Digital descriptors sharpen classical descriptors, for improving genebank accession management: A case study on Arachis spp. and Phaseolus spp.

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cg.authorship.typesCGIAR and developing country instituteen
cg.contributor.affiliationInternational Center for Tropical Agricultureen
cg.contributor.affiliationUniversidad Nacional de Colombiaen
cg.creator.identifierConejo-Rodriguez: 0000-0001-7129-4016en
cg.creator.identifierJuan José Gonzalez Guzman: 0000-0002-7643-3430en
cg.creator.identifierPeter Wenzl: 0000-0003-4657-8468en
cg.creator.identifierMilan Oldřich Urban: 0000-0002-3684-856Xen
cg.identifier.doihttps://doi.org/10.1371/journal.pone.0302158en
cg.isijournalISI Journalen
cg.issn1932-6203en
cg.issue5en
cg.journalPLoS ONEen
cg.reviewStatusPeer Reviewen
cg.subject.alliancebiovciatGENETIC RESOURCESen
cg.subject.alliancebiovciatSTANDARDSen
cg.volume19en
dc.contributor.authorConejo-Rodriguez, Diego Felipeen
dc.contributor.authorGonzalez-Guzman, Juan Joseen
dc.contributor.authorRamirez-Gil, Joaquín Guillermoen
dc.contributor.authorWenzl, Peteren
dc.contributor.authorUrban, Milanen
dc.date.accessioned2025-03-10T14:44:16Zen
dc.date.available2025-03-10T14:44:16Zen
dc.identifier.urihttps://hdl.handle.net/10568/173546
dc.titleDigital descriptors sharpen classical descriptors, for improving genebank accession management: A case study on Arachis spp. and Phaseolus spp.en
dcterms.abstractHigh-throughput phenotyping brings new opportunities for detailed genebank accessions characterization based on image-processing techniques and data analysis using machine learning algorithms. Our work proposes to improve the characterization processes of bean and peanut accessions in the CIAT genebank through the identification of phenomic descriptors comparable to classical descriptors including methodology integration into the genebank workflow. To cope with these goals morphometrics and colorimetry traits of 14 bean and 16 forage peanut accessions were determined and compared to the classical International Board for Plant Genetic Resources (IBPGR) descriptors. Descriptors discriminating most accessions were identified using a random forest algorithm. The most-valuable classification descriptors for peanuts were 100-seed weight and days to flowering, and for beans, days to flowering and primary seed color. The combination of phenomic and classical descriptors increased the accuracy of the classification of Phaseolus and Arachis accessions. Functional diversity indices are recommended to genebank curators to evaluate phenotypic variability to identify accessions with unique traits or identify accessions that represent the greatest phenotypic variation of the species (functional agrobiodiversity collections). The artificial intelligence algorithms are capable of characterizing accessions which reduces costs generated by additional phenotyping. Even though deep analysis of data requires new skills, associating genetic, morphological and ecogeographic diversity is giving us an opportunity to establish unique functional agrobiodiversity collections with new potential traits.en
dcterms.accessRightsOpen Access
dcterms.bibliographicCitationConejo-Rodriguez, D.F.; Gonzalez-Guzman, J.J.; Ramirez-Gil, J.G.; Wenzl, P.; Urban, M. (2024) Digital descriptors sharpen classical descriptors, for improving genebank accession management: A case study on Arachis spp. and Phaseolus spp. PLoS ONE 19(5): e0302158. ISSN: 1932-6203en
dcterms.extente0302158en
dcterms.issued2024-05-02en
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.publisherPublic Library of Scienceen
dcterms.subjectbeansen
dcterms.subjectgene banksen
dcterms.subjectstandardsen
dcterms.subjectagronomic charactersen
dcterms.subjectphenotypingen
dcterms.subjectmethodsen
dcterms.subjecthigh-throughput phenotypingen
dcterms.subjectgroundnutsen
dcterms.typeJournal Article

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