Computer vision‐based method for classification of wheat grains using artificial neural network
BACKGROUND A simplified computer vision‐based application using artificial neural network (ANN) depending on multilayer perceptron (MLP) for accurately classifying wheat grains into bread or durum is presented. The images of 100 bread and 100 durum wheat grains are taken via a high‐resolution camera...
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| Veröffentlicht in: | Journal of the science of food and agriculture 2017-06, Vol.97 (8), p.2588-2593 |
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| container_title | Journal of the science of food and agriculture |
| container_volume | 97 |
| creator | Sabanci, Kadir Kayabasi, Ahmet Toktas, Abdurrahim |
| description | BACKGROUND
A simplified computer vision‐based application using artificial neural network (ANN) depending on multilayer perceptron (MLP) for accurately classifying wheat grains into bread or durum is presented. The images of 100 bread and 100 durum wheat grains are taken via a high‐resolution camera and subjected to pre‐processing. The main visual features of four dimensions, three colors and five textures are acquired using image‐processing techniques (IPTs). A total of 21 visual features are reproduced from the 12 main features to diversify the input population for training and testing the ANN model. The data sets of visual features are considered as input parameters of the ANN model. The ANN with four different input data subsets is modelled to classify the wheat grains into bread or durum. The ANN model is trained with 180 grains and its accuracy tested with 20 grains from a total of 200 wheat grains.
RESULTS
Seven input parameters that are most effective on the classifying results are determined using the correlation‐based CfsSubsetEval algorithm to simplify the ANN model. The results of the ANN model are compared in terms of accuracy rate. The best result is achieved with a mean absolute error (MAE) of 9.8 × 10−6 by the simplified ANN model.
CONCLUSION
This shows that the proposed classifier based on computer vision can be successfully exploited to automatically classify a variety of grains. © 2016 Society of Chemical Industry |
| doi_str_mv | 10.1002/jsfa.8080 |
| format | Article |
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A simplified computer vision‐based application using artificial neural network (ANN) depending on multilayer perceptron (MLP) for accurately classifying wheat grains into bread or durum is presented. The images of 100 bread and 100 durum wheat grains are taken via a high‐resolution camera and subjected to pre‐processing. The main visual features of four dimensions, three colors and five textures are acquired using image‐processing techniques (IPTs). A total of 21 visual features are reproduced from the 12 main features to diversify the input population for training and testing the ANN model. The data sets of visual features are considered as input parameters of the ANN model. The ANN with four different input data subsets is modelled to classify the wheat grains into bread or durum. The ANN model is trained with 180 grains and its accuracy tested with 20 grains from a total of 200 wheat grains.
RESULTS
Seven input parameters that are most effective on the classifying results are determined using the correlation‐based CfsSubsetEval algorithm to simplify the ANN model. The results of the ANN model are compared in terms of accuracy rate. The best result is achieved with a mean absolute error (MAE) of 9.8 × 10−6 by the simplified ANN model.
