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 eligibility rate after sorting (P) increases from iP under this condition offers a reasonable criterion for ranking candidate models. Moreover, the model performance under conditions with adjusted discrimination strength was verified using a metric "relative precision" (rP) defined as (P-iP)/(1-iP). Because rP, compared to precision (= P), is less dependent on iP in relation to recall (R), i.e., recovery rate of eligible seeds, the rP-R curve and area under the curve also offer useful criteria for spotting better discriminant models. We confirmed that the batches of seeds given higher discriminant scores by the models selected with reference to these criteria were more enriched with eligible seeds. The method presented can be readily implemented in developing a sorting device that enables "last-percent improvement" in eligibility rates of crop seeds.