Sweet potato is a key crop for food security, yet its production and quality are threatened by challenges such as Sweet potato Virus Disease (SPVD), weevil damage, and difficulties in accurately measuring mealiness an important trait linked to consumer preference. Traditional phenotyping methods for assessing these traits are often slow, labor-intensive, and inefficient. This project aims to develop high-throughput image analysis technologies to enable rapid, accurate, and efficient assessment of these critical traits. The solutions developed will support improved breeding programs, pest and disease management, and postharvest quality monitoring, ultimately contributing to enhanced food security and crop resilience.