Near-infrared leaf reflectance modeling of Annona emarginata seedlings for early detection of variations in nitrogen concentration

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dc.contributor.authorGomes, Rafaela Lanças [UNESP]
dc.contributor.authorSousa, Marília Caixeta [UNESP]
dc.contributor.authorCampos, Felipe Girotto [UNESP]
dc.contributor.authorBoaro, Carmen Sílvia Fernandes [UNESP]
dc.contributor.authorde Souza Passos, José Raimundo [UNESP]
dc.contributor.authorFerreira, Gisela [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2023-07-29T13:27:46Z
dc.date.available2023-07-29T13:27:46Z
dc.date.issued2023-02-01
dc.description.abstractNitrogen (N) monitoring is essential in nurseries to ensure the production of high-quality seedlings. Near-infrared spectroscopy (NIRS) is an instantaneous, nondestructive method to monitor N. Spectral data such as NIRS can also provide the basis for developing a new vegetation spectral index (VSI). Here, we evaluated whether NIRS combined with statistical modeling can accurately detect early variations in N concentration in leaves of young plants of Annona emarginata and developed a new VSI for this task. Plants were grown in a hydroponics system with 0, 2.75, 5.5 or 11 mM N for 45 days. Then we measured gas exchange, chlorophylla fluorescence, and pigments in leaves; analyzed complete leaf nutrients, and recorded spectral data for leaves at 966 to 1685 nm using NIRS. With a statistical learning approach, the dimensionality of the spectral data was reduced, then models were generated using two classes (N deficiency, N) or four classes (0, 2.75, 5.5, 11 mM N). The best combination of techniques for dimensionality reduction and classification, respectively, was stepwise regression (PROC STEPDISC) and linear discriminant function. It was possible to detect N deficiency in seedlings leaves with 100% precision, and the four N concentrations with 93.55% accuracy before photosynthetic damage to the plant occurred. Thereby, NIRS combined with statistical modeling of multidimensional data is effective for detecting N variations in seedlings leaves of A. emarginata.en
dc.description.affiliationBioestatistic Plant Biology Parasitology and Zoology Department Bioscience Institute (IBB) Universidade Estadual Paulista (UNESP), São Paulo
dc.description.affiliationUnespBioestatistic Plant Biology Parasitology and Zoology Department Bioscience Institute (IBB) Universidade Estadual Paulista (UNESP), São Paulo
dc.format.extent269-282
dc.identifierhttp://dx.doi.org/10.1007/s11676-022-01557-3
dc.identifier.citationJournal of Forestry Research, v. 34, n. 1, p. 269-282, 2023.
dc.identifier.doi10.1007/s11676-022-01557-3
dc.identifier.issn1993-0607
dc.identifier.issn1007-662X
dc.identifier.scopus2-s2.0-85141680342
dc.identifier.urihttp://hdl.handle.net/11449/247855
dc.language.isoeng
dc.relation.ispartofJournal of Forestry Research
dc.sourceScopus
dc.subjectDigital signature
dc.subjectFluorescence of chlorophylla
dc.subjectMineral nutrition of plants
dc.subjectNear-infrared spectroscopy
dc.subjectSpectral vegetation index
dc.subjectStatistical learning
dc.titleNear-infrared leaf reflectance modeling of Annona emarginata seedlings for early detection of variations in nitrogen concentrationen
dc.typeArtigo

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