Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification.
Identifieur interne : 000274 ( PubMed/Corpus ); précédent : 000273; suivant : 000275Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification.
Auteurs : Zhan-Yu Liu ; Jing-Jing Shi ; Li-Wen Zhang ; Jing-Feng HuangSource :
- Journal of Zhejiang University. Science. B [ 1862-1783 ] ; 2010.
English descriptors
- KwdEn :
- MESH :
- genetics : Oryza.
- metabolism : Ascomycota.
- methods : Biotechnology.
- microbiology : Oryza.
- Agriculture, Data Interpretation, Statistical, Food Contamination, Genes, Fungal, Principal Component Analysis.
Abstract
Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions: healthy panicles, empty panicles caused by Nilaparvata lugens Stål, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis (PCA) was performed to obtain the principal component spectra (PCS) of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification (SVC) was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the independent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.
DOI: 10.1631/jzus.B0900193
PubMed: 20043354
Links to Exploration step
pubmed:20043354Le document en format XML
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Science. B</title><idno type="eISSN">1862-1783</idno><imprint><date when="2010" type="published">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Agriculture</term><term>Ascomycota (metabolism)</term><term>Biotechnology (methods)</term><term>Data Interpretation, Statistical</term><term>Food Contamination</term><term>Genes, Fungal</term><term>Oryza (genetics)</term><term>Oryza (microbiology)</term><term>Principal Component Analysis</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Oryza</term></keywords><keywords scheme="MESH" qualifier="metabolism" xml:lang="en"><term>Ascomycota</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Biotechnology</term></keywords><keywords scheme="MESH" qualifier="microbiology" xml:lang="en"><term>Oryza</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Agriculture</term><term>Data Interpretation, Statistical</term><term>Food Contamination</term><term>Genes, Fungal</term><term>Principal Component Analysis</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions: healthy panicles, empty panicles caused by Nilaparvata lugens Stål, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis (PCA) was performed to obtain the principal component spectra (PCS) of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification (SVC) was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the independent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">20043354</PMID><DateCreated><Year>2009</Year><Month>12</Month><Day>31</Day></DateCreated><DateCompleted><Year>2010</Year><Month>03</Month><Day>22</Day></DateCompleted><DateRevised><Year>2017</Year><Month>02</Month><Day>20</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1862-1783</ISSN><JournalIssue CitedMedium="Internet"><Volume>11</Volume><Issue>1</Issue><PubDate><Year>2010</Year><Month>Jan</Month></PubDate></JournalIssue><Title>Journal of Zhejiang University. Science. B</Title><ISOAbbreviation>J Zhejiang Univ Sci B</ISOAbbreviation></Journal><ArticleTitle>Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification.</ArticleTitle><Pagination><MedlinePgn>71-8</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1631/jzus.B0900193</ELocationID><Abstract><AbstractText>Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions: healthy panicles, empty panicles caused by Nilaparvata lugens Stål, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis (PCA) was performed to obtain the principal component spectra (PCS) of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification (SVC) was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the independent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Liu</LastName><ForeName>Zhan-yu</ForeName><Initials>ZY</Initials><AffiliationInfo><Affiliation>Institute of Agricultural Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310029, China. zdrsbond@zju.edu.cn</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Shi</LastName><ForeName>Jing-jing</ForeName><Initials>JJ</Initials></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Li-wen</ForeName><Initials>LW</Initials></Author><Author ValidYN="Y"><LastName>Huang</LastName><ForeName>Jing-feng</ForeName><Initials>JF</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>China</Country><MedlineTA>J Zhejiang Univ Sci B</MedlineTA><NlmUniqueID>101236535</NlmUniqueID><ISSNLinking>1673-1581</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Vet Microbiol. 2000 Jan;71(1-2):161-7</RefSource><PMID Version="1">10665543</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Guang Pu Xue Yu Guang Pu Fen Xi. 2008 Sep;28(9):2156-60</RefSource><PMID Version="1">19093583</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Phytopathology. 2001 Mar;91(3):316-23</RefSource><PMID Version="1">18943352</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Annu Rev Phytopathol. 1995;33:489-528</RefSource><PMID Version="1">18999971</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>J Zhejiang Univ Sci B. 2007 Oct;8(10):738-44</RefSource><PMID Version="1">17910117</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName UI="D000383" MajorTopicYN="N">Agriculture</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001203" MajorTopicYN="N">Ascomycota</DescriptorName><QualifierName UI="Q000378" MajorTopicYN="Y">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D001709" MajorTopicYN="N">Biotechnology</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D003627" MajorTopicYN="N">Data Interpretation, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005506" MajorTopicYN="N">Food Contamination</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005800" MajorTopicYN="N">Genes, Fungal</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012275" MajorTopicYN="N">Oryza</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName><QualifierName UI="Q000382" MajorTopicYN="Y">microbiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D025341" MajorTopicYN="N">Principal Component Analysis</DescriptorName></MeshHeading></MeshHeadingList><OtherID Source="NLM">PMC2801092</OtherID></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2010</Year><Month>1</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2010</Year><Month>1</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2010</Year><Month>3</Month><Day>23</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">20043354</ArticleId><ArticleId IdType="doi">10.1631/jzus.B0900193</ArticleId><ArticleId IdType="pmc">PMC2801092</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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