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Assessing the Leaf Blade Nutrient Status of Pinot Noir Using Hyperspectral Reflectance and Machine Learning Models

dc.contributor.authorLyu, Hen_US
dc.contributor.authorGrafton, MCen_US
dc.contributor.authorRamilan, Ten_US
dc.contributor.authorIrwin, Men_US
dc.contributor.authorSandoval - Cruz, Een_US
dc.contributor.editorDíaz-Varela, RAen_US
dc.date.accessioned2023-03-08T22:24:46Z
dc.date.accessioned2023-03-09T18:31:20Z
dc.date.available2023-03-08en_US
dc.date.available2023-03-08T22:24:46Z
dc.date.available2023-03-09T18:31:20Z
dc.date.issued2023-03-08en_US
dc.identifier.citationRemote Sensing, 2023, 15 (1497),(19)en_US
dc.identifier.issn2072-4292en_US
dc.identifier.urihttp://hdl.handle.net/10179/18079
dc.format.extent? - ? (19)en_US
dc.languageEnglishen_US
dc.publisherMDPI AGen_US
dc.rights(c) The author/s CC BY 4.0en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectspectroradiometeren_US
dc.subjectproximal sensoren_US
dc.subjectvineyarden_US
dc.subjectnutrientsen_US
dc.subjectpartial least squares regressionen_US
dc.subjectrandom forest regressionen_US
dc.subjectsupport vector regressionen_US
dc.titleAssessing the Leaf Blade Nutrient Status of Pinot Noir Using Hyperspectral Reflectance and Machine Learning Modelsen_US
dc.typeJournal Article
dc.citation.volume15en_US
dc.description.confidentialfalseen_US
dc.identifier.elements-id460095
dc.relation.isPartOfRemote Sensingen_US
dc.citation.issue1497en_US
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Agriculture & Environment
dc.identifier.harvestedMassey_Dark
pubs.notesNot knownen_US
dc.subject.anzsrc0203 Classical Physicsen_US
dc.subject.anzsrc0406 Physical Geography and Environmental Geoscienceen_US
dc.subject.anzsrc0909 Geomatic Engineeringen_US


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