
Shengping Liu ◽  
Qiyuan Wang ◽  
Tao Liu ◽  
Shijuan Li
Keyword(s):  
Winter Wheat ◽  
Harmonic Analysis ◽  
Stress Level ◽  
Hyperspectral Imagery ◽  
Wheat Variety ◽  
Freezing Stress ◽  
Support Vector ◽  
Maturation Stage ◽  
Waterlogging Stress ◽  
Stress Levels

Frequent waterlogging disasters can have serious effects on regional ecology, food safety, and socioeconomic sustainable development. Early monitoring of waterlogging stress levels is vital for accurate production input management and reduction of crop production-related risks. In this study, a pot experiment on winter wheat was designed using three varieties and seven gradients of waterlogging stress. Hyperspectral imagery of the winter wheat canopy in the jointing stage, heading stage, flowering stage, filling stage, and maturation stage were measured and then classified. Wavebands of imaging data were screened. Waterlogging stress level was assessed by a combined harmonic analysis method, and application of this method at field scale was discussed preliminarily. Results show that compared to the k-nearest neighbor and support vector machine algorithms, the random forest algorithm is the best batch classification method for hyperspectral imagery of potted winter wheat. It can recognize waterlogging stress well in the wavebands of red absorption valley (RW: 640–680 nm), red-edge (RE: 670–737 nm), and near-infrared (NIR: 700–900 nm). In the RW region, amplitudes of the first three harmonic sub-signals (c1, c2, and c3) can be used as indexes to recognize the waterlogging stress level that each winter wheat variety undertakes. The third harmonic sub-signal amplitude c3 of the RE region is also suitable for judging stress levels of JM31 (one of the three varieties which is highly sensitive to water content). This study has important theoretical significance and practical application values related to the accurate control of waterlogging stress, and functions as a new method to monitor other types of environmental stress levels such as drought stress, freezing stress, and high-temperature stress levels.

scholarly journals Using UAV-Based Hyperspectral Imagery to Detect Winter Wheat Fusarium Head Blight

2021 ◽  
Vol 13 (15) ◽  
pp. 3024
Author(s):  
Huiqin Ma ◽  
Wenjiang Huang ◽  
Yingying Dong ◽  
Linyi Liu ◽  
Anting Guo
Keyword(s):  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Hyperspectral Imagery ◽  
Spectral Feature ◽  
Support Vector ◽  
Detection Accuracy ◽  
Feature Combination ◽  
Field Scale ◽  
Head Blight ◽  
Detection Model

Fusarium head blight (FHB) is a major winter wheat disease in China. The accurate and timely detection of wheat FHB is vital to scientific field management. By combining three types of spectral features, namely, spectral bands (SBs), vegetation indices (VIs), and wavelet features (WFs), in this study, we explore the potential of using hyperspectral imagery obtained from an unmanned aerial vehicle (UAV), to detect wheat FHB. First, during the wheat filling period, two UAV-based hyperspectral images were acquired. SBs, VIs, and WFs that were sensitive to wheat FHB were extracted and optimized from the two images. Subsequently, a field-scale wheat FHB detection model was formulated, based on the optimal spectral feature combination of SBs, VIs, and WFs (SBs + VIs + WFs), using a support vector machine. Two commonly used data normalization algorithms were utilized before the construction of the model. The single WFs, and the spectral feature combination of optimal SBs and VIs (SBs + VIs), were respectively used to formulate models for comparison and testing. The results showed that the detection model based on the normalized SBs + VIs + WFs, using min–max normalization algorithm, achieved the highest R2 of 0.88 and the lowest RMSE of 2.68% among the three models. Our results suggest that UAV-based hyperspectral imaging technology is promising for the field-scale detection of wheat FHB. Combining traditional SBs and VIs with WFs can improve the detection accuracy of wheat FHB effectively.

scholarly journals UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

2021 ◽  
Vol 13 (24) ◽  
pp. 5166
Author(s):  
Jianjun Wang ◽  
Qi Zhou ◽  
Jiali Shang ◽  
Chang Liu ◽  
Tingxuan Zhuang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Variable Selection ◽  
Winter Wheat ◽  
Wheat Variety ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Monitoring Method ◽  
Chlorophyll Contents ◽  
Winter Wheat Variety

