Advances in Seed Health Testing: Integrating Molecular, Imaging and AI-Based Diagnostics for Improved Seed Quality Assurance

Seed health testing is undergoing a rapid transformation as emerging technologies supplement and, in some cases, replace conventional diagnostic methods. This review synthesizes recent advances in molecular diagnostics (PCR, qPCR, LAMP, and metabarcoding), non-destructive imaging approaches (hyperspectral, multispectral and X-ray) and AI-assisted pattern recognition for pathogen detection in seeds. Emphasis is placed on integrating these tools into high-throughput seed quality programs, with case studies from vegetable, ornamental and field crop systems. We highlight current limitations in cost, regulatory alignment and global standardization, while identifying future opportunities for rapid, sensitive and field-deployable testing. This review aims to guide researchers, seed technologists and policymakers toward more efficient and reliable seed health assurance strategies.