Sunpheno: A Deep Neural Network for Phenological Classification of Sunflower Images

Autores
Bengoa Luoni, Sofia Ailin; Ricci, Riccardo; Corzo, Melanie Anahi; Hoxha, Genc; Melgani, Farid; Fernández, Paula del Carmen
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Leaf senescence is a complex trait which becomes crucial for grain filling because photoassimilates are translocated to the seeds. Therefore, a correct sync between leaf senescence and phenological stages is necessary to obtain increasing yields. In this study, we evaluated the performance of five deep machine-learning methods for the evaluation of the phenological stages of sunflowers using images taken with cell phones in the field. From the analysis, we found that the method based on the pre-trained network resnet50 outperformed the other methods, both in terms of accuracy and velocity. Finally, the model generated, Sunpheno, was used to evaluate the phenological stages of two contrasting lines, B481_6 and R453, during senescence. We observed clear differences in phenological stages, confirming the results obtained in previous studies. A database with 5000 images was generated and was classified by an expert. This is important to end the subjectivity involved in decision making regarding the progression of this trait in the field and could be correlated with performance and senescence parameters that are highly associated with yield increase.
Fil: Bengoa Luoni, Sofia Ailin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Ricci, Riccardo. Universita degli Studi di Trento; Italia
Fil: Corzo, Melanie Anahi. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Hoxha, Genc. Freie Universität Berlin; Alemania
Fil: Melgani, Farid. Universita degli Studi di Trento; Italia
Fil: Fernández, Paula del Carmen. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina
Materia
PHENOLOGY
SENESCENCE
DEEP MACHINE LEARNING
SUNFLOWER
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/265392
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Sunpheno: A Deep Neural Network for Phenological Classification of Sunflower ImagesBengoa Luoni, Sofia AilinRicci, RiccardoCorzo, Melanie AnahiHoxha, GencMelgani, FaridFernández, Paula del CarmenPHENOLOGYSENESCENCEDEEP MACHINE LEARNINGSUNFLOWERhttps://purl.org/becyt/ford/4.4https://purl.org/becyt/ford/4Leaf senescence is a complex trait which becomes crucial for grain filling because photoassimilates are translocated to the seeds. Therefore, a correct sync between leaf senescence and phenological stages is necessary to obtain increasing yields. In this study, we evaluated the performance of five deep machine-learning methods for the evaluation of the phenological stages of sunflowers using images taken with cell phones in the field. From the analysis, we found that the method based on the pre-trained network resnet50 outperformed the other methods, both in terms of accuracy and velocity. Finally, the model generated, Sunpheno, was used to evaluate the phenological stages of two contrasting lines, B481_6 and R453, during senescence. We observed clear differences in phenological stages, confirming the results obtained in previous studies. A database with 5000 images was generated and was classified by an expert. This is important to end the subjectivity involved in decision making regarding the progression of this trait in the field and could be correlated with performance and senescence parameters that are highly associated with yield increase.Fil: Bengoa Luoni, Sofia Ailin. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ricci, Riccardo. Universita degli Studi di Trento; ItaliaFil: Corzo, Melanie Anahi. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; ArgentinaFil: Hoxha, Genc. Freie Universität Berlin; AlemaniaFil: Melgani, Farid. Universita degli Studi di Trento; ItaliaFil: Fernández, Paula del Carmen. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; ArgentinaMDPI2024-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/265392Bengoa Luoni, Sofia Ailin; Ricci, Riccardo; Corzo, Melanie Anahi; Hoxha, Genc; Melgani, Farid; et al.; Sunpheno: A Deep Neural Network for Phenological Classification of Sunflower Images; MDPI; Plants; 13; 14; 7-2024; 1-152223-7747CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2223-7747/13/14/1998info:eu-repo/semantics/altIdentifier/doi/10.3390/plants13141998info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:22:18Zoai:ri.conicet.gov.ar:11336/265392instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:22:18.898CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Sunpheno: A Deep Neural Network for Phenological Classification of Sunflower Images
title Sunpheno: A Deep Neural Network for Phenological Classification of Sunflower Images
spellingShingle Sunpheno: A Deep Neural Network for Phenological Classification of Sunflower Images
Bengoa Luoni, Sofia Ailin
PHENOLOGY
SENESCENCE
DEEP MACHINE LEARNING
SUNFLOWER
