Digital functional phenomic descriptors featured from machine learning-driven image-based phenotyping improve the accuracy of classic descriptors: A case study on Arachis spp. and Phaseolus spp.

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dc.creatorConejo Rodriguez, F.
dc.creatorGonzalez Guzman, J.
dc.creatorRamirez, Gil J.
dc.creatorUrban, Milan Oldřich
dc.creatorWenzl, Peter
dc.date2023-08-01
dc.date2023-12-26T13:59:57Z
dc.date2023-12-26T13:59:57Z
dc.date.accessioned2026-06-27T13:20:57Z
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/135933
dc.identifier.urihttp://hdl.handle.net/123456789/56543
dc.languageen
dc.rightsOpen Access
dc.sourceConejo Rodriguez, F.; Gonzalez Guzman, J.; Ramirez, G.J.; Urban, M.; Wenzl, P. (2023) Digital functional phenomic descriptors featured from machine learning-driven image-based phenotyping improve the accuracy of classic descriptors: A case study on Arachis spp. and Phaseolus spp. 17 sl.
dc.subjectevaluation
dc.subjectgene banks
dc.subjectmachine learning
dc.subjectagronomic characters
dc.subjectphenotyping
dc.subjectimagery
dc.subjectclassification
dc.subjectfunctional diversity
dc.titleDigital functional phenomic descriptors featured from machine learning-driven image-based phenotyping improve the accuracy of classic descriptors: A case study on Arachis spp. and Phaseolus spp.
dc.typePresentation

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