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

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dc.citation.issue1497
dc.citation.volume15
dc.contributor.authorLyu H
dc.contributor.authorGrafton MC
dc.contributor.authorRamilan T
dc.contributor.authorIrwin M
dc.contributor.authorSandoval - Cruz E
dc.contributor.editorDíaz-Varela, RA
dc.date.available2023-03-08
dc.date.issued2023-03-08
dc.description.confidentialfalse
dc.format.extent? - ? (19)
dc.identifier.citationRemote Sensing, 2023, 15 (1497), pp. ? - ? (19)
dc.identifier.elements-id460095
dc.identifier.harvestedMassey_Dark
dc.identifier.issn2072-4292
dc.languageEnglish
dc.publisherMDPI AG
dc.relation.isPartOfRemote Sensing
dc.rights(c) The author/s CC BY 4.0
dc.subjectspectroradiometer
dc.subjectproximal sensor
dc.subjectvineyard
dc.subjectnutrients
dc.subjectpartial least squares regression
dc.subjectrandom forest regression
dc.subjectsupport vector regression
dc.subject.anzsrc0203 Classical Physics
dc.subject.anzsrc0406 Physical Geography and Environmental Geoscience
dc.subject.anzsrc0909 Geomatic Engineering
dc.titleAssessing the Leaf Blade Nutrient Status of Pinot Noir Using Hyperspectral Reflectance and Machine Learning Models
dc.typeJournal article
pubs.notesNot known
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
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