Updating high-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids with expanded images and annotations
This dataset is an expanded version of a previously published collection of high-resolution RGB images of Urochloa spp. genotypes, initially designed to facilitate automated classification of phenological stages and raceme identification in forage breeding trials. The original dataset included 2400...
| Main Authors: | , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Elsevier
2025
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/174759 |
| _version_ | 1855525087857344512 |
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| author | Arrechea-Castillo, Darwin Alexis Espitia-Buitrago, Paula Florian-Vargas, David Estupinan, Ronald David Velázquez-Hernández, Riquelmer Ruiz-Hurtado, Andres Felipe Hernandez, Luis Miguel Jauregui, Rosa Noemi Cardoso, Juan Andres |
| author_browse | Arrechea-Castillo, Darwin Alexis Cardoso, Juan Andres Espitia-Buitrago, Paula Estupinan, Ronald David Florian-Vargas, David Hernandez, Luis Miguel Jauregui, Rosa Noemi Ruiz-Hurtado, Andres Felipe Velázquez-Hernández, Riquelmer |
| author_facet | Arrechea-Castillo, Darwin Alexis Espitia-Buitrago, Paula Florian-Vargas, David Estupinan, Ronald David Velázquez-Hernández, Riquelmer Ruiz-Hurtado, Andres Felipe Hernandez, Luis Miguel Jauregui, Rosa Noemi Cardoso, Juan Andres |
| author_sort | Arrechea-Castillo, Darwin Alexis |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This dataset is an expanded version of a previously published collection of high-resolution RGB images of Urochloa spp. genotypes, initially designed to facilitate automated classification of phenological stages and raceme identification in forage breeding trials. The original dataset included 2400 images of 200 genotypes captured under controlled conditions, supporting the development of computer vision models for High-Throughput Phenotyping (HTP). In this updated release, 139 additional images and 24,983 new annotations have been added, bringing the dataset to a total of 2539 images and 47,323 raceme annotations. This version introduces increased diversity in image-capture conditions, with data collected from two geographic locations (Palmira, Colombia, and Ocozocoautla de Espinosa, Mexico) and a range of image-capture devices, including smartphones (e.g. Realme C53 and Oppo Reno 11), a Nikon D5600 camera, and a Phantom 4 Pro V2 drone. Images now vary in perspective (nadir, high-angle, and frontal) and capture distance (1–3 meters), enhancing the dataset applicability for robust Deep Learning (DL) models. Compared to the original dataset, raceme density per plant has nearly doubled in some samples, offering higher raceme overlap for advanced instance segmentation tasks. This expanded dataset supports deeper exploration of phenotypic variation in Urochloa spp. and offers greater potential for developing adaptable models in crop phenotyping. |
| format | Journal Article |
| id | CGSpace174759 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1747592025-11-11T18:48:49Z Updating high-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids with expanded images and annotations Arrechea-Castillo, Darwin Alexis Espitia-Buitrago, Paula Florian-Vargas, David Estupinan, Ronald David Velázquez-Hernández, Riquelmer Ruiz-Hurtado, Andres Felipe Hernandez, Luis Miguel Jauregui, Rosa Noemi Cardoso, Juan Andres machine learning aprendizaje automático artificial intelligence forage inteligencia artificial grasses high-throughput phenotyping urochloa fenotipado de alto rendimiento imagery-computer vision imagen-visión por ordenador forraje datasets This dataset is an expanded version of a previously published collection of high-resolution RGB images of Urochloa spp. genotypes, initially designed to facilitate automated classification of phenological stages and raceme identification in forage breeding trials. The original dataset included 2400 images of 200 genotypes captured under controlled conditions, supporting the development of computer vision models for High-Throughput Phenotyping (HTP). In this updated release, 139 additional images and 24,983 new annotations have been added, bringing the dataset to a total of 2539 images and 47,323 raceme annotations. This version introduces increased diversity in image-capture conditions, with data collected from two geographic locations (Palmira, Colombia, and Ocozocoautla de Espinosa, Mexico) and a range of image-capture devices, including smartphones (e.g. Realme C53 and Oppo Reno 11), a Nikon D5600 camera, and a Phantom 4 Pro V2 drone. Images now vary in perspective (nadir, high-angle, and frontal) and capture distance (1–3 meters), enhancing the dataset applicability for robust Deep Learning (DL) models. Compared to the original dataset, raceme density per plant has nearly doubled in some samples, offering higher raceme overlap for advanced instance segmentation tasks. This expanded dataset supports deeper exploration of phenotypic variation in Urochloa spp. and offers greater potential for developing adaptable models in crop phenotyping. 2025-06 2025-05-21T14:55:09Z 2025-05-21T14:55:09Z Journal Article https://hdl.handle.net/10568/174759 en Open Access application/pdf Elsevier Arrechea-Castillo, D.A.; Espitia-Buitrago, P.; Florian-Vargas, D.; Estupinan, R.D.; Velázquez-Hernández, R.; Ruiz-Hurtado, A.F.; Hernandez, L.M.; Jauregui, R.N.; Cardoso, J.A. (2025) Updating high-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids with expanded images and annotations. Data in Brief 60: 111593. ISSN: 2352-3409 |
| spellingShingle | machine learning aprendizaje automático artificial intelligence forage inteligencia artificial grasses high-throughput phenotyping urochloa fenotipado de alto rendimiento imagery-computer vision imagen-visión por ordenador forraje datasets Arrechea-Castillo, Darwin Alexis Espitia-Buitrago, Paula Florian-Vargas, David Estupinan, Ronald David Velázquez-Hernández, Riquelmer Ruiz-Hurtado, Andres Felipe Hernandez, Luis Miguel Jauregui, Rosa Noemi Cardoso, Juan Andres Updating high-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids with expanded images and annotations |
| title | Updating high-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids with expanded images and annotations |
| title_full | Updating high-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids with expanded images and annotations |
| title_fullStr | Updating high-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids with expanded images and annotations |
| title_full_unstemmed | Updating high-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids with expanded images and annotations |
| title_short | Updating high-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids with expanded images and annotations |
| title_sort | updating high resolution image dataset for the automatic classification of phenological stage and identification of racemes in urochloa spp hybrids with expanded images and annotations |
| topic | machine learning aprendizaje automático artificial intelligence forage inteligencia artificial grasses high-throughput phenotyping urochloa fenotipado de alto rendimiento imagery-computer vision imagen-visión por ordenador forraje datasets |
| url | https://hdl.handle.net/10568/174759 |
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