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dc.contributor.authorAcosta, Maylin
dc.contributor.authorRodríguez-Carretero, Isabel
dc.contributor.authorBlasco, José 
dc.contributor.authorDe-Paz, José M. 
dc.contributor.authorQuinones, Ana 
dc.date.accessioned2023-08-29T11:20:30Z
dc.date.available2023-08-29T11:20:30Z
dc.date.issued2023es
dc.identifier.citationAcosta, M., Rodríguez-Carretero, I., Blasco, J., de-Paz, J. M. & Quiñones, A. (2023). Non-Destructive Appraisal of Macro-and Micronutrients in Persimmon Leaves Using Vis/NIR Hyperspectral Imaging. Agriculture, 13(4), 916.es
dc.identifier.issn2077-0472
dc.identifier.urihttps://hdl.handle.net/20.500.11939/8696
dc.description.abstractVisible and near-infrared (Vis/NIR) hyperspectral imaging (HSI) was used for rapid and non-destructive determination of macro- and micronutrient contents in persimmon leaves. Hyperspectral images of 687 leaves were acquired in the 500–980 nm range over 6 months, covering a complete vegetative cycle. The average reflectance spectrum of each leaf was extracted, and foliar ionomic analysis was used as a reference method to determine the actual concentration of the nutrients in the leaves. Analyses were performed via emission spectrometry (ICP-OES) for macro- and micronutrients after microwave digestion and using the Kjeldahl method to quantify nitrogen. Partial least square regression (PLS-R) was used to predict the nutrient concentration based on spectral data from the leaf using actual values of each element as predictor variables. Several methods were used to pre-process the spectra, including Savitzky–Golay (SG) smoothing, standard normal variate (SNV) and first (1D) and second derivatives (2D). Seventy-five percent of the samples were used to calibrate and validate the model by cross-validation, whereas the remaining twenty-five % were used as an independent test set. The best performance of the models for the test set achieved an R2 = 0.80 for nitrogen. Results were also satisfactory for phosphorous, calcium, magnesium and boron, with determination coefficient R2 values of 0.63, 0.66, 0.58 and 0.69, respectively. For the other nutrients, lower prediction rates were attained (R2 = 0.48 for potassium, R2 = 0.38 for iron, R2 = 0.24 for copper, R2 = 0.23 for zinc and R2 = 0.22 for manganese). The variable importance in projection (VIP) was used to extract the most influential bands for the best-predicted nutrients, which were N, K and B.es
dc.language.isoenes
dc.publisherMDPIes
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectHyperspectral imaginges
dc.subjectVis/NIRes
dc.subjectChemometricses
dc.subjectVariable selectiones
dc.subjectNon-invasive techniqueses
dc.titleNon-Destructive Appraisal of Macro- and Micronutrients in Persimmon Leaves Using Vis/NIR Hyperspectral Imaginges
dc.typearticlees
dc.authorAddressquinones_ana@gva.eses
dc.entidadIVIACentro de Agroingenieríaes
dc.entidadIVIACentro para el Desarrollo de la Agricultura Sosteniblees
dc.identifier.doi10.3390/agriculture13040916es
dc.identifier.urlhttps://www.mdpi.com/2077-0472/13/4/916es
dc.journal.issueNumber4es
dc.journal.titleAgriculturees
dc.journal.volumeNumber13es
dc.page.final916es
dc.page.initial916es
dc.relation.projectIDThis work is co-funded by MICIN-AEI through project TED2021-130117B-C31, GVA-IVIA through projects 52203 and 52204, and the EU through the European Regional Development Fund (ERDF) of the Generalitat Valenciana 2021–2027.es
dc.relation.projectIDinfo:eu-repo/grantAgreement/ERDF/PCV 2021-2027/52203/ES/Sostenibilidad y economía circular como ejes de desarrollo del sector agrario valenciano: suelo, agua y biodiversidad/SostE-SABioes
dc.relation.projectIDinfo:eu-repo/grantAgreement/ERDF/PCV 2021-2027/52204/ES/Tecnología inteligente para una agricultura digital, sostenible y precisa en la comunitat valenciana/AgrIntel·ligència-CVes
dc.relation.projectIDinfo:eu-repo/grantAgreement/MICIN//TED2021-130117B-C31/ES/Smart autonomous electrical robot for a digital and sustainable agriculture in the Valencian Community/AgriSmartRobotes
dc.rights.accessRightsopenAccesses
dc.source.typeelectronicoes
dc.subject.agrisF04 Fertilizinges
dc.subject.agrisF61 Plant physiology - Nutritiones
dc.subject.agrisU30 Research methodses
dc.subject.agrisU40 Surveying methodses
dc.subject.agrisN20 Agricultural machinery and equipmentes
dc.subject.agrovocSpectroscopy es
dc.subject.agrovocMicronutrients es
dc.subject.agrovocMacronutrients es
dc.subject.agrovocDiospyros kaki es
dc.subject.agrovocLeaf analysis es
dc.type.hasVersionpublishedVersiones


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