scholarly journals Possibilities of Digital Control and Classification of Grain Crops

2020 ◽  
pp. 131-143
Author(s):  
D. Troshkin ◽  
A. Chertov ◽  
E. Gorbunova ◽  
A. Alyokhin
Keyword(s):  
Computer Vision ◽  
Digital Control ◽  
Visual Analysis ◽  
Quality Parameters ◽  
Malting Barley ◽  
Research Results ◽  
Grain Crops

The article presents the research results on determining the quality parameters of wheat and malting barley by computer vision, confirming the possibility of switching from subjective visual analysis to digital control.

scholarly journals Geometric and Topological Bases of a New Classification of Wood Vascular Tissues Part 1: Shape and Arrangement Classifications of Vessels

2021 ◽  
Vol 13 (14) ◽  
pp. 7545
Author(s):  
Nikolai Bardarov ◽  
Vladislav Todorov ◽  
Nicole Christoff
Keyword(s):  
Computer Vision ◽  
Detailed Analysis ◽  
Tree Species ◽  
Complete Solution ◽  
New Classification ◽  
Topological Characteristics ◽  
Definition Of ◽  
Wood Science ◽  
Quantitative Indicators

The need to identify wood by its anatomical features requires a detailed analysis of all the elements that make it up. This is a significant problem of structural wood science, the most general and complete solution of which is yet to be sought. In recent years, increasing attention has been paid to the use of computer vision methods to automate processes such as the detection, identification, and classification of different tissues and different tree species. The more successful use of these methods in wood anatomy requires a more precise and comprehensive definition of the anatomical elements, according to their geometric and topological characteristics. In this article, we conduct a detailed analysis of the limits of variation of the location and grouping of vessels in the observed microscopic samples. The present development offers criteria and quantitative indicators for defining the terms shape, location, and group of wood tissues. It is proposed to differentiate the quantitative indicators of the vessels depending on their geometric and topological characteristics. Thus, with the help of computer vision technics, it will be possible to establish topological characteristics of wood vessels, the extraction of which would be used to develop an algorithm for the automatic classification of tree species.

Study on Effect of Spheroidization on Hazardous Classification of ADN

2021 ◽  
Vol 881 ◽  
pp. 71-76
Author(s):  
Jian Yang ◽  
Hong Bin Li ◽  
Song Tao Ren ◽  
Peng Gang Jin ◽  
Zan Gao
Keyword(s):  
Temperature Stability ◽  
Impact Sensitivity ◽  
Classification Method ◽  
Ammonium Dinitramide ◽  
Classification Procedure ◽  
Research Results ◽  
Before And After ◽  
Hazard Classification ◽  
Friction Sensitivity

In order to determine the influence of spheroidization process of Ammonium dinitramide’s hazard grade, the hazardous division of Ammonium dinitramide before and after spheroidization is studied by using hazard classification procedure for combustible and explosive substances and articles standard (WJ20405) and hazard classification method and criterion for combusitible and explosive substances and articles standard (WJ20404). The research results show that spheroidization process can significantly improve the temperature stability of Ammonium dinitramide and significantly reduce friction sensitivity and impact sensitivity of Ammonium dinitramide. So spheroidization process can reduce the hazardous of Ammonium dinitramide and improve the safe character of Ammonium dinitramide.

Automatic recognition and classification of microseismic waveforms based on computer vision

2022 ◽  
Vol 121 ◽  
pp. 104327
Author(s):  
Jiaming Li ◽  
Shibin Tang ◽  
Kunyao Li ◽  
Shichao Zhang ◽  
Liexian Tang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Automatic Recognition

scholarly journals A Comparative Study on Application of Computer Vision and Fluorescence Imaging Spectroscopy for Detection of Huanglongbing Citrus Disease in the USA and Brazil

