Sunpheno : a deep neural network for phenological classification of sunflower images

Autores
Bengoa Luoni, Sofía Ailin; Ricci, Riccardo; Corzo, Melanie Anahi; Hoxha, Genc; Melgani, Farid; Fernandez, 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.
Instituto de Biotecnología
Fil: Bengoa Luoni, Sofia Ailin. Wageningen University & Research. Laboratory of Genetics; Países Bajos
Fil: Ricci, Riccardo. University of Trento. Department of Information Engineering and Computer Science; Italia
Fil: Corzo, Melanie Anahi. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Corzo, Melanie Anahi. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Hoxha, Genc. Technische Universität Berlin. Faculty of Electrical Engineering and Computer Science; Alemania
Fil: Melgani, Farid. University of Trento. Department of Information Engineering and Computer Science; Italia
Fil: Fernandez, Paula Del Carmen. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Fernandez, Paula Del Carmen. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fuente
Plants 13 (14) : 1998 (July 2024)
Materia
Phenology
Senescence
Sunflowers
Machine Learning
Fenología
Avejentamiento
Girasol
Aprendizaje Automático
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/18739
Acceder
id INTADig_cd5f9b331ba9c227a3ce8a344cc67cc0
oai_identifier_str oai:localhost:20.500.12123/18739
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling Sunpheno : a deep neural network for phenological classification of sunflower imagesBengoa Luoni, Sofía AilinRicci, RiccardoCorzo, Melanie AnahiHoxha, GencMelgani, FaridFernandez, Paula Del CarmenPhenologySenescenceSunflowersMachine LearningFenologíaAvejentamientoGirasolAprendizaje AutomáticoLeaf 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.Instituto de BiotecnologíaFil: Bengoa Luoni, Sofia Ailin. Wageningen University & Research. Laboratory of Genetics; Países BajosFil: Ricci, Riccardo. University of Trento. Department of Information Engineering and Computer Science; ItaliaFil: Corzo, Melanie Anahi. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; ArgentinaFil: Corzo, Melanie Anahi. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Hoxha, Genc. Technische Universität Berlin. Faculty of Electrical Engineering and Computer Science; AlemaniaFil: Melgani, Farid. University of Trento. Department of Information Engineering and Computer Science; ItaliaFil: Fernandez, Paula Del Carmen. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; ArgentinaFil: Fernandez, Paula Del Carmen. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaMDPI2024-08-01T10:18:50Z2024-08-01T10:18:50Z2024-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/18739https://www.mdpi.com/2223-7747/13/14/19982223-7747https://doi.org/10.3390/plants13141998Plants 13 (14) : 1998 (July 2024)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repograntAgreement/INTA/PNBIO/1131022/AR./Genómica funcional y biología de sistemas.info:eu-repograntAgreement/INTA/PNBIO/1131043/AR./Bioinformática y Estadística Genómica.info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-10-16T09:31:46Zoai:localhost:20.500.12123/18739instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-10-16 09:31:47.132INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
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, Sofía Ailin
Phenology
Senescence
Sunflowers
Machine Learning
Fenología
Avejentamiento
Girasol
Aprendizaje Automático
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, Sofía Ailin
Ricci, Riccardo
Corzo, Melanie Anahi
Hoxha, Genc
Melgani, Farid
Fernandez, Paula Del Carmen
author Bengoa Luoni, Sofía Ailin
author_facet Bengoa Luoni, Sofía Ailin
Ricci, Riccardo
Corzo, Melanie Anahi
Hoxha, Genc
Melgani, Farid
Fernandez, Paula Del Carmen
author_role author
author2 Ricci, Riccardo
Corzo, Melanie Anahi
Hoxha, Genc
Melgani, Farid
Fernandez, Paula Del Carmen
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Phenology
Senescence
Sunflowers
Machine Learning
Fenología
Avejentamiento
Girasol
Aprendizaje Automático
topic Phenology
Senescence
Sunflowers
Machine Learning
Fenología
Avejentamiento
Girasol
Aprendizaje Automático
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.
Instituto de Biotecnología
Fil: Bengoa Luoni, Sofia Ailin. Wageningen University & Research. Laboratory of Genetics; Países Bajos
Fil: Ricci, Riccardo. University of Trento. Department of Information Engineering and Computer Science; Italia
Fil: Corzo, Melanie Anahi. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Corzo, Melanie Anahi. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Hoxha, Genc. Technische Universität Berlin. Faculty of Electrical Engineering and Computer Science; Alemania
Fil: Melgani, Farid. University of Trento. Department of Information Engineering and Computer Science; Italia
Fil: Fernandez, Paula Del Carmen. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular; Argentina
Fil: Fernandez, Paula Del Carmen. Consejo Nacional de Investigaciones Científicas y Técnicas; 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 2024-08-01T10:18:50Z
2024-08-01T10:18:50Z
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/20.500.12123/18739
https://www.mdpi.com/2223-7747/13/14/1998
2223-7747
https://doi.org/10.3390/plants13141998
url http://hdl.handle.net/20.500.12123/18739
https://www.mdpi.com/2223-7747/13/14/1998
https://doi.org/10.3390/plants13141998
identifier_str_mv 2223-7747
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repograntAgreement/INTA/PNBIO/1131022/AR./Genómica funcional y biología de sistemas.
info:eu-repograntAgreement/INTA/PNBIO/1131043/AR./Bioinformática y Estadística Genómica.
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Plants 13 (14) : 1998 (July 2024)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
_version_ 1846143576277254144
score 12.712165

Publicaciones similares