Videometer Lab4多光譜成像系統(tǒng)在苜蓿種子自然老化無損鑒別上的應(yīng)用

來自中國農(nóng)業(yè)大學(xué)草業(yè)科學(xué)與技術(shù)學(xué)院的科學(xué)家利用VideometerLab 4多光譜成像系統(tǒng)發(fā)表了題為Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis的文章，文章發(fā)表于期刊Sensors 2021,21(17), 5804; https://doi.org/10.3390/s21175804 (registering DOI)。

種子老化檢測和對有活力種子的預(yù)測在紫花苜蓿種子生產(chǎn)中具有重要意義，但傳統(tǒng)方法具有破壞性。因此，建立一種快速、無損的種子篩選方法在種子產(chǎn)業(yè)和研究中是十分必要的。本研究利用多光譜成像技術(shù)對不同貯藏年份的老化苜蓿種子的形態(tài)特征和光譜特征進(jìn)行了研究。然后，我們采用五種多元分析方法，即主成分分析（PCA）、線性判別分析（LDA）、支持向量機（SVM）、隨機森林（RF）和歸一化典型判別分析（nCDA）來預(yù)測老化和存活的種子。結(jié)果表明，未老化和老化種子在450～690nm處的平均光反射率存在顯著差異。LDA模型在區(qū)分老化種子和非老化種子方面具有較高的準(zhǔn)確率（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，死亡種子與老化種子的識別準(zhǔn)確率分別為69.7%、72.0%和97.6%。nCDA預(yù)測老化種子發(fā)芽的準(zhǔn)確率在75.0%到100.0%之間�？傊�，我們描述了一種非破壞性、快速和高通量的方法來篩選紫花苜蓿中具有各種活力的老化種子。

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.
Keywords: aged seeds; multispectral imaging; multivariate analysis; alfalfa; non-destructive identification