scholarly journals Classification of Grain s and Quality Analysis u sing Deep Learning

2021 ◽  
Vol 11 (1) ◽  
pp. 244-250
Author(s):  
Priyanshu Shrivastava ◽  
◽  
Karan Singh ◽  
Ashish Pancham ◽  
◽  
...  
Keyword(s):  
Seed Coat ◽  
Back Propagation ◽  
Quality Analysis ◽  
Rice Grain ◽  
Traditional Grading ◽  
Kernel Length ◽  
Cotyledon Colour ◽  
Low Efficiency ◽  
Processing Techniques ◽  
Smart Machine

There are various varieties of Rice and lentils. Price fabrication and adulteration have been some of the various issues faced by the consumers, farmers and wholesale retailers. Traditional methods for Identification of these similar types of grains and their quality analysis are crude and inaccurate. Methods were tried to implemented earlier but due to financial inability and low efficiency, they weren’t successful. To overcome this problem, the project proposes a method that uses a machine learning technique for identification and quality analysis of these grains. Rice and Lentils which have the maximum consumption have been selected. Lentils are designated into classes based on colors. The technique of determining the elegance of a lentil is with the aid of seed coat shade. Red lentils can be confirmed through the cotyledon coloration. Lentil types may also have a huge variety of seed coat colors from inexperienced, red, speckled inexperienced, black and tan. The cotyledon colour may be red, yellow or inexperienced. The size and color of every Indian Lentil type (i.e. Red, Green, and Yellow, Black, White) are decided to be large or Medium or small, then size and colour end up part of the grade name. An smart machine is used to perceive the kind of Indian lentils from bulk samples. The proposed machine allows kernel length and coloration size using picture processing techniques. These Lentil size measurements, when combined with color attributes of the sample, classify three lentil varieties commonly grown in India with the highest accuracy. Rice is one of most consumed grains in India so its quality is of utmost importance. In this project, we identify and grade five types of rice and grade them with the help of their distinguished features such as size, color, shape, and surface. The project works in three phases viz., Feature Extraction, Training, and Testing. Various rice grain has a different shape, size, surface and various lentils come in different colors, Hence the feature that will be extracted is texture and colors. The method of regression will be adopted for the grading mechanism where the output will be in terms of percentage purity. The methodology for the extraction of the feature will be GLCM and Edge Detection where for supervised learning SVM and Back Propagation will be utilized. The project provides an efficient replacement for the traditional grading mechanism and standardizes the pricing of farm products based on their quality only.

scholarly journals Rice Quality Analysis and Classification Using Image Processing Techniques

2021 ◽  
Vol 10 (6) ◽  
pp. 79-82
Author(s):  
Vijay Sonawane ◽  
Nikhil Gaikwad ◽  
Hrushikesh Mandekar ◽  
Kishore Baradkar ◽  
Chetan Gunjal
Keyword(s):  
Image Processing ◽  
Quality Analysis ◽  
World Population ◽  
Rice Grain ◽  
Rice Grains ◽  
Unique Shape ◽  
Good For ◽  
Phosphorus And Potassium ◽  
Processing Techniques

More than half the world's people consume rice every day and fulfills over 21% calorific requirement of world population. It is considered the whole grain which is rich in fiber and it contains 80 percent with protein, phosphorus, and potassium. There are hundreds of different varieties of rice and each rice grain has a unique shape, texture, and flavor that make it just right for certain dishes. The quality of rice between various types has different standards. Therefore, you must select the best quality rice because rice with best quality is not only good for consumption but also good for health. Analyzing grain sample manually is a tedious task and also time consuming. The paper presents the solution to analysis and grading of rice grains using image processing techniques. Image reduction, image enhancement, and image increment, object recognition in spatial domain is applied on grain by grain of different samples of rice to determine its size, color and quality as whole to grade the grain of rice. We find the endpoints of each grains and after we measure the length and breadth of rice grains.

scholarly journals Survey of rice seed quality analysis for varietal purity estimation by using image processing techniques

2018 ◽  
Vol 7 (1.7) ◽  
pp. 34
Author(s):  
S. Durai ◽  
C. Mahesh ◽  
T. Sujithra ◽  
A. Suresh
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
South India ◽  
Seed Quality ◽  
Quality Analysis ◽  
Rice Seed ◽  
Two Factors ◽  
Computer Vision Technology ◽  
Processing Techniques ◽  
Varietal Purity

