中国科学家利用Videometer多光谱成像系统在知名期刊Front. Plant Sci.,上发表了题为“Cultivars identification of oat (Avena sativa L.) seed via multispectral imaging analysis”的文章。

燕麦种子品种的多光谱成像分析

品种鉴定在确保燕麦生产质量和生产者利益方面发挥着重要作用。然而，传统的燕麦品种鉴别方法通常具有破坏性、耗时和复杂性。在这项研究中，通过多光谱成像结合多元分析，检验了快速无损检测燕麦种子品种的可行性。应用主成分分析（PCA）、线性判别分析（LDA）和支持向量机（SVM）对16个燕麦品种的种子进行了形态学特征、光谱特征或其组合分类。结果表明，使用多光谱成像技术可以很容易地显示燕麦种子品种之间的明显差异，并且通过结合形态学和光谱特征的数据可以实现出色的区分。LDA和SVM模型的平均分类准确率分别为89.69%和92.71%。因此，多光谱成像结合多元分析为燕麦品种的快速无损鉴定提供了一种新的方法。

Cultivars identification of oat (Avena sativa L.) seed via multispectral imaging analysis

Cultivar identification plays an important role in ensuring the quality of oat production and the interests of producers. However, the traditional methods for discrimination of oat cultivars are generally destructive, time-consuming and complex. In this study, the feasibility of a rapid and nondestructive determination of cultivars of oat seeds was examined by using multispectral imaging combined with multivariate analysis. The principal component analysis (PCA), linear discrimination analysis (LDA) and support vector machines (SVM) were applied to classify seeds of 16 oat cultivars according to their morphological features, spectral traits or a combination thereof. The results demonstrate that clear differences among cultivars of oat seeds could be easily visualized using the multispectral imaging technique and an excellent discrimination could be achieved by combining data of the morphological and spectral features. The average classification accuracy of the testing sets was 89.69% for LDA, and 92.71% for SVM model. Therefore, the potential of a new method for rapid and nondestructive identification of oat cultivars was provided by multispectral imaging combined with multivariate analysis.