Leaf nitrogen content estimation in olive orchards based on analysis and modelling of UAV-acquired hyperspectral data

Nitrogen (N) management is critical for optimising yield and quality in modern super high-density (SHD) olive orchards while minimising environmental impacts. Traditional leaf nitrogen content (LNC) assessment relies on costly and time-consuming laboratory methods, lacking the resolution for precisi...

Descripción completa

Detalles Bibliográficos
Autores: Argüello Morán, Daniel, Noguera Manzano, Miguel, Vaz Fernández, Federico, Cordeiro, Antonio, Silvestre, José, Aquino Martín, Arturo
Tipo de recurso: artículo
Fecha de publicación:2026
País:España
Institución:Universidad de Huelva (UHU)
Repositorio:Arias Montano. Repositorio Institucional de la Universidad de Huelva
Idioma:inglés
OAI Identifier:oai:dnet:ariasmontano::b4c41b9b50f70a56752e010b931c3270
Acceso en línea:https://hdl.handle.net/10272/28157
Access Level:acceso abierto
Palabra clave:Nitrogen
Olive
Hyperspectral
Computer vision
Neural networks
Precision agriculture
3103.01 Producción de Cultivos
3102 Ingeniería Agrícola
id ES_3fd5ca6fa86efa4e099e1f0677918595
oai_identifier_str oai:dnet:ariasmontano::b4c41b9b50f70a56752e010b931c3270
network_acronym_str ES
network_name_str España
repository_id_str
spelling Leaf nitrogen content estimation in olive orchards based on analysis and modelling of UAV-acquired hyperspectral dataArgüello Morán, DanielNoguera Manzano, MiguelVaz Fernández, FedericoCordeiro, AntonioSilvestre, JoséAquino Martín, ArturoNitrogenOliveHyperspectralComputer visionNeural networksPrecision agriculture3103.01 Producción de Cultivos3102 Ingeniería AgrícolaNitrogen (N) management is critical for optimising yield and quality in modern super high-density (SHD) olive orchards while minimising environmental impacts. Traditional leaf nitrogen content (LNC) assessment relies on costly and time-consuming laboratory methods, lacking the resolution for precision agriculture. This study evaluated the potential of unmanned aerial vehicle (UAV)-based hyperspectral sensing to non-invasively estimate LNC in SHD olive groves.Hyperspectral images (537 bands, 400–2500 nm) were acquired over an experimental orchard containing three olive varieties receiving contrasting N-fertigation treatments. Canopy spectral reflectance was extracted and subjected to preprocessing, including Savitzky-Golay filtering (first and second derivatives), scatter correction (MSC and SNV), and Principal Component Analysis (PCA). Partial Least Squares Regression (PLSR) and Artificial Neural Network (ANN) models were then trained to estimate LNC values derived from chemical analysis. Results from an external validation set showed that the ANN model provided superior performance compared to PLSR. The best-performing ANN model, which utilised normalised information from both spectral derivatives, achieved outstanding predictive accuracy (R2 > 0.8 and RPD = 2.3). This work demonstrates that a non-linear modelling approach leveraging UAV-acquired VNIR-SWIR hyperspectral data is a robust methodology for N status monitoring, offering the high spatial and temporal resolution required for precision fertilisation in modern olive cultivation.Elsevier20262026-01-0120262026-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10272/28157reponame:Arias Montano. Repositorio Institucional de la Universidad de Huelvainstname:Universidad de Huelva (UHU)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:dnet:ariasmontano::b4c41b9b50f70a56752e010b931c32702026-06-02T14:58:11Z
dc.title.none.fl_str_mv Leaf nitrogen content estimation in olive orchards based on analysis and modelling of UAV-acquired hyperspectral data
title Leaf nitrogen content estimation in olive orchards based on analysis and modelling of UAV-acquired hyperspectral data
spellingShingle Leaf nitrogen content estimation in olive orchards based on analysis and modelling of UAV-acquired hyperspectral data
Argüello Morán, Daniel
Nitrogen
Olive
Hyperspectral
Computer vision
Neural networks
Precision agriculture
3103.01 Producción de Cultivos
3102 Ingeniería Agrícola
title_short Leaf nitrogen content estimation in olive orchards based on analysis and modelling of UAV-acquired hyperspectral data
title_full Leaf nitrogen content estimation in olive orchards based on analysis and modelling of UAV-acquired hyperspectral data
title_fullStr Leaf nitrogen content estimation in olive orchards based on analysis and modelling of UAV-acquired hyperspectral data
title_full_unstemmed Leaf nitrogen content estimation in olive orchards based on analysis and modelling of UAV-acquired hyperspectral data
title_sort Leaf nitrogen content estimation in olive orchards based on analysis and modelling of UAV-acquired hyperspectral data
dc.creator.none.fl_str_mv Argüello Morán, Daniel
Noguera Manzano, Miguel
Vaz Fernández, Federico
Cordeiro, Antonio
Silvestre, José
Aquino Martín, Arturo
author Argüello Morán, Daniel
author_facet Argüello Morán, Daniel
Noguera Manzano, Miguel
Vaz Fernández, Federico
Cordeiro, Antonio
Silvestre, José
Aquino Martín, Arturo
author_role author
author2 Noguera Manzano, Miguel
Vaz Fernández, Federico
Cordeiro, Antonio
Silvestre, José
Aquino Martín, Arturo
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv
dc.subject.none.fl_str_mv Nitrogen
Olive
Hyperspectral
Computer vision
Neural networks
Precision agriculture
3103.01 Producción de Cultivos
3102 Ingeniería Agrícola
topic Nitrogen
Olive
Hyperspectral
Computer vision
Neural networks
Precision agriculture
3103.01 Producción de Cultivos
3102 Ingeniería Agrícola
description Nitrogen (N) management is critical for optimising yield and quality in modern super high-density (SHD) olive orchards while minimising environmental impacts. Traditional leaf nitrogen content (LNC) assessment relies on costly and time-consuming laboratory methods, lacking the resolution for precision agriculture. This study evaluated the potential of unmanned aerial vehicle (UAV)-based hyperspectral sensing to non-invasively estimate LNC in SHD olive groves.Hyperspectral images (537 bands, 400–2500 nm) were acquired over an experimental orchard containing three olive varieties receiving contrasting N-fertigation treatments. Canopy spectral reflectance was extracted and subjected to preprocessing, including Savitzky-Golay filtering (first and second derivatives), scatter correction (MSC and SNV), and Principal Component Analysis (PCA). Partial Least Squares Regression (PLSR) and Artificial Neural Network (ANN) models were then trained to estimate LNC values derived from chemical analysis. Results from an external validation set showed that the ANN model provided superior performance compared to PLSR. The best-performing ANN model, which utilised normalised information from both spectral derivatives, achieved outstanding predictive accuracy (R2 > 0.8 and RPD = 2.3). This work demonstrates that a non-linear modelling approach leveraging UAV-acquired VNIR-SWIR hyperspectral data is a robust methodology for N status monitoring, offering the high spatial and temporal resolution required for precision fertilisation in modern olive cultivation.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026-01-01
2026
2026-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10272/28157
url https://hdl.handle.net/10272/28157
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Arias Montano. Repositorio Institucional de la Universidad de Huelva
instname:Universidad de Huelva (UHU)
instname_str Universidad de Huelva (UHU)
reponame_str Arias Montano. Repositorio Institucional de la Universidad de Huelva
collection Arias Montano. Repositorio Institucional de la Universidad de Huelva
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869406687954206720
score 15,81155

Similares en LA Referencia