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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cg.authorship.typesCGIAR and developing country instituteen
cg.contributor.affiliationInternational Center for Tropical Agricultureen
cg.contributor.affiliationGrupo Papalotlaen
cg.contributor.initiativeAccelerated Breeding
cg.contributor.initiativeSustainable Animal Productivity
cg.coverage.countryColombia
cg.coverage.countryMexico
cg.coverage.iso3166-alpha2CO
cg.coverage.iso3166-alpha2MX
cg.coverage.regionAmericas
cg.coverage.regionCentral America
cg.coverage.regionSouth America
cg.coverage.regionLatin America and the Caribbean
cg.creator.identifierDarwin Alexis Arrechea-Castillo: 0000-0002-2395-2181en
cg.creator.identifierPaula Espitia-Buitrago: 0000-0002-6610-1491en
cg.creator.identifierronald david estupiñan arboleda: 0009-0000-1006-4401en
cg.creator.identifierRiquelmer de Jesus Velázquez Henández: 0009-0004-4308-917Xen
cg.creator.identifierAndres Felipe Ruiz-Hurtado: 0000-0003-1293-8736en
cg.creator.identifierLuis M. Hernandez: 0000-0002-8816-0572en
cg.creator.identifierRosa Noemi Jauregui: 0000-0001-5403-7392en
cg.creator.identifierJuan Andrés Cardoso Arango: 0009-0001-8761-0578en
cg.identifier.doihttps://doi.org/10.1016/j.dib.2025.111593en
cg.isijournalISI Journalen
cg.issn2352-3409en
cg.journalData in Briefen
cg.reviewStatusPeer Reviewen
cg.subject.actionAreaGenetic Innovation
cg.subject.actionAreaResilient Agrifood Systems
cg.subject.alliancebiovciatINFORMATICSen
cg.subject.alliancebiovciatLIVESTOCKen
cg.subject.alliancebiovciatPLANT BREEDINGen
cg.subject.alliancebiovciatPLANT GENETIC RESOURCESen
cg.subject.alliancebiovciatTROPICAL FORAGESen
cg.subject.impactAreaNutrition, health and food security
cg.subject.impactAreaPoverty reduction, livelihoods and jobs
cg.subject.sdgSDG 1 - No povertyen
cg.subject.sdgSDG 2 - Zero hungeren
cg.subject.sdgSDG 8 - Decent work and economic growthen
cg.subject.sdgSDG 13 - Climate actionen
cg.subject.sdgSDG 17 - Partnerships for the goalsen
cg.volume60en
dc.contributor.authorArrechea-Castillo, Darwin Alexisen
dc.contributor.authorEspitia-Buitrago, Paulaen
dc.contributor.authorFlorian-Vargas, Daviden
dc.contributor.authorEstupinan, Ronald Daviden
dc.contributor.authorVelázquez-Hernández, Riquelmeren
dc.contributor.authorRuiz-Hurtado, Andres Felipeen
dc.contributor.authorHernandez, Luis Miguelen
dc.contributor.authorJauregui, Rosa Noemien
dc.contributor.authorCardoso, Juan Andresen
dc.date.accessioned2025-05-21T14:55:09Z
dc.date.available2025-05-21T14:55:09Z
dc.identifier.urihttps://hdl.handle.net/10568/174759
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 annotationsen
dcterms.abstractThis 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.en
dcterms.accessRightsOpen Access
dcterms.available2025-04-28
dcterms.bibliographicCitationArrechea-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: 2352-3409en
dcterms.extent111593en
dcterms.issued2025-04-28
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.publisherElsevier BVen
dcterms.subjectmachine learningen
dcterms.subjectaprendizaje automáticoen
dcterms.subjectartificial intelligenceen
dcterms.subjectforageen
dcterms.subjectinteligencia artificialen
dcterms.subjectgrassesen
dcterms.subjecthigh-throughput phenotypingen
dcterms.subjecturochloaen
dcterms.subjectfenotipado de alto rendimientoen
dcterms.subjectimagery-computer visionen
dcterms.subjectimagen-visión por ordenadoren
dcterms.subjectforrajeen
dcterms.subjectdatasetsen
dcterms.typeData Paper
dcterms.typeJournal Article

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