Videometer多光谱种子表型产品与解决方案

VideometerLab 4多光谱种子表型成像系统是丹麦理工大学与丹麦Videometer公司开发，是用于种子研究先进的多光谱表型成像设备，典型客户为ISTA国际种子检验协会、ESTA欧洲种子检验协会、John Innes Centre、LGC化学家集团、奥胡斯大学等等，利用该系统发表的文章已经超过300篇。

Videometer种子表型表型成像系统可测量种子如尺寸、颜色、形状等，间接测定种子参数如种子纯度、发芽百分比、发芽率、种子健康度、种子成熟度、中寿命等。种子活力综合种子活力是种子发芽和出苗率、幼苗生长的潜势、植株抗逆能力和生产潜力的总和（发芽和出苗期间的活性水平与行为），是种子品质的重要指标，具体包括吸涨后旺盛的代谢强度、出苗能力、抗逆性、发芽速度及同步性、幼苗发育与产量潜力。种子活力是植物的重要表型特征，传统检测方法包括低温测试、高温加速衰老测试、幼苗生长测定等。

该系统也可以对细菌、虫卵、真菌等进行高通量成像测量，进行病理学、毒理学或其它研究。对于拟南芥等冠层平展的植物，可以进行自动的叶片计数等。

Videometer Lab 4是一款新型、功能强大且性价比高的表型成像测量系统。通过控制系统就可以进行高分辨率多光谱成像。基础模块包括可见光成像，UV紫外成像以及NIR成像。可固定摄像头或移动摄像头。因拍照速度迅速，可实现较高通量成像。可以测量较小的样品，比如拟南芥等小植株、用多孔板培养的植物、多孔板里的叶圆片、以及植物的种子等，分析软件功能强大。

Videometer SeedLab种子表型实验室系统

最近Videometer公司又推出了业界具有革新性、颠覆性的Videometer SeedLab种子实验室系统。按照客户的评价，该多光谱成像系统是增强种子分析和分类功能的强化版数字种子表型实验系统，该系统将通过提供种子和谷物分析的综合解决方案来帮助研究活动。此外，种子表型实验系统将可对种子和谷物进行分析，作为多功能通用成像平台来支持相关领域活动。客户评价道：该革新性的Videometer种子表型实验系统有可能彻底改变种子行业。

系统的功能

该系统是一个交钥匙一站式解决方案，可完全实现自动化种子分析。事实上，Videometer SeedLab由光谱成像、先进的多元统计和机器学习、人工智能算法驱动，这些算法能够自动检查、分析、分类和排序不同的种子。

Videometer SeedLab集成了不同的元件，可完全实现自动化种子检测。分析从一个漏斗开始，用于导入产品，然后在VideometerLab光谱成像仪下方的传送带上运输。然后对产品进行检查并进行“数字”分类。分级后，一个提取和放置自动机器臂膀根据种子的分类将其物理分类到不同的运输容器中。

Videometer SeedLab定制

此外，Videometer SeedLab允许高度定制：使用该系统，不仅可以个性化定制送货箱和吸盘，还可以定制分类模型，以便根据您的产品需求进行定制。此外，还可以添加多荧光选项，例如允许检测真菌毒素或前照灯选项，以便更好地检查种子表面的形态。

玉米真菌感染检测

大麦镰刀菌感染

Videometer通过测量样品在19种不同波长的LED频闪光下的成像来获取有用的信息。这些图像可以独立分析使用，也可以叠加起来合成高分辨率的彩色图像。Videometer备选模块包括叶绿素荧光成像模块，能够实现叶绿素荧光成像（叶绿素a和叶绿素b）。

Videometer种子表型活力成像系统包括种子形态测量、种苗多光谱荧光成像检测等现代技术，全面检测种子的形态、发芽及其抗逆性，是目前种子表型活力较全面的无损检测系统，是种子及种苗表型分析的较佳组合。

主要技术特点

LED光源技术，测量样品在19个波段下成像获取种子各种信息，VideometerLab多光谱荧光成像技术，高通量、高灵敏度检测种苗表型、叶绿素含量、活力、光合效率及抗逆性等，进一步分析种子的反射光谱及种子含水量等。种子形态测量参数：种子数量、长度、宽度、体积大小、表面积、周长及颜色分析

种子、种质资源库建设

种子叶绿素荧光成像测量，可用于小植株表型测量以及生态学研究，研究植物密度、宽度、叶柄长、叶片数、叶色、叶长、叶面积、叶颜色、叶病斑、绿度指数，花径、花面积、花、色分级、画图像提取，果实品质、纵径、果形指数、果实颜色分级，如小侧根、绒毛研究等。

