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Quantifying Nutrient Content in the Leaves of Cowpea Using Remote Sensing

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dc.abstract.enAlthough hyperspectral remote sensing techniques have increasingly been used in the nutritional quantification of plants, it is important to understand whether the method shows a satisfactory response during the various phenological stages of the crop. The aim of this study was to quantify the levels of phosphorus (P), potassium (K), calcium (Ca) and zinc (Zn) in the leaves of Vigna Unguiculata (L.) Walp using spectral data obtained by a spectroradiometer. A randomised block design was used, with three treatments and twenty-five replications. The crop was evaluated at three growth stages: V4, R6 and R9. Single-band models were fitted using simple correlations. For the band ratio models, the wavelengths were selected by 2D correlation. For the models using partial least squares regression (PLSR), the stepwise method was used. The model showing the best fit was used to estimate the phosphorus content in the single-band (R² = 0.62; RMSE = 0.54 and RPD = 1.61), band ratio (R² = 0.66; RMSE = 0.65 and RPD = 1.52) and PLSR models, using data from each of the phenological stages (R² = 0.80; RMSE = 0.47 and RPD = 1.66). Accuracy in modelling leaf nutrients depends on the phenological stage, as well as the amount of data used, and is more accurate with a larger number of samples.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Inżynierii Biosystemów
dc.contributor.authorAmaral, Julyanne Braga Cruz
dc.contributor.authorLopes, Fernando Bezerra
dc.contributor.authorMagalhães, Ana Caroline Messias de
dc.contributor.authorKujawa, Sebastian
dc.contributor.authorTaniguchi, Carlos Alberto Kenji
dc.contributor.authorTeixeira, Adunias dos Santos
dc.contributor.authorLacerda, Claudivan Feitosa de
dc.contributor.authorQueiroz, Thales Rafael Guimarães
dc.contributor.authorAndrade, Eunice Maia de
dc.contributor.authorAraújo, Isabel Cristina da Silva
dc.contributor.authorNiedbała, Gniewko
dc.date.access2026-01-23
dc.date.accessioned2026-01-26T07:11:10Z
dc.date.available2026-01-26T07:11:10Z
dc.date.copyright2022-01-04
dc.date.issued2022
dc.description.abstract<jats:p>Although hyperspectral remote sensing techniques have increasingly been used in the nutritional quantification of plants, it is important to understand whether the method shows a satisfactory response during the various phenological stages of the crop. The aim of this study was to quantify the levels of phosphorus (P), potassium (K), calcium (Ca) and zinc (Zn) in the leaves of Vigna Unguiculata (L.) Walp using spectral data obtained by a spectroradiometer. A randomised block design was used, with three treatments and twenty-five replications. The crop was evaluated at three growth stages: V4, R6 and R9. Single-band models were fitted using simple correlations. For the band ratio models, the wavelengths were selected by 2D correlation. For the models using partial least squares regression (PLSR), the stepwise method was used. The model showing the best fit was used to estimate the phosphorus content in the single-band (R² = 0.62; RMSE = 0.54 and RPD = 1.61), band ratio (R² = 0.66; RMSE = 0.65 and RPD = 1.52) and PLSR models, using data from each of the phenological stages (R² = 0.80; RMSE = 0.47 and RPD = 1.66). Accuracy in modelling leaf nutrients depends on the phenological stage, as well as the amount of data used, and is more accurate with a larger number of samples.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if2,7
dc.description.number1
dc.description.points100
dc.description.versionfinal_published
dc.description.volume12
dc.identifier.doi10.3390/app12010458
dc.identifier.issn2076-3417
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/7111
dc.identifier.weblinkhttps://www.mdpi.com/2076-3417/12/1/458
dc.languageen
dc.relation.ispartofApplied Sciences (Switzerland)
dc.relation.pagesart. 458
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOPEN_JOURNAL
dc.subject.enVigna unguiculata
dc.subject.enhyperspectral data
dc.subject.enevaluating nutritional status
dc.titleQuantifying Nutrient Content in the Leaves of Cowpea Using Remote Sensing
dc.title.volumeSpecial Issue New Development in Smart Farming for Sustainable Agriculture
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue1
oaire.citation.volume12