Seed materials used in this study.

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.

随着全球人口持续增长，保障粮食稳产的优质作物种子需求日益攀升。与之相悖的是，对高品质种子的过度需求，却因合格率的细微缺口引发了“种子损耗与浪费”问题。本研究将近红外成像光谱（near-infrared imaging spectrometry）技术与机器学习方法相结合，针对6个物种、8个栽培品种的作物种子的发芽率与父本单倍型开展评估。研究以种子反射光谱作为解释变量，通过线性稀疏建模推导得到用于品质评价的候选判别因子。为系统性推进模型选择流程，本研究将“种子回收率与初始合格率（initial eligibility rate，iP）一致”的筛选条件定义为“标准条件”；在此条件下，筛选后的合格率（P）相较于初始合格率iP的提升幅度，可为候选模型的排序提供合理评判标准。此外，本研究通过定义相对精度（relative precision，rP）=(P–iP)/(1–iP)，对判别强度调整后的模型性能进行验证。相较于精度（即P），相对精度rP与召回率（recall，R，即合格种子的回收率）的关联对iP的依赖性更低，因此rP-R曲线及其曲线下面积也可为筛选更优判别模型提供有效依据。本研究证实，经上述标准筛选得到的模型所给出判别分数更高的种子批次，其合格种子占比显著提升。本研究提出的方法可便捷地应用于开发可实现作物种子合格率“最后百分之一提升”的分选设备。