Fruit weight estimation using image-based deep learning for use in dietary assessment

One of the key limitations of current dietary assessment methodologies is to adjust the food portion size. In this study, we propose a Deep Learning (DL) approach to estimate the weight of individual pieces of commonly consumed fruits (apple, pear, orange and banana) from single-view RGB (Red, Green...

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Autores: Izquierdo González, Pablo, Aragón Espinosa, Patricia, Relaño de la Guía, Edgard, Cobo Cano, Miriam, Heredia, Ignacio, García Díaz, Daniel, Aguilar, Fernando, Lloret Iglesias, Lara, Yuste, Silvia, Íñiguez, María, Pérez-Matute, Patricia, Moreno-Arribas, M. Victoria, Bartolomé, Begoña, Motilva, María-José
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/406317
Acceso en línea:http://hdl.handle.net/10261/406317
Access Level:acceso abierto
Palabra clave:Artificial intelligence
Fruit weight estimation
Deep learning
Convolutional neural network
Image recognition
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oai_identifier_str oai:digital.csic.es:10261/406317
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Fruit weight estimation using image-based deep learning for use in dietary assessment
title Fruit weight estimation using image-based deep learning for use in dietary assessment
spellingShingle Fruit weight estimation using image-based deep learning for use in dietary assessment
Izquierdo González, Pablo
Artificial intelligence
Fruit weight estimation
Deep learning
Convolutional neural network
Image recognition
title_short Fruit weight estimation using image-based deep learning for use in dietary assessment
title_full Fruit weight estimation using image-based deep learning for use in dietary assessment
title_fullStr Fruit weight estimation using image-based deep learning for use in dietary assessment
title_full_unstemmed Fruit weight estimation using image-based deep learning for use in dietary assessment
title_sort Fruit weight estimation using image-based deep learning for use in dietary assessment
dc.creator.none.fl_str_mv Izquierdo González, Pablo
Aragón Espinosa, Patricia
Relaño de la Guía, Edgard
Cobo Cano, Miriam
Heredia, Ignacio
García Díaz, Daniel
Aguilar, Fernando
Lloret Iglesias, Lara
Yuste, Silvia
Íñiguez, María
Pérez-Matute, Patricia
Moreno-Arribas, M. Victoria
Bartolomé, Begoña
Motilva, María-José
author Izquierdo González, Pablo
author_facet Izquierdo González, Pablo
Aragón Espinosa, Patricia
Relaño de la Guía, Edgard
Cobo Cano, Miriam
Heredia, Ignacio
García Díaz, Daniel
Aguilar, Fernando
Lloret Iglesias, Lara
Yuste, Silvia
Íñiguez, María
Pérez-Matute, Patricia
Moreno-Arribas, M. Victoria
Bartolomé, Begoña
Motilva, María-José
author_role author
author2 Aragón Espinosa, Patricia
Relaño de la Guía, Edgard
Cobo Cano, Miriam
Heredia, Ignacio
García Díaz, Daniel
Aguilar, Fernando
Lloret Iglesias, Lara
Yuste, Silvia
Íñiguez, María
Pérez-Matute, Patricia
Moreno-Arribas, M. Victoria
Bartolomé, Begoña
Motilva, María-José
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación (España)
Ministerio de Ciencia, Innovación y Universidades (España)
European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Artificial intelligence
Fruit weight estimation
Deep learning
Convolutional neural network
Image recognition
topic Artificial intelligence
Fruit weight estimation
Deep learning
Convolutional neural network
Image recognition