叶绿素含量测量以及生物钟节奏研究。叶绿素含量多少与种子活力密切相关，可用此作为种子活力筛选的一个重要指标，系统还可用于Marker标记测量，如GFP绿色荧光蛋白等。 

利用Videometer多光谱表型研究平台发表的部分文章

1、Classification of Processing Damage in Sugar Beet (Beta vulgaris) Seeds by Multispectral Image Analysis

2、Systematic establishment of colour descriptor states through image-based phenotyping

3、利用多光谱成像系统结合化学计量法无损鉴别高品质西瓜种子的可行性。

4、Genebank seed accession phenotyping through spectral imaging

5、Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview

6、Utilization of computer vision and multispectral imaging techniques for classifcation of cowpea (Vigna unguiculata) seeds

7、Final report: Application of  multispectral imaging (MSI) to  food and feed sampling and  analysis FSA Contract Reference No.: SEP-EOI-05
Project Deliverable: 5

种子研究部分文章列表

1.A virtual seed file: the use of multispectral image analysis in the management of genebank seed accessions

2.Multispectral imaging as a potential tool for seed health testing of spinach (Spinacia oleracea L.)

3.Classification of different tomato seed cultivars by multispectral visible-near infrared spectroscopy and chemometrics

4.Viability prediction of Ricinus cummunis L. seeds using multispectral imaging

5.Online variety discrimination of rice seeds using multispectral imaging and chemometric methods

6.Classification of Processing Damage in Sugar Beet (Beta vulgaris) Seeds by Multispectral Image Analysis

7.Nondestructive determination of transgenic Bacillus thuringiensis rice seeds (Oryza sativa L.) using multispectral imaging and chemometric methods

8.Use of multispectral images and chemometrics in tomato seed studies    

9.Discrimination in varieties of rice seeds with multispectral imaging using support vector machine

10.Rapid Discrimination of High-Quality Watermelon Seeds by Multispectral Imaging Combined with Chemometric Methods

11.Non-destructive discrimination of conventional and glyphosate-resistant soybean seeds and their hybrid descendants using multispectral imaging and chemometric methods

12.Discrimination of Kernel Quality Characteristics for Sunflower Seeds Based on Multispectral Imaging Approach

13.Multispectral imaging – a new tool in seed quality assessment?

14.Classification of Haploid and Diploid Maize Seeds by Using Image Processing Techniques and Support Vector Machines

15.Use of partial least squares discriminant analysis on visible-near infrared multispectral image data to examine germination ability and germ length in spinach seeds

16.Identification of Haploid Maize Seeds using Gray Level Co-occurrence Matrix and Machine Learning Techniques

17.Effects of Polymer Coating on Rice Seed Germination

18.Recent advances in emerging techniques for non-destructive detection of seed viability: A review

19.Optimization of Germination Inhibitors Elimination from Sugar Beet (Beta vulgaris L.) Seeds of Different Maturity Classes

20.Differentiation of alfalfa and sweet clover seeds via multispectral imaging

21.Integrating Optical Imaging Tools for Rapid and Non-invasive Characterization of Seed Quality: Tomato (Solanum lycopersicum L.) and Carrot (Daucus carota L.) as Study Cases

22.Determination Of Sitotroga cerealella (Lepidoptera: Gelechiidae) Infestation In Wheat Seeds By Radiographic And Multispectral Image Analysis

23.Geographical and inter-annual patterns of seed viability in the threatened cold desert perennial Ivesia webberi, and the prospect of nondestructive seed testing methods*

24.Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality

25.Discrimination of Pepper Seed Varieties by Multispectral Imaging Combined with Machine Learning

26.Non-destructive identification of single hard seed via multispectral imaging analysis in six legume species

27.Seed germination and seedling growth parameters in nine tall fescue varieties under salinity stress

28.A novel approach for Jatropha curcas seed health analysis based on multispectral and resonance imaging techniques

29.Cultivar Discrimination of Single Alfalfa (Medicago sativa L.) Seed via Multispectral Imaging Combined with Multivariate Analysis

30.Chlorophyll fluorescence as a new marker for peanut seed quality evaluation

31.Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis

32.Autofluorescencespectral imaging as an innovative method for rapid, nondestructive and reliable assessing of soybean seed quality

33.Research on Classification Method of Eggplant Seeds Based on Machine Learning and Multispectral Imaging Classification Eggplant Seeds