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Caio B. Wetterich ◽  
Ratnesh Kumar ◽  
Sindhuja Sankaran ◽  
José Belasque Junior ◽  
Reza Ehsani ◽  
...  
Keyword(s):  
Computer Vision ◽  
Fluorescence Imaging ◽  
Imaging Spectroscopy ◽  
Texture Features ◽  
Support Vector ◽  
Infected Leaf ◽  
Segmented Images ◽  
The Usa ◽  
Citrus Disease

The overall objective of this work was to develop and evaluate computer vision and machine learning technique for classification of Huanglongbing-(HLB)-infected and healthy leaves using fluorescence imaging spectroscopy. The fluorescence images were segmented using normalized graph cut, and texture features were extracted from the segmented images using cooccurrence matrix. The extracted features were used as an input into the classifier, support vector machine (SVM). The classification results were evaluated based on classification accuracies and number of false positives and false negatives. The results indicated that the SVM could classify HLB-infected leaf fluorescence intensities with up to 90% classification accuracy. Though the fluorescence intensities from leaves collected in Brazil and the USA were different, the method shows potential for detecting HLB.

scholarly journals Study and classification of the Crystallographic Orientation Distribution Function of a non-grain oriented electrical steel using computer vision system

2019 ◽  
Vol 8 (1) ◽  
pp. 1070-1083
Author(s):  
Roberto Fernandes Ivo ◽  
Douglas de Araújo Rodrigues ◽  
José Ciro dos Santos ◽  
Francisco Nélio Costa Freitas ◽  
Luis Flaávio Gaspar Herculano ◽  
...  
Keyword(s):  
Computer Vision ◽  
Distribution Function ◽  
Orientation Distribution Function ◽  
Crystallographic Orientation ◽  
Electrical Steel ◽  
Vision System ◽  
Orientation Distribution ◽  
Computer Vision System

Non-destructively Prediction of Quality Parameters of Dry-Cured Iberian Ham by Applying Computer Vision and Low-Field MRI

2019 ◽  
pp. 498-507
Author(s):  
Juan Pedro Torres ◽  
Mar Ávila ◽  
Andrés Caro ◽  
Trinidad Pérez-Palacios ◽  
Daniel Caballero
Keyword(s):  
Computer Vision ◽  
Quality Parameters ◽  
Low Field ◽  
Low Field Mri

Identification and Classification of Fungal Colonies in Moldy Paddy Based on Computer Vision

2018 ◽  
Vol 61 (5) ◽  
pp. 1497-1504
Author(s):  
Zhenjie Wang ◽  
Ke Sun ◽  
Lihui Du ◽  
Jian Yuan ◽  
Kang Tu ◽  
...  
Keyword(s):  
Computer Vision ◽  
Discriminant Analysis ◽  
Texture Features ◽  
Fungal Species ◽  
Projection Algorithm ◽  
Support Vector ◽  
Linear Discriminant ◽  
Sequential Selection ◽  
The Individual

Abstract. In this study, computer vision was used for the identification and classification of fungi on moldy paddy. To develop a rapid and efficient method for the classification of common fungal species found in stored paddy, computer vision was used to acquire images of individual colonies of growing fungi for three consecutive days. After image processing, the color, shape, and texture features were acquired and used in a subsequent discriminant analysis. Both linear (i.e., linear discriminant analysis and partial least squares discriminant analysis) and nonlinear (i.e., random forest and support vector machine [SVM]) pattern recognition models were employed for the classification of fungal colonies, and the results were compared. The results indicate that when using all of the features for three consecutive days, the performance of the nonlinear tools was superior to that of the linear tools, especially in the case of the SVM models, which achieved an accuracy of 100% on the calibration sets and an accuracy of 93.2% to 97.6% on the prediction sets. After sequential selection of projection algorithm, ten common features were selected for building the classification models. The results showed that the SVM model achieved an overall accuracy of 95.6%, 98.3%, and 99.0% on the prediction sets on days 2, 3, and 4, respectively. This work demonstrated that computer vision with several features is suitable for the identification and classification of fungi on moldy paddy based on the form of the individual colonies at an early growth stage during paddy storage. Keywords: Classification, Computer vision, Fungal colony, Feature selection, SVM.

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