scholarly journals Detection of Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in Black Oat Seeds (Avena strigosa Schreb) Using Multispectral Imaging

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3343 ◽  
Author(s):  
Fabiano França-Silva ◽  
Carlos Henrique Queiroz Rego ◽  
Francisco Guilhien Gomes-Junior ◽  
Maria Heloisa Duarte de Moraes ◽  
André Dantas de Medeiros ◽  
...  
Keyword(s):  
High Performance ◽  
Multispectral Imaging ◽  
Texture Features ◽  
Pathogenic Fungi ◽  
Classification Model ◽  
Linear Discriminant ◽  
Avena Strigosa ◽  
Oat Seeds ◽  
Conventional Methods ◽  
Color And Texture Features

Conventional methods for detecting seed-borne fungi are laborious and time-consuming, requiring specialized analysts for characterization of pathogenic fungi on seed. Multispectral imaging (MSI) combined with machine vision was used as an alternative method to detect Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in black oat seeds (Avena strigosa Schreb). The seeds were inoculated with Drechslera avenae (D. avenae) and then incubated for 24, 72 and 120 h. Multispectral images of non-infested and infested seeds were acquired at 19 wavelengths within the spectral range of 365 to 970 nm. A classification model based on linear discriminant analysis (LDA) was created using reflectance, color, and texture features of the seed images. The model developed showed high performance of MSI in detecting D. avenae in black oat seeds, particularly using color and texture features from seeds incubated for 120 h, with an accuracy of 0.86 in independent validation. The high precision of the classifier showed that the method using images captured in the Ultraviolet A region (365 nm) could be easily used to classify black oat seeds according to their health status, and results can be achieved more rapidly and effectively compared to conventional methods.

scholarly journals Using Multispectral Imaging for Detecting Seed-Borne Fungi in Cowpea

Agriculture ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 361
Author(s):  
Carlos Henrique Queiroz Rego ◽  
Fabiano França-Silva ◽  
Francisco Guilhien Gomes-Junior ◽  
Maria Heloisa Duarte de Moraes ◽  
André Dantas de Medeiros ◽  
...  
Keyword(s):  
Statistical Models ◽  
Multispectral Imaging ◽  
Control Process ◽  
Texture Features ◽  
Fungal Species ◽  
Linear Discriminant ◽  
Cowpea Seeds ◽  
Seed Health ◽  
Quality Control Process ◽  
Aspergillus Sp

Recent advances in multispectral imaging-based technology have provided useful information on seed health in order to optimize the quality control process. In this study, we verified the efficiency of multispectral imaging (MSI) combined with statistical models to assess the cowpea seed health and differentiate seeds carrying different fungal species. Seeds were artificially inoculated with Fusarium pallidoroseum, Rhizoctonia solani and Aspergillus sp. Multispectral images were acquired at 19 wavelengths (365 to 970 nm) from inoculated seeds and freeze-killed ‘incubated’ seeds. Statistical models based on linear discriminant analysis (LDA) were developed using reflectance, color and texture features of the seed images. Results demonstrated that the LDA-based models were efficient in detecting and identifying different species of fungi in cowpea seeds. The model showed above 92% accuracy before incubation and 99% after incubation, indicating that the MSI technique in combination with statistical models can be a useful tool for evaluating the health status of cowpea seeds. Our findings can be a guide for the development of in-depth studies with more cultivars and fungal species, isolated and in association, for the successful application of MSI in the routine health inspection of cowpea seeds and other important legumes.

scholarly journals Correlation between Kidney Function and Sonographic Texture Features after Allograft Transplantation with Corresponding to Serum Creatinine: A Long Term Follow-Up Study

2019 ◽  
Author(s):  
A Abbasian Ardakani ◽  
A Sattar ◽  
J Abolghasemi ◽  
A Mohammadi
Keyword(s):  
Serum Creatinine ◽  
Texture Analysis ◽  
Kidney Function ◽  
High Performance ◽  
Characteristic Curve ◽  
Kidney Tissue ◽  
Texture Features ◽  
Linear Discriminant ◽  
Run Length

