Classification and Quality Analysis of Food Grains

Siddagangappa, Megha R.; Kulkarni, Asso.Prof. A. H.

doi:10.9790/0661-16430110

Cited by 23 publications

(7 citation statements)

References 8 publications

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“…The accuracy delivered by various features for example 82.61% for shape, 88.00% for texture and 87.27% for texture n-shape respectively. Siddagangappa and Kulkarni (2014) suggested an automatic probabilistic neural network for distinguishing proof and quality investigation and grading. The proposed neural system used color as well as geometrical features for classification.…”

Section: B Research Studies Based On Blending Of Morphological With mentioning

confidence: 99%

Feature Based Qualitative Classification of Rice Varieties: A Review

Komal¹,

Sethi²,

Bawa³

2020

JSR

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Rice is regarded as main staple food for around 80% of the Southeast Asia population alone. As most nations achieving selfcompetency in rice production, better quality rice is of utmost priority of consumer. It is very tedious work for consumers to analyze the good quality and rice grading in the market. Rice grains' quality determination is judged visually by human inspectors following manual process which is neither objective in nature nor effective due to non-reliability of results occurring as a result of inexperienced inspectors or human errors. So need for an automatic rice quality grading system arises which can eliminate the inadequacies of manual quality grading process. In this paper, Different techniques of machine learning, deep learning and image processing considering morphological, color, shape, textural as well as other features of rice are analyzed to review the current research scenario in automatic quality grading process. Various procedures and methods are considered for the review purpose to analyze the quality of rice grains on the basis of different features of rice.

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Section: B Research Studies Based On Blending Of Morphological With mentioning

confidence: 99%

Feature Based Qualitative Classification of Rice Varieties: A Review

Komal¹,

Sethi²,

Bawa³

2020

JSR

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“…The main aim of classification is to group a set of multidimensional observations, represented as data points, scattered through N-dimensional space, into clusters, according to their similarities and dissimilarities. Several different classification algorithms, have been proposed in the literature [18]. Multilayer Feed-Forward Back-Propagation based Artificial Neural Network has been applied successfully in many different problems since the advent of the gradient descent back-propagation learning algorithm for the purpose of object recognition [19].…”

Section: Classificationmentioning

confidence: 99%

Detection of Foreign Materials in Wheat Kernels using Regional Texture Descriptors

Julka*,

Singh

2019

IJRTE

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The present paper reports the development of an efficient machine vision system for automatic detection of foreign materials in wheat kernels using regional texture descriptors. In this system, the detection task is performed in two phases. These phases include features extraction phase followed by classification phase. New surface texture descriptors of wheat kernels are developed using Non-Shannon entropies in this work. These entropies are defined using intensity histograms of wheat and non-wheat regions in the given image. Such an attempt has not been made earlier . Experimental results on a database of about 2635 wheat and non-wheat components from 63 images confirm the effectiveness of the proposed method. The classification task is performed by the neural classifier in the proposed machine vision system. An accuracy of more than 98.5% is achieved using proposed system. However, the results of present investigations are quite promising.

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“…Manually determining the type of wheat needs expert judgment and takes time. When an array of seeds appears so similar, manually distinguishing them becomes a challenging process [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Wheat Seed Classification: Utilizing Ensemble Machine Learning Approach

Khatri¹,

Agrawal²,

Chatterjee³

2022

Scientific Programming

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Recognizing and authenticating wheat varieties is critical for quality evaluation in the grain supply chain, particularly for methods for seed inspection. Recognition and verification of grains are carried out manually through direct visual examination. Automatic categorization techniques based on machine learning and computer vision offered fast and high-throughput solutions. Even yet, categorization remains a complicated process at the varietal level. The paper utilized machine learning approaches for classifying wheat seeds. The seed classification is performed based on 7 physical features: area of wheat, perimeter of wheat, compactness, length of the kernel, width of the kernel, asymmetry coefficient, and kernel groove length. The dataset is collected from the UCI library and has 210 occurrences of wheat kernels. The dataset contains kernels from three wheat varieties Kama, Rosa, and Canadian, with 70 components chosen at random for the experiment. In the first phase, K-nearest neighbor, classification and regression tree, and Gaussian Naïve Bayes algorithms are implemented for classification. The results of these algorithms are compared with the ensemble approach of machine learning. The results reveal that accuracies calculated for KNN, decision, and Naïve Bayes classifiers are 92%, 94%, and 92%, respectively. The highest accuracy of 95% is achieved through the ensemble classifier in which decision is made based on hard voting.

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Classification and Quality Analysis of Food Grains

Cited by 23 publications

References 8 publications

Feature Based Qualitative Classification of Rice Varieties: A Review

Feature Based Qualitative Classification of Rice Varieties: A Review

Detection of Foreign Materials in Wheat Kernels using Regional Texture Descriptors

Wheat Seed Classification: Utilizing Ensemble Machine Learning Approach

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