Physical dormancy (hard seed) occurs in most species of Leguminosae family and has great consequences not only for ecological adaptation but also for agricultural practice of these species. A rapid, nondestructive and on-site screening method to detect hard seed within species is fundamental important for maintaining seed vigor and germplasm storage as well as understanding seed adaptation to various environment. In this study, the potential of multispectral imaging with object-wise multivariate image analysis was evaluated as a way to identify hard and soft seeds in Acacia seyal, Galega orientulis, Glycyrrhiza glabra, Medicago sativa, Melilotus officinalis, and Thermopsis lanceolata. Principal component analysis (PCA), linear discrimination analysis (LDA), and support vector machines (SVM) methods were applied to classify hard and soft seeds according to their morphological features and spectral traits. The performance of discrimination model via multispectral imaging analysis was varied with species. For M. officinalis, M. sativa, and G. orientulis, an excellent classification could be achieved in an independent validation data set. LDA model had the best calibration and validation abilities with the accuracy up to 90% for M. sativa. SVM got excellent seed discrimination results with classification accuracy of 91.67% and 87.5% for M. officinalis and G. orientulis, respectively. However, both LDA and SVM model failed to discriminate hard and soft seeds in A. seyal, G. glabra, and T. lanceolate. Multispectral imaging together with multivariate analysis could be a promising technique to identify single hard seed in some legume species with high efficiency. More legume species with physical dormancy need to be studied in future research to extend the use of multispectral imaging techniques.

中文翻译：

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通过六种豆科植物的多光谱成像分析对单个硬种子进行无损识别。

物理休眠（硬种子）发生在豆科的大多数物种中，不仅对生态适应有重大影响，而且对这些物种的农业实践也有重大影响。一种快速、无损和现场筛选方法来检测物种内的硬种子对于保持种子活力和种质储存以及了解种子对各种环境的适应至关重要。在这项研究中，评估了多光谱成像与对象多变量图像分析的潜力，作为一种识别金合欢、东方 Galega 、光果甘草、紫花苜蓿、苜蓿和 Thermopsis lanceolata 中硬种子和软种子的方法。主成分分析 (PCA)、线性判别分析 (LDA)、并应用支持向量机（SVM）方法根据其形态特征和光谱特征对硬种子和软种子进行分类。通过多光谱成像分析识别模型的性能因物种而异。对于 M. officinalis、M. sativa 和 G. orientulis，可以在独立的验证数据集中实现出色的分类。LDA 模型具有最佳的校准和验证能力，对 M. sativa 的准确度高达 90%。SVM对M. officinalis和G. orientulis的分类准确率分别为91.67%和87.5%，获得了优异的种子识别结果。然而，LDA 和 SVM 模型都未能区分 A. seyal、G. glabra 和 T. lanceolate 中的硬种子和软种子。多光谱成像结合多变量分析可能是一种有前途的技术，可以高效地识别某些豆科植物中的单个硬种子。在未来的研究中需要研究更多具有物理休眠的豆科植物，以扩展多光谱成像技术的使用。