 In south India rice is the major food source and in agriculture, rice production covers more than 70 percentages of entire forming. But in recent the production only from south India not enough to satisfy the need of all, such a huge demand is there. The better production comes from the selection of good seeds. Up to now formers depend on two factors for selecting better seeds, One is the brand which is approved by some quality standards and second one is analyzed manually by experienced people. Both are risky one, we are not pretty much sure the accuracy of analyze. The second one is seeing and feeling. The inspection is not consistent also very time consuming. In the other way we can use computer vision technology to analyze the quality of the seeds. In recent years many of the big industries they are using computer vision technology with Digital Image Processing for many of the applications. In this Paper we are going to discuss the different seed quality analyzing methods and accuracy of result also. Moreover there are different factors and features are there for it, here we are going to study about varietal purity estimation by different methods.

scholarly journals Klasifikasi Sel Darah Putih dan Sel Limfoblas Menggunakan Metode Multilayer Perceptron Backpropagation

2019 ◽  
Vol 9 (2) ◽  
pp. 173
Author(s):  
Apri Nur Liyantoko ◽  
Ika Candradewi ◽  
Agus Harjoko
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Blood Cells ◽  
White Blood Cells ◽  
Back Propagation ◽  
Cell Types ◽  
Diagnostic Process ◽  
The Subject ◽  
Processing Techniques

 Leukemia is a type of cancer that is on white blood cell. This disease are characterized by abundance of abnormal white blood cell called lymphoblast in the bone marrow. Classification of blood cell types, calculation of the ratio of cell types and comparison with normal blood cells can be the subject of diagnosing this disease. The diagnostic process is carried out manually by hematologists through microscopic image. This method is likely to provide a subjective result and time-consuming.The application of digital image processing techniques and machine learning in the process of classifying white blood cells can provide more objective results. This research used thresholding method as segmentation and  multilayer method of back propagation perceptron with variations in the extraction of textural features, geometry, and colors. The results of segmentation testing in this study amounted to 68.70%. Whereas the classification test shows that the combination of feature extraction of GLCM features, geometry features, and color features gives the best results. This test produces an accuration value 91.43%, precision value of 50.63%, sensitivity 56.67%, F1Score 51.95%, and specitifity 94.16%.

scholarly journals Stages of arc atomic emission spectrometry development as applied to the solid geological samples’ analysis

2021 ◽  
Vol 25 (4) ◽  
pp. 280-295
Author(s):  
Irina E. Vasil’eva ◽  
◽  
Elena V. Shabanova ◽  
Keyword(s):  
Arc Discharge ◽  
Back Propagation ◽  
Atomic Emission ◽  
Atomic Emission Spectrometry ◽  
Emission Spectrometry ◽  
Geological Samples ◽  
Information Models ◽  
Spectra Processing ◽  
Quantitative Results ◽  
Processing Techniques

The atomic emission spectrometry (AES) with arc discharge method evolution is inextricably linked with the fundamental scientific discoveries made in the 19th and 20th centuries, and it also reflects the change of scientific paradigms in a specific field of natural science – analytical chemistry. Theoretical comprehension and generalization of experimental data, along with the improving spectral equipment and methodological techniques for determining the elemental and material composition of solid geological samples, increased the accuracy of the analysis results i.e. the results were translated from qualitative to semi-quantitative and quantitative. Modern computerized equipment for direct AES with arc discharge provides minimal errors in measuring the spectral intensity due to the high stability of the excitation source of the spectra of atoms and molecules, the use of high-power polychromators and express digital recording of spectra by multi-channel detectors. However, in the commercial software of spectrometers, only the methods of manual spectra processing proposed in the 30s of the last century are programmed. That limits the possibilities of improving the analysis quality. The time has come to use the developed concept of computer processing of big spectral data, which is based on the information models of chemical analysis and the back propagation of error, in order to select the best models. Current article shows that the information models of computer spectra interpretation obtained from direct arc AES multi-element techniques of geological samples’ analysis using the injection-spillage method provide better quantitative results (category III of accuracy) due to a more complete account of spectral and matrix influences compared to the routine processing techniques.

scholarly journals Food Quality Research Basedon Various Image Processing Techniques

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1825-1827
Keyword(s):  
Image Processing ◽  
Food Quality ◽  
Physical Characteristics ◽  
Healthy Food ◽  
Quality Analysis ◽  
Quality Research ◽  
Image Processing Techniques ◽  
Naked Eye ◽  
Processing Techniques ◽  
Efficiency And Reliability

Quality analysis and inspection of different kind of foods based on various attributes using image processing. Different kind of foods are examined based on their physical characteristics such as size, colour, blob, shape and texture. Various methodologies are used for inspection as well as for detection of foods to identify and quantify the features based on mentioned characteristics. This technique helped in recognizing first-rate and healthy food that is to be released in markets. The main purpose of adopting this method is to include those specifications that could go unnoticed by naked eye. Also, inspection that is carried manually can be curtailed significantly thereby increasing the efficiency and reliability with better utilization of time.

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