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

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FRONTIERS MEDIA

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This 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.

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cassava, image analysis, artificial intelligence, visual inspection, genome-wide association studies, postharvest decay

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http://hdl.handle.net/123456789/59822

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https://hdl.handle.net/10568/182872

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Alianza Bioversity International y CIAT