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.

Sunpheno: A Deep Neural Network for Phenological Classification of Sunflower Images / Bengoa Luoni, Sofia A.; Ricci, Riccardo; Corzo, Melanie A.; Hoxha, Genc; Melgani, Farid; Fernandez, Paula. - In: PLANTS. - ISSN 2223-7747. - 2024, 13:14(2024), pp. 199801-199815. [10.3390/plants13141998]

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

Bengoa Luoni, Sofia A.;Ricci, Riccardo;Hoxha, Genc;Melgani, Farid;
2024-01-01

Abstract

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.
2024
14
Bengoa Luoni, Sofia A.; Ricci, Riccardo; Corzo, Melanie A.; Hoxha, Genc; Melgani, Farid; Fernandez, Paula
Sunpheno: A Deep Neural Network for Phenological Classification of Sunflower Images / Bengoa Luoni, Sofia A.; Ricci, Riccardo; Corzo, Melanie A.; Hoxha, Genc; Melgani, Farid; Fernandez, Paula. - In: PLANTS. - ISSN 2223-7747. - 2024, 13:14(2024), pp. 199801-199815. [10.3390/plants13141998]
File in questo prodotto:
File Dimensione Formato  
20024_Plants-Sofia.pdf

accesso aperto

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Creative commons
Dimensione 4.15 MB
Formato Adobe PDF
4.15 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/437975
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
  • OpenAlex ND
social impact