description One of the key limitations of current dietary assessment methodologies is to adjust the food portion size. In this study, we propose a Deep Learning (DL) approach to estimate the weight of individual pieces of commonly consumed fruits (apple, pear, orange and banana) from single-view RGB (Red, Green and Blue) photographs. The DL models developed in this study were based on convolutional neural networks trained on an ad-hoc dataset of 48,960 photographs including a wide and representative range of fruit piece weights, reflecting typical market variability and reducing the likelihood of encountering out-of-range samples. The photographs of apples (n=12,960), pears (n=17,208), oranges (n=8,712) and bananas (n=10,080) were taken under different conditions. The DL models were evaluated in terms of saliency maps and regression metrics. Mean Absolute Error (MAE) values indicated that the measurements in the DL models developed would be out —as a mean— by no more than 20.57 g (apple), 19.25 g (pear), 28.31 g (orange) and 21.93 g (bananas) between the predicted and the observed values, quite acceptable considering the variability in fruit weights and photographic conditions. The four DL models developed in this study predict fruit weight from a simple photograph, requiring only a visible €1 coin as a reference, without considering the background. This approach is feasible because our DL models were trained on diverse images across different angles, distances, lighting conditions, tablecloths and dish types, allowing the models to generalise well in real-world contexts.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/406317
url http://hdl.handle.net/10261/406317
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
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info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PDC2022-133861-C21
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PDC2022-133861-C22
Motilva, María-José; Bartolomé, Begoña; Moreno-Arribas, M. Victoria; Izquierdo González, Pablo; Aragón Espinosa, Patricia; Relaño de la Guía, Edgard; Cobo Cano, Miriam; Heredia, Ignacio; García Díaz, Daniel; Aguilar, Fernando; Lloret Iglesias, Lara; Yuste, Silvia; Íñiguez, María; Pérez-Matute, Patricia; 2025; IntegrALIMENTA Apple fruit laboratory images [Dataset]; DIGITAL.CSIC; https://doi.org/10.20350/digitalCSIC/17503
Motilva, María-José; Bartolomé, Begoña; Moreno-Arribas, M. Victoria; Izquierdo González, Pablo; Aragón Espinosa, Patricia; Relaño de la Guía, Edgard; Cobo Cano, Miriam; Heredia, Ignacio; García Díaz, Daniel; Aguilar, Fernando; Lloret Iglesias, Lara; Yuste, Silvia; Íñiguez, María; Pérez-Matute, Patricia; 2025; IntegrALIMENTA Pear fruit laboratory images [Dataset]; DIGITAL.CSIC; https://doi.org/10.20350/digitalCSIC/17506
Motilva, María-José; Bartolomé, Begoña; Moreno-Arribas, M. Victoria; Izquierdo González, Pablo; Aragón Espinosa, Patricia; Relaño de la Guía, Edgard; Cobo Cano, Miriam; Heredia, Ignacio; García Díaz, Daniel; Aguilar, Fernando; Lloret Iglesias, Lara; Yuste, Silvia; Íñiguez, María; Pérez-Matute, Patricia; 2025; IntegrALIMENTA Orange fruit laboratory images [Dataset]; DIGITAL.CSIC; https://doi.org/10.20350/digitalCSIC/17507
Motilva, María-José; Bartolomé, Begoña; Moreno-Arribas, M. Victoria; Izquierdo González, Pablo; Aragón Espinosa, Patricia; Relaño de la Guía, Edgard; Cobo Cano, Miriam; Heredia, Ignacio; García Díaz, Daniel; Aguilar, Fernando; Lloret Iglesias, Lara; Yuste, Silvia; Íñiguez, María; Pérez-Matute, Patricia; 2025; IntegrALIMENTA Banana fruit laboratory images [Dataset]; DIGITAL.CSIC; https://doi.org/10.20350/digitalCSIC/17508
https://doi.org/10.1016/j.nexres.2025.101069
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
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spelling Fruit weight estimation using image-based deep learning for use in dietary assessmentIzquierdo González, PabloAragón Espinosa, PatriciaRelaño de la Guía, EdgardCobo Cano, MiriamHeredia, IgnacioGarcía Díaz, DanielAguilar, FernandoLloret Iglesias, LaraYuste, SilviaÍñiguez, MaríaPérez-Matute, PatriciaMoreno-Arribas, M. VictoriaBartolomé, BegoñaMotilva, María-JoséArtificial intelligenceFruit weight estimationDeep learningConvolutional neural networkImage recognitionOne of the key limitations of current dietary assessment methodologies is to adjust the food portion size. In this study, we propose a Deep Learning (DL) approach to estimate the weight of individual pieces of commonly consumed fruits (apple, pear, orange and banana) from single-view RGB (Red, Green and Blue) photographs. The DL models developed in this study were based on convolutional neural networks trained on an ad-hoc dataset of 48,960 photographs including a wide and representative range of fruit piece weights, reflecting typical market variability and reducing the likelihood of encountering out-of-range samples. The photographs of apples (n=12,960), pears (n=17,208), oranges (n=8,712) and bananas (n=10,080) were taken under different conditions. The DL models were evaluated in terms of saliency maps and regression metrics. Mean Absolute Error (MAE) values indicated that the measurements in the DL models developed would be out —as a mean— by no more than 20.57 g (apple), 19.25 g (pear), 28.31 g (orange) and 21.93 g (bananas) between the predicted and the observed values, quite acceptable considering the variability in fruit weights and photographic conditions. The four DL models developed in this study predict fruit weight from a simple photograph, requiring only a visible €1 coin as a reference, without considering the background. This approach is feasible because our DL models were trained on diverse images across different angles, distances, lighting conditions, tablecloths and dish types, allowing the models to generalise well in real-world contexts.This study was supported by the MCIN (Ministerio Español de Ciencia e Innovación)/AEI (Agencia Estatal de Investigación)/10.13039/501100011033 and the European Union NextGenerationEU/PRTR through the projects ‘Prueba de Concepto’ PDC2022–133861-C21 and PDC2022–133861-C22.Peer reviewedElsevierAgencia Estatal de Investigación (España)Ministerio de Ciencia, Innovación y Universidades (España)European CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/406317reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PDC2022-133861-C21info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PDC2022-133861-C22Motilva, María-José; Bartolomé, Begoña; Moreno-Arribas, M. Victoria; Izquierdo González, Pablo; Aragón Espinosa, Patricia; Relaño de la Guía, Edgard; Cobo Cano, Miriam; Heredia, Ignacio; García Díaz, Daniel; Aguilar, Fernando; Lloret Iglesias, Lara; Yuste, Silvia; Íñiguez, María; Pérez-Matute, Patricia; 2025; IntegrALIMENTA Apple fruit laboratory images [Dataset]; DIGITAL.CSIC; https://doi.org/10.20350/digitalCSIC/17503Motilva, María-José; Bartolomé, Begoña; Moreno-Arribas, M. Victoria; Izquierdo González, Pablo; Aragón Espinosa, Patricia; Relaño de la Guía, Edgard; Cobo Cano, Miriam; Heredia, Ignacio; García Díaz, Daniel; Aguilar, Fernando; Lloret Iglesias, Lara; Yuste, Silvia; Íñiguez, María; Pérez-Matute, Patricia; 2025; IntegrALIMENTA Pear fruit laboratory images [Dataset]; DIGITAL.CSIC; https://doi.org/10.20350/digitalCSIC/17506Motilva, María-José; Bartolomé, Begoña; Moreno-Arribas, M. Victoria; Izquierdo González, Pablo; Aragón Espinosa, Patricia; Relaño de la Guía, Edgard; Cobo Cano, Miriam; Heredia, Ignacio; García Díaz, Daniel; Aguilar, Fernando; Lloret Iglesias, Lara; Yuste, Silvia; Íñiguez, María; Pérez-Matute, Patricia; 2025; IntegrALIMENTA Orange fruit laboratory images [Dataset]; DIGITAL.CSIC; https://doi.org/10.20350/digitalCSIC/17507Motilva, María-José; Bartolomé, Begoña; Moreno-Arribas, M. Victoria; Izquierdo González, Pablo; Aragón Espinosa, Patricia; Relaño de la Guía, Edgard; Cobo Cano, Miriam; Heredia, Ignacio; García Díaz, Daniel; Aguilar, Fernando; Lloret Iglesias, Lara; Yuste, Silvia; Íñiguez, María; Pérez-Matute, Patricia; 2025; IntegrALIMENTA Banana fruit laboratory images [Dataset]; DIGITAL.CSIC; https://doi.org/10.20350/digitalCSIC/17508https://doi.org/10.1016/j.nexres.2025.101069Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4063172026-05-22T06:33:51Z
score 15,811543

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