Using Neural Network to Model the Relationship between Plant Surface Color and its Pigment
Combining the intelligent algorithm such as BP neural network and support vector maching (SVM) with traditional chemical method, this paper models the relationship between plant surface color and its pigment. Using the neural network model constructed above, people can figure out the content of plan...
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| creator | Zeng Qingmao Zhang Tong Zhu Tonglin |
| description | Combining the intelligent algorithm such as BP neural network and support vector maching (SVM) with traditional chemical method, this paper models the relationship between plant surface color and its pigment. Using the neural network model constructed above, people can figure out the content of plant pigments by getting the corresponding plant surface color information. Compared with the traditional modeling methods, this method can significantly save time and experimental supplies. Furthermore, it is easy to implement because it needn't touch samples and doesn't cause any damage to samples. So this method provides a practical tool for non-destructive measurements of plant pigments and solutions to explore the mystery of plant color. |
| doi_str_mv | 10.1109/ICICTA.2011.361 |
| format | Conference Proceeding |
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Using the neural network model constructed above, people can figure out the content of plant pigments by getting the corresponding plant surface color information. Compared with the traditional modeling methods, this method can significantly save time and experimental supplies. Furthermore, it is easy to implement because it needn't touch samples and doesn't cause any damage to samples. 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Using the neural network model constructed above, people can figure out the content of plant pigments by getting the corresponding plant surface color information. Compared with the traditional modeling methods, this method can significantly save time and experimental supplies. Furthermore, it is easy to implement because it needn't touch samples and doesn't cause any damage to samples. So this method provides a practical tool for non-destructive measurements of plant pigments and solutions to explore the mystery of plant color.</description><subject>Artificial neural networks</subject><subject>BP neural network</subject><subject>Chinese kale stems</subject><subject>Data models</subject><subject>HSV mean</subject><subject>Image color analysis</subject><subject>Machine vision</subject><subject>Pigments</subject><subject>plant pigment</subject><subject>Support vector machine (SVM)</subject><subject>Support vector machines</subject><subject>Training</subject><isbn>1612842895</isbn><isbn>9781612842899</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjE1LwzAchwMy0M2dPXjJF2hN0ubtOIovhalD68XLSNp_tmjWjjRD_PZW9Lk88PDjh9AVJTmlRN_UVV01q5wRSvNC0DM0p4IyVTKl-QzNf7tmRAp1jpbj-EEmhNCS0gv0_jb6foef4BRNmJS-hviJ04Afhw4CTnvALxBM8kM_7v0R22kB0ONNMH3Cr6foTAu4GsIQsek77NOIN353gD5dopkzYYTlvxeoubttqods_XxfV6t15jVJmS6gdbJVgmgtrSSlI4IRJk3nHLVOMiGdZaZjnNvOlrYstJGtVlq1kitJigW6_rv1ALA9Rn8w8XvLJSdKieIHW7pSMw</recordid><startdate>201103</startdate><enddate>201103</enddate><creator>Zeng Qingmao</creator><creator>Zhang Tong</creator><creator>Zhu Tonglin</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201103</creationdate><title>Using Neural Network to Model the Relationship between Plant Surface Color and its Pigment</title><author>Zeng Qingmao ; Zhang Tong ; Zhu Tonglin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-93ecf7c860997b704f062027adff1bf7267fb2ad255bdb4b439a7c9898c758703</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Artificial neural networks</topic><topic>BP neural network</topic><topic>Chinese kale stems</topic><topic>Data models</topic><topic>HSV mean</topic><topic>Image color analysis</topic><topic>Machine vision</topic><topic>Pigments</topic><topic>plant pigment</topic><topic>Support vector machine (SVM)</topic><topic>Support vector machines</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Zeng Qingmao</creatorcontrib><creatorcontrib>Zhang Tong</creatorcontrib><creatorcontrib>Zhu Tonglin</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zeng Qingmao</au><au>Zhang Tong</au><au>Zhu Tonglin</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Using Neural Network to Model the Relationship between Plant Surface Color and its Pigment</atitle><btitle>2011 Fourth International Conference on Intelligent Computation Technology and Automation</btitle><stitle>icicta</stitle><date>2011-03</date><risdate>2011</risdate><volume>2</volume><spage>303</spage><epage>307</epage><pages>303-307</pages><isbn>1612842895</isbn><isbn>9781612842899</isbn><abstract>Combining the intelligent algorithm such as BP neural network and support vector maching (SVM) with traditional chemical method, this paper models the relationship between plant surface color and its pigment. Using the neural network model constructed above, people can figure out the content of plant pigments by getting the corresponding plant surface color information. Compared with the traditional modeling methods, this method can significantly save time and experimental supplies. Furthermore, it is easy to implement because it needn't touch samples and doesn't cause any damage to samples. So this method provides a practical tool for non-destructive measurements of plant pigments and solutions to explore the mystery of plant color.</abstract><pub>IEEE</pub><doi>10.1109/ICICTA.2011.361</doi><tpages>5</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks BP neural network Chinese kale stems Data models HSV mean Image color analysis Machine vision Pigments plant pigment Support vector machine (SVM) Support vector machines Training |
| title | Using Neural Network to Model the Relationship between Plant Surface Color and its Pigment |
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