Using Leaf Based Hyperspectral Models for Monitoring Biochemical Constituents and Plant Phenotyping in Maize

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dc.authoridKahrıman, Fatih/0000-0001-6944-0512
dc.contributor.authorKahriman, F.
dc.contributor.authorDemirel, K.
dc.contributor.authorInalpulat, M.
dc.contributor.authorEgesel, C. O.
dc.contributor.authorGenc, L.
dc.date.accessioned2025-01-27T20:59:51Z
dc.date.available2025-01-27T20:59:51Z
dc.date.issued2016
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractThe aim of this study was to develop and validate qualitative and quantitative models to discriminate different types of maize and also estimate biochemical constituents. Spectral data were taken from the central leaf of randomly-chosen plants grown in field trials in 2011 and 2012. Leaf chlorophyll and protein content and stalk protein content were determined in the same plants. Four different Support Vector Machine (SVM) models were generated and validated in this study. In qualitative models, maize type was designated as dependent variable while Full Spectral (FS) data (400-1,000 nm) and Spectral Indices (SI) data (34 indices/bands) were independent variables. In the two quantitative models (SVMR-FS and SVMR-SI), independent variables were the same, whereas dependent variables were assigned as the quantitatively measured traits. Results showed the qualitative models to be a robust method of classification for distinguishing different maize types, such as High Oil Maize (HOM), High Protein Maize (HPM) and standard (NORMAL) maize genotypes. The SVMC-FS model was superior to SVMC-SI in terms of the genotypic classification of maize plants. Quantitative models with full spectral data gave more robust prediction than the others. The best prediction result (RMSEC=222.4 mu g g(-1), R-2 for Cal=0.739, SEP=213.3 mu g g(-1); RPD=2.04 and r=0.877) was obtained from the SVMR-FS model developed for chlorophyll content. Indirect estimation models, based on relationships between leaf-based spectral measurements and leaf and stalk protein content, were less satisfactory.
dc.identifier.endpage1718
dc.identifier.issn1680-7073
dc.identifier.issn2345-3737
dc.identifier.issue6
dc.identifier.scopus2-s2.0-84988603282
dc.identifier.scopusqualityQ2
dc.identifier.startpage1705
dc.identifier.urihttps://hdl.handle.net/20.500.12428/26857
dc.identifier.volume18
dc.identifier.wosWOS:000384254400023
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTarbiat Modares Univ
dc.relation.ispartofJournal of Agricultural Science and Technology
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20250125
dc.subjectGenotypic classification
dc.subjectSupport Vector Machine
dc.subjectZea mays
dc.titleUsing Leaf Based Hyperspectral Models for Monitoring Biochemical Constituents and Plant Phenotyping in Maize
dc.typeArticle

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