A grain quality classification system

Pabamalie, L. A. I.; Premaratne, H. L.

doi:10.1109/i-society16502.2010.6018794

Cited by 18 publications

(8 citation statements)

References 4 publications

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“…Pabamalie and Premaratne proposed 21 texture and 10 color based features for the classification of milled rice [78]. These features were given to Neural Network achieving a test accuracy of 80.5% on a data split of 72% for training and 28% for testing.…”

Section: A Era 1 (1996-2010)mentioning

confidence: 99%

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On the Frontiers of Rice Grain Analysis, Classification and Quality Grading: A Review

Ahmed

Ali²,

Khan

2021

IEEE Access

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Section: A Era 1 (1996-2010)mentioning

confidence: 99%

“…In [78], an approach based on texture and color features was proposed for milled rice quality recognition. A total of 17 features were extracted for grading the rice grains into Premium, Grade 1, Grade 2, and Grade 3.…”

Section: Automated Grading Of Rice Grainsmentioning

confidence: 99%

On the Frontiers of Rice Grain Analysis, Classification and Quality Grading: A Review

Ahmed

Ali²,

Khan

2021

IEEE Access

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“…The combined model defined by morphological and color features achieved a classification accuracy of 98.5% for barley, 99.97% for CWRS, 99.93% for oat, and 100% for rye and CWAD. L.A.I.Pabamalie, H.L.Premaratne, [13] focused on providing a better approach for identification of rice quality by using neural network and image processing concepts. Here a back propagation neural network with two hidden layers has been developed for the quality classification.…”

Section: Related Workmentioning

confidence: 99%

Classification and Quality Analysis of Food Grains

Siddagangappa¹,

Kulkarni²

2014

IOSRJCE

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In the present grain-handling scenario, grain type and quality are identified manually by visual inspection which is tedious and not accurate. There is need for the growth of fast, accurate and objective system for quality determination of food grains. An automated system is introduced which is used for grain type identification and analysis of rice quality (i.e. Basmati, Boiled and Delhi) and grade (i.e. grade 1, grade 2, and grade3) using Probabilistic Neural Network. This paper proposes a model that uses color and geometrical features as attributes for classification. The grading of rice sample is done according to the size of the grain kernel and presence of impurities. A good classification accuracy is achieved using only 6 features, i.e. mean of RGB colors and 3 geometrical features. The total success rate of type identification is 98% and total success rate of quality analysis and grading of rice is 90% and 92% respectively.

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“…Classification and identification of various types of bulk grains are presented in [31–34]. However, it is found that works related to identification and classifications of rice based on bulk image are quite limited and are presented in [35, 36]. In order to carry out a classification task, one need to look for certain attributes that can best distinguish objects to be classified.…”

Section: Introductionmentioning

confidence: 99%

“…Classification of bulk images of cereal grains using texture features based on GLCM and GLRLM is presented in [31–34, 38, 39]. Rice grain classification based on bulk images using GLCM texture is presented in [36]. Applications of GLCM based texture features for classification task other than cereal grains are also presented in [40–44].…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of texture feature extraction techniques for rice grain classification

Singh

Chaudhury

2020

IET image process

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Classifications of eight different varieties of rice grain are discussed in this study based on various texture models. Four local texture feature extraction techniques are proposed and three sets of texture features (SET‐A, SET‐B and SET‐C) are formed, for the classification task. Performances of the proposed feature sets are compared with the existing techniques based on, run length matrix, co‐occurrence matrix, size zone matrix, neighbourhood grey tone difference matrix and wavelet decomposition, towards classification of rice grain using a back propagation neural network (BPNN). The proposed techniques are also tested against publicly available data from Brodatz's texture data set and their results are compared with other techniques. The classification accuracy by the BPNN classifier is also compared with other statistical classifiers namely, K‐nearest neighbour, linear discriminant classifier and Naive Bayes classifier. It is found that, the proposed feature sets yield better classification results on both rice data and Brodatz's data. Results show that, feature SET‐B, is able to classify rice grain with an average classification accuracy of 99.63% with a minimum of six features.

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A grain quality classification system

Cited by 18 publications

References 4 publications

On the Frontiers of Rice Grain Analysis, Classification and Quality Grading: A Review

On the Frontiers of Rice Grain Analysis, Classification and Quality Grading: A Review

Classification and Quality Analysis of Food Grains

Comparative analysis of texture feature extraction techniques for rice grain classification

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