High-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids
Urochloa grasses are widely used forages in the Neotropics and are gaining importance in other regions due to their role in meeting the increasing global demand for sustainable agricultural practices. High-throughput phenotyping (HTP) is important for accelerating Urochloa breeding programs focused...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Elsevier
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/155305 |
| _version_ | 1855513192039448576 |
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| author | Arrechea-Castillo, Darwin Alexis Espitia-Buitrago, Paula Arboleda, Ronald David Hernández, Luis Miguel Jauregui, Rosa N. Cardoso, Juan Andrés |
| author_browse | Arboleda, Ronald David Arrechea-Castillo, Darwin Alexis Cardoso, Juan Andrés Espitia-Buitrago, Paula Hernández, Luis Miguel Jauregui, Rosa N. |
| author_facet | Arrechea-Castillo, Darwin Alexis Espitia-Buitrago, Paula Arboleda, Ronald David Hernández, Luis Miguel Jauregui, Rosa N. Cardoso, Juan Andrés |
| author_sort | Arrechea-Castillo, Darwin Alexis |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Urochloa grasses are widely used forages in the Neotropics and are gaining importance in other regions due to their role in meeting the increasing global demand for sustainable agricultural practices. High-throughput phenotyping (HTP) is important for accelerating Urochloa breeding programs focused on improving forage and seed yield. While RGB imaging has been used for HTP of vegetative traits, the assessment of phenological stages and seed yield using image analysis remains unexplored in this genus. This work presents a dataset of 2,400 high-resolution RGB images of 200 Urochloa hybrid genotypes, captured over seven months and covering both vegetative and reproductive stages. Images were manually labelled as vegetative or reproductive, and a subset of 255 reproductive stage images were annotated to identify 22,340 individual racemes. This dataset enables the development of machine learning and deep learning models for automated phenological stage classification and raceme identification, facilitating HTP and accelerated breeding of Urochloa spp. hybrids with high seed yield potential. |
| format | Journal Article |
| id | CGSpace155305 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1553052025-11-11T17:38:48Z High-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids Arrechea-Castillo, Darwin Alexis Espitia-Buitrago, Paula Arboleda, Ronald David Hernández, Luis Miguel Jauregui, Rosa N. Cardoso, Juan Andrés machine learning aprendizaje automático artificial intelligence inteligencia artificial high-throughput phenotyping forage species urochloa fenotipado de alto rendimiento forraje racemes racimo Urochloa grasses are widely used forages in the Neotropics and are gaining importance in other regions due to their role in meeting the increasing global demand for sustainable agricultural practices. High-throughput phenotyping (HTP) is important for accelerating Urochloa breeding programs focused on improving forage and seed yield. While RGB imaging has been used for HTP of vegetative traits, the assessment of phenological stages and seed yield using image analysis remains unexplored in this genus. This work presents a dataset of 2,400 high-resolution RGB images of 200 Urochloa hybrid genotypes, captured over seven months and covering both vegetative and reproductive stages. Images were manually labelled as vegetative or reproductive, and a subset of 255 reproductive stage images were annotated to identify 22,340 individual racemes. This dataset enables the development of machine learning and deep learning models for automated phenological stage classification and raceme identification, facilitating HTP and accelerated breeding of Urochloa spp. hybrids with high seed yield potential. 2024-12 2024-10-10T18:06:57Z 2024-10-10T18:06:57Z Journal Article https://hdl.handle.net/10568/155305 en Open Access application/pdf Elsevier Arrechea-Castillo, D.A.; Espitia-Buitrago, P.; Arboleda, R.D.; Hernandez, L.M.; Jauregui, R.N.; Cardoso, J.A. (2024) High-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids. Data in Brief 57: 110928. ISSN: 2352-3409 |
| spellingShingle | machine learning aprendizaje automático artificial intelligence inteligencia artificial high-throughput phenotyping forage species urochloa fenotipado de alto rendimiento forraje racemes racimo Arrechea-Castillo, Darwin Alexis Espitia-Buitrago, Paula Arboleda, Ronald David Hernández, Luis Miguel Jauregui, Rosa N. Cardoso, Juan Andrés High-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids |
| title | High-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids |
| title_full | High-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids |
| title_fullStr | High-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids |
| title_full_unstemmed | High-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids |
| title_short | High-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids |
| title_sort | high resolution image dataset for the automatic classification of phenological stage and identification of racemes in urochloa spp hybrids |
| topic | machine learning aprendizaje automático artificial intelligence inteligencia artificial high-throughput phenotyping forage species urochloa fenotipado de alto rendimiento forraje racemes racimo |
| url | https://hdl.handle.net/10568/155305 |
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