Last-percent improvement in eligibility rates of crop seeds based on quality evaluation using near-infrared imaging spectrometry.

ABSTRACT: As the world population continues to grow, the need for high-quality crop seeds that promise stable food production is increasing. Conversely, excessive demand for high quality is causing "seed loss and waste" due to slight shortfalls in eligibility rates. In this study, we applied near-infrared imaging spectrometry combined with machine learning techniques to evaluate germinability and paternal haplotype in crop seeds from 6 species and 8 cultivars. Candidate discriminants for quality evaluation were derived by linear sparse modeling using the seed reflectance spectra as explanatory variables. To systematically proceed with model selection, we defined the sorting condition where the recovery rate of seeds matches the initial eligibility rate (iP) as "standard condition". How much the elig

SUBMITTER: Matsuda O 

PROVIDER: S-EPMC10511137 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature