Rice false smut (RFS) is a devastating seed-brone rice disease in many rice-growing countries, endangering the health of rice germplasm resources and reducing the yield and quality of rice. This study aimed to propose an effective method for RFS detection in actual production based on near-infrared hyperspectral imaging (NIR-HIS) paired with pathological analysis. The true infection status of rice kernels collected in different conditions was labeled by PCR. The separability between healthy and infected rice kernels was explored using principal component analysis (PCA). Multivariate quantitative analysis models were constructed based on full wavelengths of laboratory-inoculated kernels. Characteristic wavelengths extracted to improve detection performance contained fingerprint information related to RFS infection. The best classification accuracies for healthy and infected mixed kernels with different infection degrees achieved 99.33% on calibration set and 99.20% on prediction set, respectively, using RF-ELM model. The practicality of detection model was further verified through obtaining detection accuracies of 91.07% and 89.38% for two varieties of field-infected rice kernels and visualizing the category attribute of single rice kernel in hyperspectral images. The overall results indicated the excellent potential of NIR-HSI for on-line large-scale seeds detection in modern seed industry.

稻曲丝病（RFS）在许多水稻种植国是毁灭性的种米水稻病，危害稻种质资源的健康并降低稻谷的产量和质量。本研究旨在提出一种基于近红外高光谱成像（NIR-HIS）结合病理分析的实际生产中RFS检测的有效方法。通过PCR标记在不同条件下收集的稻粒的真实感染状态。使用主成分分析（PCA）探索了健康稻谷粒和受感染稻谷粒之间的可分离性。基于实验室接种谷粒的全波长，构建了多元定量分析模型。提取以提高检测性能的特征波长包含与RFS感染有关的指纹信息。使用RF-ELM模型，不同感染程度的健康和感染混合粒的最佳分类精度分别在校准集和预测集上分别达到99.33％和99.20％。通过获得两个现场感染的稻米品种的检测精度为91.07％和89.38％，并在高光谱图像中可视化单个稻仁的类别属性，进一步验证了检测模型的实用性。总体结果表明，NIR-HSI在现代种子行业中具有在线大规模种子检测的巨大潜力。通过获得两个现场感染的稻米品种的检测精度为91.07％和89.38％，并在高光谱图像中可视化单个稻仁的类别属性，进一步验证了检测模型的实用性。总体结果表明，NIR-HSI在现代种子行业中具有在线大规模种子检测的巨大潜力。通过获得两个现场感染的稻米品种的检测精度为91.07％和89.38％，并在高光谱图像中可视化单个稻仁的类别属性，进一步验证了检测模型的实用性。总体结果表明，NIR-HSI在现代种子行业中具有在线大规模种子检测的巨大潜力。