Tracing pistachio nuts&apos

[EN] Pistachio trees have become a significant global agricultural commodity because their nuts are renowned for their unique flavour and numerous health benefits, contributing to their high demand worldwide. This study explores the application of Hyperspectral Imaging (HSI) and Machine Learning (ML...

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Detalhes bibliográficos
Autores: Martínez-Peña, Raquel, Castillo-Gironés, Salvador, Álvarez, Sara, Vélez, Sergio
Formato: artículo
Fecha de publicación:2024
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/221262
Acesso em linha:https://riunet.upv.es/handle/10251/221262
Access Level:acceso abierto
Palavra-chave:Geographical location
Hyperspectral imaging
Irrigation treatments
Pistacia vera
Traceability
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oai_identifier_str oai:riunet.upv.es:10251/221262
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Tracing pistachio nuts&apos
origin and irrigation practices through hyperspectral imaging
title Tracing pistachio nuts&apos
spellingShingle Tracing pistachio nuts&apos
Martínez-Peña, Raquel
Geographical location
Hyperspectral imaging
Irrigation treatments
Pistacia vera
Traceability
title_short Tracing pistachio nuts&apos
title_full Tracing pistachio nuts&apos
title_fullStr Tracing pistachio nuts&apos
title_full_unstemmed Tracing pistachio nuts&apos
title_sort Tracing pistachio nuts&apos
dc.creator.none.fl_str_mv Martínez-Peña, Raquel
Castillo-Gironés, Salvador
Álvarez, Sara
Vélez, Sergio
author Martínez-Peña, Raquel
author_facet Martínez-Peña, Raquel
Castillo-Gironés, Salvador
Álvarez, Sara
Vélez, Sergio
author_role author
author2 Castillo-Gironés, Salvador
Álvarez, Sara
Vélez, Sergio
author2_role author
author
author
dc.contributor.none.fl_str_mv European Commission
Junta de Castilla y León
Agencia Estatal de Investigación
European Cooperation in Science and Technology
European Agricultural Fund for Rural Development
Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Geographical location
Hyperspectral imaging
Irrigation treatments
Pistacia vera
Traceability
topic Geographical location
Hyperspectral imaging
Irrigation treatments
Pistacia vera
Traceability
description [EN] Pistachio trees have become a significant global agricultural commodity because their nuts are renowned for their unique flavour and numerous health benefits, contributing to their high demand worldwide. This study explores the application of Hyperspectral Imaging (HSI) and Machine Learning (ML) to determine pistachio nuts' geographic origin and irrigation practices, alongside predicting essential commercial quality and yield parameters. The study was conducted in two Spanish orchards and employed HSI technology to capture spectral data. It used ML models like Partial Least Squares (PLS), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) for analysis. The results demonstrated high accuracy in classifying pistachios based on origin, with accuracies exceeding 94%, and in assessing water content and colour pigments, where both PLS and SVM models achieved 99% accuracy. The research highlighted distinct spectral signatures associated with different irrigation treatments, particularly in the Near-Infrared (NIR) region, with PLS showing an accuracy of 92%. However, challenges were noted in predicting fruit orientation, while predicting height location within the tree was more successful, reflecting clearer spectral distinctions. Regression models also showed promise, particularly in predicting yield (R2 = 0.89 with PLS) and percentage of blank nuts (R2 = 0.71 with PLS). The correlation analysis revealed key insights, such as an inverse relationship between blank nuts and yield, and a strong correlation between yield and split nuts. Despite challenges in predicting fruit orientation, the research showed promising results in forecasting yield and commercial quality factors, indicating the effectiveness of spectral analysis in optimising pistachio production and sustainability.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-01-01
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://riunet.upv.es/handle/10251/221262
url https://riunet.upv.es/handle/10251/221262
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission https://doi.org/10.13039/501100000780 H2020 101034297 ENERGY FOR FUTURE
Instituto Nacional de Innovación Agraria, Perú https://doi.org/10.13039/100007652 PRE2020-094491
European Cooperation in Science and Technology https://doi.org/10.13039/501100000921 CA21142
Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 RYC2021-033890
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/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
Reconocimiento (by)
http://creativecommons.org/licenses/by/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:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling Tracing pistachio nuts&aposorigin and irrigation practices through hyperspectral imagingMartínez-Peña, RaquelCastillo-Gironés, SalvadorÁlvarez, SaraVélez, SergioGeographical locationHyperspectral imagingIrrigation treatmentsPistacia veraTraceability[EN] Pistachio trees have become a significant global agricultural commodity because their nuts are renowned for their unique flavour and numerous health benefits, contributing to their high demand worldwide. This study explores the application of Hyperspectral Imaging (HSI) and Machine Learning (ML) to determine pistachio nuts' geographic origin and irrigation practices, alongside predicting essential commercial quality and yield parameters. The study was conducted in two Spanish orchards and employed HSI technology to capture spectral data. It used ML models like Partial Least Squares (PLS), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) for analysis. The results demonstrated high accuracy in classifying pistachios based on origin, with accuracies exceeding 94%, and in assessing water content and colour pigments, where both PLS and SVM models achieved 99% accuracy. The research highlighted distinct spectral signatures associated with different irrigation treatments, particularly in the Near-Infrared (NIR) region, with PLS showing an accuracy of 92%. However, challenges were noted in predicting fruit orientation, while predicting height location within the tree was more successful, reflecting clearer spectral distinctions. Regression models also showed promise, particularly in predicting yield (R2 = 0.89 with PLS) and percentage of blank nuts (R2 = 0.71 with PLS). The correlation analysis revealed key insights, such as an inverse relationship between blank nuts and yield, and a strong correlation between yield and split nuts. Despite challenges in predicting fruit orientation, the research showed promising results in forecasting yield and commercial quality factors, indicating the effectiveness of spectral analysis in optimising pistachio production and sustainability.This work was supported by: -Project CDTI (IDI-20200822) and by MCIN/AEI/10.13039/501100011033 and European Union "NextGenerationEU"//PRTR, grant number RYC2021-033890. Co-financed by FEADER funds and Junta de Castilla y Leon (Spain) . -COST Action CA21142 titled "Fruit tree Crop REsponses to Water deficit and decision support Systems applications (FruitCREWS) ", https :// www.cost.eu/actions/CA21142/. -Salvador Castillo-Girones thanks INIA for the FPI-INIA grant number. PRE2020-094491, partially supported by European Union FSE funds. -Dr. Sergio Velez's contract has been supported by the Iberdrola Foundation and the European Commission under the Marie Sklodowska-Curie Actions (MSCA) -E4F, part of the Horizon 2020 program (Grant Agreement No 101034297, https://doi.org/10.3030/101034297).ElsevierEuropean CommissionJunta de Castilla y LeónAgencia Estatal de InvestigaciónEuropean Cooperation in Science and TechnologyEuropean Agricultural Fund for Rural DevelopmentInstituto Nacional de Investigación y Tecnología Agraria y AlimentariaRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/221262reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengEuropean Commission https://doi.org/10.13039/501100000780 H2020 101034297 ENERGY FOR FUTUREInstituto Nacional de Innovación Agraria, Perú https://doi.org/10.13039/100007652 PRE2020-094491European Cooperation in Science and Technology https://doi.org/10.13039/501100000921 CA21142Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 RYC2021-033890open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2212622026-06-13T07:49:27Z
score 15,81155

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