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

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dc.creatorConejo-Rodriguez, Diego Felipe
dc.creatorGonzalez-Guzman, Juan Jose
dc.creatorRamirez-Gil, Joaquín Guillermo
dc.creatorWenzl, Peter
dc.creatorUrban, Milan
dc.date2024-05-02
dc.date2025-03-10T14:44:16Z
dc.date2025-03-10T14:44:16Z
dc.date.accessioned2026-06-27T14:43:51Z
dc.descriptionHigh-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.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/173546
dc.identifier.urihttp://hdl.handle.net/123456789/86736
dc.languageen
dc.publisherPublic Library of Science
dc.rightsOpen Access
dc.sourceConejo-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: 1932-6203
dc.subjectbeans
dc.subjectgene banks
dc.subjectstandards
dc.subjectagronomic characters
dc.subjectphenotyping
dc.subjectmethods
dc.subjecthigh-throughput phenotyping
dc.subjectgroundnuts
dc.titleDigital descriptors sharpen classical descriptors, for improving genebank accession management: A case study on Arachis spp. and Phaseolus spp.
dc.typeJournal Article

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Centro Internacional de Agricultura Tropical (CIAT)