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

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dc.creatorArrechea-Castillo, Darwin Alexis
dc.creatorEspitia-Buitrago, Paula
dc.creatorFlorian-Vargas, David
dc.creatorEstupinan, Ronald David
dc.creatorVelázquez-Hernández, Riquelmer
dc.creatorRuiz-Hurtado, Andres Felipe
dc.creatorHernandez, Luis Miguel
dc.creatorJauregui, Rosa Noemi
dc.creatorCardoso, Juan Andres
dc.date2025-06
dc.date2025-05-21T14:55:09Z
dc.date2025-05-21T14:55:09Z
dc.date.accessioned2026-06-27T13:27:37Z
dc.descriptionThis 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.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/174759
dc.identifier.urihttp://hdl.handle.net/123456789/60072
dc.languageen
dc.publisherElsevier
dc.rightsOpen Access
dc.sourceArrechea-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
dc.subjectmachine learning
dc.subjectaprendizaje automático
dc.subjectartificial intelligence
dc.subjectforage
dc.subjectinteligencia artificial
dc.subjectgrasses
dc.subjecthigh-throughput phenotyping
dc.subjecturochloa
dc.subjectfenotipado de alto rendimiento
dc.subjectimagery-computer vision
dc.subjectimagen-visión por ordenador
dc.subjectforraje
dc.subjectdatasets
dc.titleUpdating high-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids with expanded images and annotations
dc.typeJournal Article

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