Genome-wide association study of post-harvest physiological deterioration in cassava (Manihot esculenta Crantz) using visual and AI-powered phenotyping

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dc.creatorDankwa, Kwame Obeng
dc.creatorLuna Melendez, Jorge Luis
dc.creatorGiraldo, Juan Camilo
dc.creatorSanchez Sarria, Camilo Enrique
dc.creatorSelvaraj, Michael
dc.creatorOlasanmi, Bunmi
dc.creatorGimode, Winnie
dc.date2026-04-22
dc.date2026-05-12T15:13:36Z
dc.date.accessioned2026-06-27T13:27:08Z
dc.descriptionThis study investigated the genetic basis of postharvest physiological deterioration (PPD) in cassava using genome-wide association studies (GWAS) and both traditional visual scoring and AI-based phenotyping. Researchers analyzed 298 cassava accessions with two genotyping platforms and identified several significant genetic markers linked to PPD tolerance, particularly on chromosomes 1 and 12. The findings suggest that AI-powered phenotyping provides a more consistent and reproducible method for assessing PPD and offers valuable targets for breeding cassava varieties with improved shelf life and delayed deterioration.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/182872
dc.identifier.urihttp://hdl.handle.net/123456789/59822
dc.languageen
dc.publisherFRONTIERS MEDIA
dc.rightsOpen Access
dc.sourceDankwa, K.O.; Luna Melendez, J.L.; Giraldo, J.C.; Sanchez Sarria, C.E.; Selvaraj, M.; Olasanmi, B.; Gimode, W. (2026) Genome-wide association study of post-harvest physiological deterioration in cassava (Manihot esculenta Crantz) using visual and AI-powered phenotyping. Frontiers in Plant Science 17: 1807180. ISSN: 1664-462X
dc.subjectcassava
dc.subjectimage analysis
dc.subjectartificial intelligence
dc.subjectvisual inspection
dc.subjectgenome-wide association studies
dc.subjectpostharvest decay
dc.titleGenome-wide association study of post-harvest physiological deterioration in cassava (Manihot esculenta Crantz) using visual and AI-powered phenotyping
dc.typeJournal Article

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