CONCLUSION
This shows that the proposed classifier based on computer vision can be successfully exploited to automatically classify a variety of grains. © 2016 Society of Chemical Industry</description><identifier>ISSN: 0022-5142</identifier><identifier>EISSN: 1097-0010</identifier><identifier>DOI: 10.1002/jsfa.8080</identifier><identifier>PMID: 27718230</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Accuracy ; Algorithms ; artificial neural network (ANN) ; Artificial neural networks ; Bread ; Classification ; Computer vision ; Food science ; Grain ; Image acquisition ; Image classification ; Image processing ; Image Processing, Computer-Assisted - methods ; Mathematical models ; multilayer perceptron ; Multilayer perceptrons ; Neural networks ; Neural Networks (Computer) ; Parameters ; Seeds - chemistry ; Seeds - classification ; Triticum - chemistry ; Triticum - classification ; Wheat ; wheat grains</subject><ispartof>Journal of the science of food and agriculture, 2017-06, Vol.97 (8), p.2588-2593</ispartof><rights>2016 Society of Chemical Industry</rights><rights>2016 Society of Chemical Industry.</rights><rights>2017 Society of Chemical Industry</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4190-91827b45428abf46329862cb537e4c809c702f4b7cf9d8d835174b1b0433fe8c3</citedby><cites>FETCH-LOGICAL-c4190-91827b45428abf46329862cb537e4c809c702f4b7cf9d8d835174b1b0433fe8c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjsfa.8080$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjsfa.8080$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,777,781,1412,27905,27906,45555,45556</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27718230$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sabanci, Kadir</creatorcontrib><creatorcontrib>Kayabasi, Ahmet</creatorcontrib><creatorcontrib>Toktas, Abdurrahim</creatorcontrib><title>Computer vision‐based method for classification of wheat grains using artificial neural network</title><title>Journal of the science of food and agriculture</title><addtitle>J Sci Food Agric</addtitle><description>BACKGROUND
A simplified computer vision‐based application using artificial neural network (ANN) depending on multilayer perceptron (MLP) for accurately classifying wheat grains into bread or durum is presented. The images of 100 bread and 100 durum wheat grains are taken via a high‐resolution camera and subjected to pre‐processing. The main visual features of four dimensions, three colors and five textures are acquired using image‐processing techniques (IPTs). A total of 21 visual features are reproduced from the 12 main features to diversify the input population for training and testing the ANN model. The data sets of visual features are considered as input parameters of the ANN model. The ANN with four different input data subsets is modelled to classify the wheat grains into bread or durum. The ANN model is trained with 180 grains and its accuracy tested with 20 grains from a total of 200 wheat grains.
RESULTS
Seven input parameters that are most effective on the classifying results are determined using the correlation‐based CfsSubsetEval algorithm to simplify the ANN model. The results of the ANN model are compared in terms of accuracy rate. The best result is achieved with a mean absolute error (MAE) of 9.8 × 10−6 by the simplified ANN model.
CONCLUSION
This shows that the proposed classifier based on computer vision can be successfully exploited to automatically classify a variety of grains. © 2016 Society of Chemical Industry</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>artificial neural network (ANN)</subject><subject>Artificial neural networks</subject><subject>Bread</subject><subject>Classification</subject><subject>Computer vision</subject><subject>Food science</subject><subject>Grain</subject><subject>Image acquisition</subject><subject>Image classification</subject><subject>Image processing</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Mathematical models</subject><subject>multilayer perceptron</subject><subject>Multilayer perceptrons</subject><subject>Neural networks</subject><subject>Neural Networks (Computer)</subject><subject>Parameters</subject><subject>Seeds - chemistry</subject><subject>Seeds - classification</subject><subject>Triticum - chemistry</subject><subject>Triticum - classification</subject><subject>Wheat</subject><subject>wheat grains</subject><issn>0022-5142</issn><issn>1097-0010</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp10MtO3DAUBmCroirDZcELIEtsYBE4viS2l2gEbRESC9p15Dg2eEjiwU4YseMR-ox9knoY6AKJ1Vmc7_w6-hE6IHBKAOjZIjl9KkHCFzQjoEQBQGALzfKOFiXhdBvtpLQAAKWq6hvapkIQSRnMkJ6HfjmNNuInn3wY_r78aXSyLe7teB9a7ELEptMpeeeNHrPAweHVvdUjvovaDwlPyQ93WMdxTbzu8GCn-DrGVYgPe-ir012y-29zF_2-vPg1_1Fc33z_OT-_LgwnCgqVHxINLzmVunG8YlTJipqmZMJyI0EZAdTxRhinWtlKVhLBG9IAZ8xZadguOt7kLmN4nGwa694nY7tODzZMqSb5hEkBqsz06ANdhCkO-busVAkVI5RmdbJRJoaUonX1Mvpex-eaQL3uvV73Xq97z_bwLXFqetv-l-9FZ3C2ASvf2efPk-qr28vz18h_fdWNkQ</recordid><startdate>201706</startdate><enddate>201706</enddate><creator>Sabanci, Kadir</creator><creator>Kayabasi, Ahmet</creator><creator>Toktas, Abdurrahim</creator><general>John Wiley & Sons, Ltd</general><general>John