BrRacemeCounter: An AI-based desktop tool for counting racemes in Urochloa spp.
Seed yield prediction in forage plants involves the detection and counting of individual racemes that comprise an inflorescence. However, this task is labor-intensive to perform manually across large numbers of plants and overly complex for classical machine learning techniques due to challenges suc...
| Autores principales: | , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/174538 |
| _version_ | 1855521539342991360 |
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| author | Arrechea-Castillo, Darwin Alexis Espitia-Buitrago, Paula Arboleda, Ronald David Gallego-Muñoz, Ana Marcela Moreno-Domínguez, Valeria Gaviria-Valencia, Juan Manuel Bravo, Valeria Andrea Ruiz-Hurtado, Andres Felipe Jauregui, Rosa Noemi Cardoso, Juan Andres |
| author_browse | Arboleda, Ronald David Arrechea-Castillo, Darwin Alexis Bravo, Valeria Andrea Cardoso, Juan Andres Espitia-Buitrago, Paula Gallego-Muñoz, Ana Marcela Gaviria-Valencia, Juan Manuel Jauregui, Rosa Noemi Moreno-Domínguez, Valeria Ruiz-Hurtado, Andres Felipe |
| author_facet | Arrechea-Castillo, Darwin Alexis Espitia-Buitrago, Paula Arboleda, Ronald David Gallego-Muñoz, Ana Marcela Moreno-Domínguez, Valeria Gaviria-Valencia, Juan Manuel Bravo, Valeria Andrea Ruiz-Hurtado, Andres Felipe Jauregui, Rosa Noemi Cardoso, Juan Andres |
| author_sort | Arrechea-Castillo, Darwin Alexis |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Seed yield prediction in forage plants involves the detection and counting of individual racemes that comprise an inflorescence. However, this task is labor-intensive to perform manually across large numbers of plants and overly complex for classical machine learning techniques due to challenges such as high raceme overlap, large variations in raceme numbers per image and spectral signature similarities between the racemes and the vegetative parts of the plant. To address these challenges, a deep learning-based desktop tool was implemented to count individual racemes in RGB images of Urochloa genotypes, showing different phenological stages and wide variation in number of racemes per plant. |
| format | Journal Article |
| id | CGSpace174538 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1745382025-11-11T17:46:55Z BrRacemeCounter: An AI-based desktop tool for counting racemes in Urochloa spp. Arrechea-Castillo, Darwin Alexis Espitia-Buitrago, Paula Arboleda, Ronald David Gallego-Muñoz, Ana Marcela Moreno-Domínguez, Valeria Gaviria-Valencia, Juan Manuel Bravo, Valeria Andrea Ruiz-Hurtado, Andres Felipe Jauregui, Rosa Noemi Cardoso, Juan Andres machine learning aprendizaje automático forage flowering detection imagery-computer vision imagen-visión por ordenador forraje floración detección Seed yield prediction in forage plants involves the detection and counting of individual racemes that comprise an inflorescence. However, this task is labor-intensive to perform manually across large numbers of plants and overly complex for classical machine learning techniques due to challenges such as high raceme overlap, large variations in raceme numbers per image and spectral signature similarities between the racemes and the vegetative parts of the plant. To address these challenges, a deep learning-based desktop tool was implemented to count individual racemes in RGB images of Urochloa genotypes, showing different phenological stages and wide variation in number of racemes per plant. 2025-05 2025-05-13T09:23:35Z 2025-05-13T09:23:35Z Journal Article https://hdl.handle.net/10568/174538 en Open Access application/pdf Elsevier Arrechea-Castillo, D.A.; Espitia-Buitrago, P.; Arboleda, R.D.; Gallego-Muñoz, A.M.; Moreno-Domínguez, V.; Gaviria-Valencia, J.M.; Bravo, V.A.; Ruiz-Hurtado, A.F.; Jauregui, R.N.; Cardoso, J.A. (2025) BrRacemeCounter: An AI-based desktop tool for counting racemes in Urochloa spp.. SoftwareX 30: 102181. ISSN: 2352-7110 |
| spellingShingle | machine learning aprendizaje automático forage flowering detection imagery-computer vision imagen-visión por ordenador forraje floración detección Arrechea-Castillo, Darwin Alexis Espitia-Buitrago, Paula Arboleda, Ronald David Gallego-Muñoz, Ana Marcela Moreno-Domínguez, Valeria Gaviria-Valencia, Juan Manuel Bravo, Valeria Andrea Ruiz-Hurtado, Andres Felipe Jauregui, Rosa Noemi Cardoso, Juan Andres BrRacemeCounter: An AI-based desktop tool for counting racemes in Urochloa spp. |
| title | BrRacemeCounter: An AI-based desktop tool for counting racemes in Urochloa spp. |
| title_full | BrRacemeCounter: An AI-based desktop tool for counting racemes in Urochloa spp. |
| title_fullStr | BrRacemeCounter: An AI-based desktop tool for counting racemes in Urochloa spp. |
| title_full_unstemmed | BrRacemeCounter: An AI-based desktop tool for counting racemes in Urochloa spp. |
| title_short | BrRacemeCounter: An AI-based desktop tool for counting racemes in Urochloa spp. |
| title_sort | brracemecounter an ai based desktop tool for counting racemes in urochloa spp |
| topic | machine learning aprendizaje automático forage flowering detection imagery-computer vision imagen-visión por ordenador forraje floración detección |
| url | https://hdl.handle.net/10568/174538 |
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