Symmetry detection algorithm to classify the tea grades using artificial intelligence
In classifying tea grades the current available used methods are not consistent to extract the tea texture features accurately. which is resulting low efficiency and poor classification results. To overcome this challenge the novel symmetry detection algorithm based on artificial intelligence is pro...
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| Veröffentlicht in: | Microprocessors and microsystems 2021-03, Vol.81, p.103738, Article 103738 |
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| container_title | Microprocessors and microsystems |
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| creator | Jiang, Mingfu Chen, Zhuo |
| description | In classifying tea grades the current available used methods are not consistent to extract the tea texture features accurately. which is resulting low efficiency and poor classification results. To overcome this challenge the novel symmetry detection algorithm based on artificial intelligence is proposed. The symmetry detection algorithm is used to detect the edge of tea image, which is the region of interest to process further based on the captured data. The vertigo model is constructed from captured inputs. The tea feature vector is extracted with the help of BP algorithm and the tea grade is analyzed using the artificial intelligence support. Using the proposed algorithm method, the experimental results shows high classification efficiency and higher accuracy results. |
| doi_str_mv | 10.1016/j.micpro.2020.103738 |
| format | Article |
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To overcome this challenge the novel symmetry detection algorithm based on artificial intelligence is proposed. The symmetry detection algorithm is used to detect the edge of tea image, which is the region of interest to process further based on the captured data. The vertigo model is constructed from captured inputs. The tea feature vector is extracted with the help of BP algorithm and the tea grade is analyzed using the artificial intelligence support. Using the proposed algorithm method, the experimental results shows high classification efficiency and higher accuracy results.</description><identifier>ISSN: 0141-9331</identifier><identifier>EISSN: 1872-9436</identifier><identifier>DOI: 10.1016/j.micpro.2020.103738</identifier><language>eng</language><publisher>Kidlington: Elsevier B.V</publisher><subject>Algorithms ; Artificial intelligence ; Artificial intelligence classification ; Classification ; Feature extraction ; Feature vector ; Symmetry ; Symmetry detection ; Symmetry detection algorithm ; Tea grade ; Vertigo</subject><ispartof>Microprocessors and microsystems, 2021-03, Vol.81, p.103738, Article 103738</ispartof><rights>2020</rights><rights>Copyright Elsevier BV Mar 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c334t-db97b64e81604e5a5d002ea52a9bd42aeefd536b10559d9bdd59a8d11acb109a3</citedby><cites>FETCH-LOGICAL-c334t-db97b64e81604e5a5d002ea52a9bd42aeefd536b10559d9bdd59a8d11acb109a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.micpro.2020.103738$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,777,781,3537,27905,27906,45976</link.rule.ids></links><search><creatorcontrib>Jiang, Mingfu</creatorcontrib><creatorcontrib>Chen, Zhuo</creatorcontrib><title>Symmetry detection algorithm to classify the tea grades using artificial intelligence</title><title>Microprocessors and microsystems</title><description>In classifying tea grades the current available used methods are not consistent to extract the tea texture features accurately. which is resulting low efficiency and poor classification results. 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Using the proposed algorithm method, the experimental results shows high classification efficiency and higher accuracy results.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Artificial intelligence classification</subject><subject>Classification</subject><subject>Feature extraction</subject><subject>Feature vector</subject><subject>Symmetry</subject><subject>Symmetry detection</subject><subject>Symmetry detection algorithm</subject><subject>Tea grade</subject><subject>Vertigo</subject><issn>0141-9331</issn><issn>1872-9436</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kElLBDEUhIMoOI7-Aw8Bzz1m7eUiiLjBgAedc0gnr3vS9DImGaH_vRnas6cHRVU96kPolpINJTS_7zaDMwc_bRhhJ4kXvDxDK1oWLKsEz8_RilBBs4pzeomuQugIIZLkbIV2n_MwQPQzthDBRDeNWPft5F3cDzhO2PQ6BNfMOO4BR9C49dpCwMfgxhZrH13jjNM9dmOEvnctjAau0UWj-wA3f3eNdi_PX09v2fbj9f3pcZsZzkXMbF0VdS6gpDkRILW0hDDQkumqtoJpgMZKnteUSFnZpFlZ6dJSqk3SKs3X6G7pTeO_jxCi6qajH9NLxSTnRUUKIpJLLC7jpxA8NOrg3aD9rChRJ4CqUwtAdQKoFoAp9rDEIC34ceBVMO60zjqfSCk7uf8LfgFNOHy6</recordid><startdate>202103</startdate><enddate>202103</enddate><creator>Jiang, Mingfu</creator><creator>Chen, Zhuo</creator><general>Elsevier B.V</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>202103</creationdate><title>Symmetry detection algorithm to classify the tea grades using artificial intelligence</title><author>Jiang, Mingfu ; Chen, Zhuo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c334t-db97b64e81604e5a5d002ea52a9bd42aeefd536b10559d9bdd59a8d11acb109a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Artificial intelligence classification</topic><topic>Classification</topic><topic>Feature extraction</topic><topic>Feature vector</topic><topic>Symmetry</topic><topic>Symmetry detection</topic><topic>Symmetry detection algorithm</topic><topic>Tea grade</topic><topic>Vertigo</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jiang, Mingfu</creatorcontrib><creatorcontrib>Chen, Zhuo</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Microprocessors and microsystems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jiang, Mingfu</au><au>Chen, Zhuo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Symmetry detection algorithm to classify the tea grades using artificial intelligence</atitle><jtitle>Microprocessors and microsystems</jtitle><date>2021-03</date><risdate>2021</risdate><volume>81</volume><spage>103738</spage><pages>103738-</pages><artnum>103738</artnum><issn>0141-9331</issn><eissn>1872-9436</eissn><abstract>In classifying tea grades the current available used methods are not consistent to extract the tea texture features accurately. which is resulting low efficiency and poor classification results. 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| source | Elsevier ScienceDirect Journals |
| subjects | Algorithms Artificial intelligence Artificial intelligence classification Classification Feature extraction Feature vector Symmetry Symmetry detection Symmetry detection algorithm Tea grade Vertigo |
| title | Symmetry detection algorithm to classify the tea grades using artificial intelligence |
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