In recent years, the delay in sowing has become a major obstacle to high wheat yield in Jiangsu Province, one of the major wheat producing areas in China; hence, it is necessary to screen wheat varieties are resilient for late sowing. This study aimed to provide an effective, fast, and non-destructive monitoring method of soil plant analysis development (SPAD) values, which can represent leaf chlorophyll contents, for late-sown winter wheat variety screening. This study acquired multispectral images using an unmanned aerial vehicle (UAV) at the overwintering stage of winter wheat growth, and further processed these images to extract reflectance of five single spectral bands and calculated 26 spectral vegetation indices. Based on these 31 variables, this study combined three variable selection methods (i.e., recursive feature elimination (RFE), random forest (RF), and Pearson correlation coefficient (r)) with four machine learning algorithms (i.e., random forest regression (RFR), linear kernel-based support vector regression (SVR), radial basis function (RBF) kernel-based SVR, and sigmoid kernel-based SVR), resulted in seven SVR models (i.e., RFE-SVR_linear, RF-SVR_linear, RF-SVR_RBF, RF-SVR_sigmoid, r-SVR_linear, r-SVR_RBF, and r-SVR_sigmoid) and three RFR models (i.e., RFE-RFR, RF-RFR, and r-RFR). The performances of the 10 machine learning models were evaluated and compared with each other according to the achieved coefficient of determination (R2), residual prediction deviation (RPD), root mean square error (RMSE), and relative RMSE (RRMSE) in SPAD estimation. Of the 10 models, the best one was the RF-SVR_sigmoid model, which was the combination of the RF variable selection method and the sigmoid kernel-based SVR algorithm. It achieved high accuracy in estimating SPAD values of the wheat canopy (R2 = 0.754, RPD = 2.017, RMSE = 1.716 and RRMSE = 4.504%). The newly developed UAV- and machine learning-based model provided a promising and real time method to monitor chlorophyll contents at the overwintering stage, which can benefit late-sown winter wheat variety screening.

scholarly journals Stress Detection Methodology based on Social Media Network: A Proposed Design

2020 ◽  
Vol 9 (3) ◽  
pp. 3489-3492
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Mental Disorders ◽  
Stress Level ◽  
Machine Learning Algorithms ◽  
Working Environment ◽  
Support Vector ◽  
Stress Detection ◽  
Lower Accuracy ◽  
Stress Levels

Mental disorders can be recognized by how a person behaves, feels, perceives, or thinks over a period of a lifetime. Nowadays, a large number of people are feeling stressed with the rapid pace of life. Stress and depression may lead to mental disorders. Work pressure, working environment, people we interact, schedule of the day, food habits, etc. are some of the major reasons behind building stress among the people. Thus, stress can be detected through some conventional medical symptoms such as headache, rapid heartbeats, feeling low energy, chest pain, frequent colds, infections, etc. The stress also may reflect in normal behavior while carrying out day-to-day activities. Individuals may share their day-to-day activities and interact with friends on social media. Thus, it may be possible to detect stress through social network data. There are many ways to detect stress levels. Some of the instruments are used to detect stress while there is a medical test to know the stress level. Also, there are apps that analyze the behavior of the person to detect stress. Many researchers had tried to use machine learning techniques including the use of various algorithms such as Decision Tree, Naïve Bayes, Random Forest, etc. which gives a lower accuracy of 70% on average. In this paper, we are using a closeness of stress levels with social media data shared by many users. In our proposed system design, Facebook posts are being accessed using a token. Further, we recommend the use of machine learning algorithms such as Conventional Neural Network (CNN) to extract Facebook posts, Transductive Support Vector Machine (TSVM) to classify posts and K-Nearest Neighbors (KNN) to recommend nearby hospitals. With the help of these algorithms, we predict the stress level of the person as positive, negative. Thus, we are expecting more accuracy to detect the stress along with the preventive recommendation. We have proposed a methodology to detect stress because severe stress may lead to self-harming activities and also it may affect the lives of people around us. Thus, stress detection has become extremely important and we are expecting that our proposed model may detect it with more accuracy.

scholarly journals Multilevel Assessment of Mental Stress using SVM with ECOC: An EEG Approach

2019 ◽  
Author(s):  
Fares Al-Shargie
Keyword(s):  
Prefrontal Cortex ◽  
Stress Level ◽  
Mental Stress ◽  
Alpha Rhythm ◽  
Support Vector ◽  
P Values ◽  
Assessment Protocol ◽  
Comparison Results ◽  
Stress Levels ◽  
Self Reporting Questionnaire

