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科学家利用Videometer多光谱成像系统发表题苜蓿种子自然老化无损鉴别文章
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来自中国农业大学草业科学与技术学院的科学家利用VideometerLab 4多光谱成像系统发表了题为Non-DestructiveIdentification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis的文章，文章发表于期刊Sensors 2021,21(17), 5804; http://doi.org/10.3390/s21175804 (registering DOI)。
自然老化苜蓿种子的多光谱成像无损鉴定
种子老化检测和对有活力种子的预测在紫花苜蓿种子生产中具有重要意义，但传统方法具有破坏性。因此，建立一种快速、无损的种子筛选方法在种子产业和研究中是十分必要的。本研究利用多光谱成像技术对不同贮藏年份的老化苜蓿种子的形态特征和光谱特征进行了研究。然后，我们采用五种多元分析方法，即主成分分析（PCA）、线性判别分析（LDA）、支持向量机（SVM）、随机森林（RF）和归一化典型判别分析（nCDA）来预测老化和存活的种子。结果表明，未老化和老化种子在450～690nm处的平均光反射率存在显著差异。LDA模型在区分老化种子和非老化种子方面具有较高的准确率（99.8~100.0%），高于SVM（87.4~99.3%）和RF（84.6~99.3%）。此外，在RF、SVM和LDA方法中，死亡种子与老化种子的识别准确率分别为69.7%、72.0%和97.6%。nCDA预测老化种子发芽的准确率在75.0%到100.0%之间。总之，我们描述了一种非破坏性、快速和高通量的方法来筛选紫花苜蓿中具有各种活力的老化种子。
Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis
Abstract
Seed aging detection and viable seed prediction are of great significance in alfalfa seed production, but traditional methods are disposable and destructive. Therefore, the establishment of a rapid and non-destructive seed screening method is necessary in seed industry and research. In this study, we used multispectral imaging technology to collect morphological features and spectral traits of aging alfalfa seeds with different storage years. Then, we employed five multivariate analysis methods, i.e., principal component analysis (PCA), linear discrimination analysis (LDA), support vector machines (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) to predict aged and viable seeds. The results revealed that the mean light reflectance was significantly different at 450~690 nm between non-aged and aged seeds. LDA model held high accuracy (99.8~100.0%) in distinguishing aged seeds from non-aged seeds, higher than those of SVM (87.4~99.3%) and RF (84.6~99.3%). Furthermore, dead seeds could be distinguished from the aged seeds, with accuracies of 69.7%, 72.0% and 97.6% in RF, SVM and LDA, respectively. The accuracy of nCDA in predicting the germination of aged seeds ranged from 75.0% to 100.0%. In summary, we described a nondestructive, rapid and high-throughput approach to screen aged seeds with various viabilities in alfalfa.
