Sunpheno : a deep neural network for phenological classification of sunflower images
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 d...
| Autores principales: | , , , , , |
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| Formato: | info:ar-repo/semantics/artículo |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2024
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12123/18739 https://www.mdpi.com/2223-7747/13/14/1998 https://doi.org/10.3390/plants13141998 |
| _version_ | 1855037908259438592 |
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| author | Bengoa Luoni, Sofía Ailin Ricci, Riccardo Corzo, Melanie Anahi Hoxha, Genc Melgani, Farid Fernandez, Paula Del Carmen |
| author_browse | Bengoa Luoni, Sofía Ailin Corzo, Melanie Anahi Fernandez, Paula Del Carmen Hoxha, Genc Melgani, Farid Ricci, Riccardo |
| author_facet | Bengoa Luoni, Sofía Ailin Ricci, Riccardo Corzo, Melanie Anahi Hoxha, Genc Melgani, Farid Fernandez, Paula Del Carmen |
| author_sort | Bengoa Luoni, Sofía Ailin |
| collection | INTA Digital |
| 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. |
| format | info:ar-repo/semantics/artículo |
| id | INTA18739 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INTA187392024-08-01T10:31:47Z Sunpheno : a deep neural network for phenological classification of sunflower images Bengoa Luoni, Sofía Ailin Ricci, Riccardo Corzo, Melanie Anahi Hoxha, Genc Melgani, Farid Fernandez, Paula Del Carmen Phenology Senescence Sunflowers Machine Learning Fenología Avejentamiento Girasol Aprendizaje Automático 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 2024-08-01T10:18:50Z 2024-08-01T10:18:50Z 2024-07 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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 eng info: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/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf MDPI Plants 13 (14) : 1998 (July 2024) |
| spellingShingle | Phenology Senescence Sunflowers Machine Learning Fenología Avejentamiento Girasol Aprendizaje Automático Bengoa Luoni, Sofía Ailin Ricci, Riccardo Corzo, Melanie Anahi Hoxha, Genc Melgani, Farid Fernandez, Paula Del Carmen Sunpheno : a deep neural network for phenological classification of sunflower images |
| title | 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_short | Sunpheno : a deep neural network for phenological classification of sunflower images |
| title_sort | sunpheno a deep neural network for phenological classification of sunflower images |
| topic | Phenology Senescence Sunflowers Machine Learning Fenología Avejentamiento Girasol Aprendizaje Automático |
| url | http://hdl.handle.net/20.500.12123/18739 https://www.mdpi.com/2223-7747/13/14/1998 https://doi.org/10.3390/plants13141998 |
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