Mental stress has been identified as one of the major contributing factors that leads to various diseases such as heart attack, depression and stroke. To avoid this, stress quantification is important for clinical intervention and disease prevention. This study aims to investigate the feasibility of exploiting Electroencephalography (EEG) signals to discriminate between different stress levels. We propose a new assessment protocol whereby the stress level is represented by the complexity of mental arithmetic (MA) task for example, at three levels of difficulty, and the stressors are time pressure and negative feedback. Using 18-male subjects, the experimental results showed that there were significant differences in EEG response between the control and stress conditions at different levels of MA task with p-values < 0.001. Furthermore, we found a significant reduction in alpha rhythm power from one stress level to another level, p-values <0.05. In comparison, results from self-reporting questionnaire NASA-TLX approach showed no significant differences between stress levels. In addition, we developed a discriminant analysis method based on multiclass support vector machine (SVM) with error-correcting output code (ECOC). Different stress levels were detected with an average classification accuracy of 94.79%. The Lateral Index (LI) results further showed dominant right prefrontal cortex (PFC) to mental stress (reduced alpha rhythm). The study demonstrated the feasibility of using EEG in classifying multilevel mental stress, and reported alpha rhythm power at right prefrontal cortex as a suitable index.

Winter Wheat Variety Test

2007 ◽  
Author(s):  
Ronald Skrdla ◽  
Jean-Luc Jannink
Keyword(s):  
Winter Wheat ◽  
Wheat Variety ◽  
Winter Wheat Variety

Conjectures, Hypotheses, and Theories of Drumlin Formation (In memory of Stanislaw Baranowski)

1981 ◽  
Vol 27 (97) ◽  
pp. 503-505 ◽  
Author(s):  
Ian J. Smalley
Keyword(s):  
Shear Strength ◽  
Pore Water ◽  
Stress Level ◽  
Critical Stress ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Glacial Till ◽  
Forming Process ◽  
Stress Levels

AbstractRecent investigations have shown that various factors may affect the shear strength of glacial till and that these factors may be involved in the drumlin-forming process. The presence of frozen till in the deforming zone, variation in pore-water pressure in the till, and the occurrence of random patches of dense stony-till texture have been considered. The occurrence of dense stony till may relate to the dilatancy hypothesis and can be considered a likely drumlin-forming factor within the region of critical stress levels. The up-glacier stress level now appears to be the more important, and to provide a sharper division between drumlin-forming and non-drumlin-forming conditions.

Stress evaluation in seven-wire strands based on singular value feature of ultrasonic guided waves

2021 ◽  
pp. 147592172110053
Author(s):  
Qian Ji ◽  
Li Jian-Bin ◽  
Liu Fan-Rui ◽  
Zhou Jian-Ting ◽  
Wang Xu
Keyword(s):  
Dispersion Curve ◽  
Support Vector Regression ◽  
Stress Level ◽  
Guided Waves ◽  
Guided Wave ◽  
Singular Value ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Various Boundary Conditions ◽  
Wave Methods

The seven-wire strands are the crucial components of prestressed structures, though their performance inevitably degrades with the passage of time. The ultrasonic guided wave methods have been intensely studied, owing to its tremendous potential for full-scale applications, among the existing nondestructive testing methods, for evaluating the stress status of strands. We have employed the theoretical and finite element methods to solve the dispersion curve of single wire and steel strands under various boundary conditions. Thereafter, the singular value decomposition was adopted to work with the simulated and experimental signals for extracting a feature vector that carries valuable stress status information. The effectiveness of the vector was verified by analyzing the relationship between the vector and the stress level. The vector was also used as an input to establish a support vector regression model. The accuracy of the model has been discussed for different sample sizes. The results show that the fundamental mode dispersion curve offset on the high-frequency part and cut-off frequency increases as the boundary constraints enhance. Simulated and experimental results have demonstrated the effectiveness and potential of the proposed support vector regression method for evaluating the stress level in the strands. This method performs well even at low stress levels and the reliability can be enhanced by adding more samples.

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
The Difference ◽  
Complex Deposition

Abstract Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

scholarly journals Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis

2021 ◽  
Vol 13 (4) ◽  
pp. 581 ◽  
Author(s):  
Yuanyuan Fu ◽  
Guijun Yang ◽  
Xiaoyu Song ◽  
Zhenhong Li ◽  
Xingang Xu ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Least Squares ◽  
Aboveground Biomass ◽  
Regression Models ◽  
Digital Images ◽  
Predictive Performance ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Biomass Estimation ◽  
High Definition

Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

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