Background: The ability to monitor kidney function after transplantation is one of the major factors to improve care of patients.Objective: Authors recommend a computerized texture analysis using run-length matrix features for detection of changes in kidney tissue after allograft in ultrasound imaging.Material and Methods: A total of 40 kidney allograft recipients (28 male, 12 female) were used in the proposed computer-aided diagnosis system. Of the 40 patients, 23 and 17 patients showed increased serum creatinine (sCr) (increased group) and decreased sCr (decreased group), respectively. Twenty run-length matrix features were used for texture analysis in three normalizations. Correlations of texture features with serum creatinine (sCr) level and differences between before and after follow-up for each group were analyzed. An area under the receiver operating characteristic curve (Az) was measured to evaluate potential of proposed method.Results: The features under default and 3sigma normalization schemes via linear discriminant analysis (LDA) showed high performance in classifying decreased group with an Az of 1. In classification of the increased group, the best performance gains were determined in the 3sigma normalization schemes via LDA with an Az of 0.974 corresponding to 95.65% sensitivity, 91.30% specificity, 93.47% accuracy, 91.67% PPV, and 95.45% NPV.Conclusion: Run-length matrix features not only have high potential for characterization but also can help physicians to diagnose kidney failure after transplantation.

scholarly journals Investigating Lignin-Derived Monomers and Oligomers in Low-Molecular-Weight Fractions Separated from Depolymerized Black Liquor Retentate by Membrane Filtration

Molecules ◽  
2021 ◽  
Vol 26 (10) ◽  
pp. 2887
Author(s):  
Kena Li ◽  
Jens Prothmann ◽  
Margareta Sandahl ◽  
Sara Blomberg ◽  
Charlotta Turner ◽  
...  
Keyword(s):  
Molecular Weight ◽  
High Performance ◽  
Membrane Filtration ◽  
Black Liquor ◽  
Complex Mixture ◽  
Value Added ◽  
Kraft Pulping ◽  
Classification Model ◽  
Low Molecular Weight ◽  
Chemical Complexity

Base-catalyzed depolymerization of black liquor retentate (BLR) from the kraft pulping process, followed by ultrafiltration, has been suggested as a means of obtaining low-molecular-weight (LMW) compounds. The chemical complexity of BLR, which consists of a mixture of softwood and hardwood lignin that has undergone several kinds of treatment, leads to a complex mixture of LMW compounds, making the separation of components for the formation of value-added chemicals more difficult. Identifying the phenolic compounds in the LMW fractions obtained under different depolymerization conditions is essential for the upgrading process. In this study, a state-of-the-art nontargeted analysis method using ultra-high-performance supercritical fluid chromatography coupled to high-resolution multiple-stage tandem mass spectrometry (UHPSFC/HRMSn) combined with a Kendrick mass defect-based classification model was applied to analyze the monomers and oligomers in the LMW fractions separated from BLR samples depolymerized at 170–210 °C. The most common phenolic compound types were dimers, followed by monomers. A second round of depolymerization yielded low amounts of monomers and dimers, while a high number of trimers were formed, thought to be the result of repolymerization.

A DECISION TREE-BASED CLASSIFICATION APPROACH TO RULE EXTRACTION FOR SECURITY ANALYSIS

2006 ◽  
Vol 05 (01) ◽  
pp. 227-240 ◽  
Author(s):  
N. REN ◽  
M. ZARGHAM ◽  
S. RAHIMI
Keyword(s):  
Decision Tree ◽  
High Performance ◽  
Security Analysis ◽  
Predictive Performance ◽  
Classification Model ◽  
Stock Selection ◽  
Selection Rules ◽  
Stock Portfolios ◽  
Decision Tree Classification ◽  
C4.5 Decision Tree

Stock selection rules are extensively utilized as the guideline to construct high performance stock portfolios. However, the predictive performance of the rules developed by some economic experts in the past has decreased dramatically for the current stock market. In this paper, C4.5 decision tree classification method was adopted to construct a model for stock prediction based on the fundamental stock data, from which a set of stock selection rules was derived. The experimental results showed that the generated rules have exceptional predictive performance. Moreover, it also demonstrated that the C4.5 decision tree classification model can work efficiently on the high noise stock data domain.

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