欧亚国际

来自中国农业大学等机构的科学家刚刚利用Videometer多光谱成像系统发表了题为“Non-Destructive Testing of Alfalfa Seed Vigor Based on Multispectral Imaging Technology”的文章，文章发表于知名期刊Sensors 2022, 22(7), 2760; http://doi.org/10.3390/s22072760

基于多光谱成像技术的苜蓿种子活力无损检测 

摘要 

种子活力是评价植物种子质量的重要指标。如何快速、准确地评价种子活力一直是种子研究领域的一个重要问题。多光谱技术作为一种新的物理检测方法，具有灵敏度高、准确度高、无损、快速等优点，在种子质量评价中具有良好的应用前景。本研究利用多光谱成像技术收集了19个波长（365、405、430、450、470、490、515、540、570、590、630、645、660、690、780、850、880、940、970 nm）的紫花苜蓿种子的形态和光谱信息，这些波长代表了不同的种子活力水平和年龄。采用主成分分析（PCA）、线性判别分析（LDA）、支持向量机（SVM）、随机森林（RF）和规范化典型判别分析（nCDA）等五种多元分析方法对其活力进行了判别和预测。结果表明，LDA模型效果最好，对不同成熟度种子样品的平均准确率为92.9%，对不同收获年份种子样品的平均准确率为97.8%，LDA模型的平均灵敏度、特异性和精密度可达90%以上。nCDA鉴定无活力死种子的平均准确率达93.3%。在鉴定高活力种子和预测紫花苜蓿种子发芽率方面，该方法可达到95.7%。综上所述，本试验采用多光谱成像和多元分析技术，可以准确地评价和预测苜蓿种子活力、种子活力和种子发芽率，为种子质量的快速无损检测提供了重要的技术手段和思路。

关键词：种子活力；多光谱成像；种子萌发；种子活力；多元分析

Non-Destructive Testing of Alfalfa Seed Vigor Based on Multispectral Imaging Technology

by Shuheng Zhang1,Hanguo Zeng 1,Wei Ji 1,Kun Yi 1,Shuangfeng Yang 1,Peisheng Mao 1,Zhanjun Wang 2,Hongqian Yu 2 and Manli Li 1,*

1College of Grassland Science and Technology, China Agricultural University, Beijing 100193, China

2Institute of Desertification Control, Ningxia Academy of Agricultural and Forestry Sciences, Yinchuan 750002, China

Abstract

Seed vigor is an important index to evalsuate seed quality in plant species. How to evalsuate seed vigor quickly and accurately has always been a serious problem in the seed research field. As a new physical testing method, multispectral technology has many advantages such as high sensitivity and accuracy, nondestructive and rapid application having advantageous prospects in seed quality evalsuation. In this study, the morphological and spectral information of 19 wavelengths (365, 405, 430, 450, 470, 490, 515, 540, 570, 590, 630, 645, 660, 690, 780, 850, 880, 940, 970 nm) of alfalfa seeds with different level of maturity and different harvest periods (years), representing different vigor levels and age of seed, were collected by using multispectral imaging. Five multivariate analysis methods including principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) were used to distinguish and predict their vigor. The results showed that LDA model had the best effect, with an average accuracy of 92.9% for seed samples of different maturity and 97.8% for seed samples of different harvest years, and the average sensitivity, specificity and precision of LDA model could reach more than 90%. The average accuracy of nCDA in identifying dead seeds with no vigor reached 93.3%. In identifying the seeds with high vigor and predicting the germination percentage of alfalfa seeds, it could reach 95.7%. In summary, the use of Multispectral Imaging and multivariate analysis in this experiment can accurately evalsuate and predict the seed vigor, seed viability and seed germination percentages of alfalfa, providing important technical methods and ideas for rapid non-destructive testing of seed quality.  

Keywords: seed vigor; multispectral imaging; seed germination; seed viability; multivariate analysis