Wiley and Sons, Limited</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QL</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7ST</scope><scope>7T5</scope><scope>7T7</scope><scope>7TA</scope><scope>7TB</scope><scope>7TM</scope><scope>7U5</scope><scope>7U9</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>H94</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M7N</scope><scope>P64</scope><scope>SOI</scope><scope>7X8</scope></search><sort><creationdate>201706</creationdate><title>Computer vision‐based method for classification of wheat grains using artificial neural network</title><author>Sabanci, Kadir ; Kayabasi, Ahmet ; Toktas, Abdurrahim</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4190-91827b45428abf46329862cb537e4c809c702f4b7cf9d8d835174b1b0433fe8c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>artificial neural network (ANN)</topic><topic>Artificial neural networks</topic><topic>Bread</topic><topic>Classification</topic><topic>Computer vision</topic><topic>Food science</topic><topic>Grain</topic><topic>Image acquisition</topic><topic>Image classification</topic><topic>Image processing</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Mathematical models</topic><topic>multilayer perceptron</topic><topic>Multilayer perceptrons</topic><topic>Neural networks</topic><topic>Neural Networks (Computer)</topic><topic>Parameters</topic><topic>Seeds - chemistry</topic><topic>Seeds - classification</topic><topic>Triticum - chemistry</topic><topic>Triticum - classification</topic><topic>Wheat</topic><topic>wheat grains</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sabanci, Kadir</creatorcontrib><creatorcontrib>Kayabasi, Ahmet</creatorcontrib><creatorcontrib>Toktas, Abdurrahim</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Environment Abstracts</collection><collection>Immunology Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environment Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of the science of food and agriculture</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sabanci, Kadir</au><au>Kayabasi, Ahmet</au><au>Toktas, Abdurrahim</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Computer vision‐based method for classification of wheat grains using artificial neural network</atitle><jtitle>Journal of the science of food and agriculture</jtitle><addtitle>J Sci Food Agric</addtitle><date>2017-06</date><risdate>2017</risdate><volume>97</volume><issue>8</issue><spage>2588</spage><epage>2593</epage><pages>2588-2593</pages><issn>0022-5142</issn><eissn>1097-0010</eissn><abstract>BACKGROUND
A simplified computer vision‐based application using artificial neural network (ANN) depending on multilayer perceptron (MLP) for accurately classifying wheat grains into bread or durum is presented. The images of 100 bread and 100 durum wheat grains are taken via a high‐resolution camera and subjected to pre‐processing. The main visual features of four dimensions, three colors and five textures are acquired using image‐processing techniques (IPTs). A total of 21 visual features are reproduced from the 12 main features to diversify the input population for training and testing the ANN model. The data sets of visual features are considered as input parameters of the ANN model. The ANN with four different input data subsets is modelled to classify the wheat grains into bread or durum. The ANN model is trained with 180 grains and its accuracy tested with 20 grains from a total of 200 wheat grains.
RESULTS
Seven input parameters that are most effective on the classifying results are determined using the correlation‐based CfsSubsetEval algorithm to simplify the ANN model. The results of the ANN model are compared in terms of accuracy rate. The best result is achieved with a mean absolute error (MAE) of 9.8 × 10−6 by the simplified ANN model.
CONCLUSION
This shows that the proposed classifier based on computer vision can be successfully exploited to automatically classify a variety of grains. © 2016 Society of Chemical Industry</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><pmid>27718230</pmid><doi>10.1002/jsfa.8080</doi><tpages>6</tpages></addata></record> |
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| ispartof | Journal of the science of food and agriculture, 2017-06, Vol.97 (8), p.2588-2593 |
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| language | eng |
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| source | MEDLINE; Wiley Online Library Journals Frontfile Complete |
| subjects | Accuracy Algorithms artificial neural network (ANN) Artificial neural networks Bread Classification Computer vision Food science Grain Image acquisition Image classification Image processing Image Processing, Computer-Assisted - methods Mathematical models multilayer perceptron Multilayer perceptrons Neural networks Neural Networks (Computer) Parameters Seeds - chemistry Seeds - classification Triticum - chemistry Triticum - classification Wheat wheat grains |
| title | Computer vision‐based method for classification of wheat grains using artificial neural network |
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