title_short Sunpheno: A Deep Neural Network for Phenological Classification of Sunflower Images
title_full Sunpheno: A Deep Neural Network for Phenological Classification of Sunflower Images
title_fullStr Sunpheno: A Deep Neural Network for Phenological Classification of Sunflower Images
title_full_unstemmed Sunpheno: A Deep Neural Network for Phenological Classification of Sunflower Images
title_sort Sunpheno: A Deep Neural Network for Phenological Classification of Sunflower Images
dc.creator.none.fl_str_mv Bengoa Luoni, Sofia Ailin
Ricci, Riccardo
Corzo, Melanie Anahi
Hoxha, Genc
Melgani, Farid
Fernández, Paula del Carmen
author Bengoa Luoni, Sofia Ailin
author_facet Bengoa Luoni, Sofia Ailin
Ricci, Riccardo
Corzo, Melanie Anahi
Hoxha, Genc
Melgani, Farid
Fernández, Paula del Carmen
author_role author
author2 Ricci, Riccardo
Corzo, Melanie Anahi
Hoxha, Genc
Melgani, Farid
Fernández, Paula del Carmen
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv PHENOLOGY
SENESCENCE
DEEP MACHINE LEARNING
SUNFLOWER
topic PHENOLOGY
SENESCENCE
DEEP MACHINE LEARNING
SUNFLOWER
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.4
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv Leaf senescence is a complex trait which becomes crucial for grain filling because photoassimilates are translocated to the seeds. Therefore, a correct sync between leaf senescence and phenological stages is necessary to obtain increasing yields. In this study, we evaluated the performance of five deep machine-learning methods for the evaluation of the phenological stages of sunflowers using images taken with cell phones in the field. From the analysis, we found that the method based on the pre-trained network resnet50 outperformed the other methods, both in terms of accuracy and velocity. Finally, the model generated, Sunpheno, was used to evaluate the phenological stages of two contrasting lines, B481_6 and R453, during senescence. We observed clear differences in phenological stages, confirming the results obtained in previous studies. A database with 5000 images was generated and was classified by an expert. This is important to end the subjectivity involved in decision making regarding the progression of this trait in the field and could be correlated with performance and senescence parameters that are highly associated with yield increase.
Fil: Bengoa Luoni, Sofia Ailin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Ricci, Riccardo. Universita degli Studi di Trento; Italia
Fil: Corzo, Melanie Anahi. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Hoxha, Genc. Freie Universität Berlin; Alemania
Fil: Melgani, Farid. Universita degli Studi di Trento; Italia
Fil: Fernández, Paula del Carmen. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Agrobiotecnología y Biología Molecular. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Agrobiotecnología y Biología Molecular; Argentina
description Leaf senescence is a complex trait which becomes crucial for grain filling because photoassimilates are translocated to the seeds. Therefore, a correct sync between leaf senescence and phenological stages is necessary to obtain increasing yields. In this study, we evaluated the performance of five deep machine-learning methods for the evaluation of the phenological stages of sunflowers using images taken with cell phones in the field. From the analysis, we found that the method based on the pre-trained network resnet50 outperformed the other methods, both in terms of accuracy and velocity. Finally, the model generated, Sunpheno, was used to evaluate the phenological stages of two contrasting lines, B481_6 and R453, during senescence. We observed clear differences in phenological stages, confirming the results obtained in previous studies. A database with 5000 images was generated and was classified by an expert. This is important to end the subjectivity involved in decision making regarding the progression of this trait in the field and could be correlated with performance and senescence parameters that are highly associated with yield increase.
publishDate 2024
dc.date.none.fl_str_mv 2024-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/265392
Bengoa Luoni, Sofia Ailin; Ricci, Riccardo; Corzo, Melanie Anahi; Hoxha, Genc; Melgani, Farid; et al.; Sunpheno: A Deep Neural Network for Phenological Classification of Sunflower Images; MDPI; Plants; 13; 14; 7-2024; 1-15
2223-7747
CONICET Digital
CONICET
url http://hdl.handle.net/11336/265392
identifier_str_mv Bengoa Luoni, Sofia Ailin; Ricci, Riccardo; Corzo, Melanie Anahi; Hoxha, Genc; Melgani, Farid; et al.; Sunpheno: A Deep Neural Network for Phenological Classification of Sunflower Images; MDPI; Plants; 13; 14; 7-2024; 1-15
2223-7747
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2223-7747/13/14/1998
info:eu-repo/semantics/altIdentifier/doi/10.3390/plants13141998
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846082621528866816
score 13.22299

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