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PlantScreen高通量植物表型成像剖析平台(传送带版)(二)

时间:2020-04-22

作者:海洋之神590官网入口

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 PlantScreen高通量植物表型成像剖析平台(传送带版)(二)

 

10.根系成像剖析

·RhizoTron根窗手艺,,, ,,,,,全自动成像剖析,,, ,,,,,标配根窗44x29.5x5.8cm(高x宽x厚度)

·不但可对根系成像剖析,,, ,,,,,还可对地上苗(shoot)举行成像剖析,,, ,,,,,苗高最大50cm

·新一代CMOS传感器,,, ,,,,,区分率12.3MP

·均一LED光源

·3层定位(顶部、中部、底部)根系浇灌系统(选配),,, ,,,,,3个水箱自力运行

·丈量参数包括:根深(或高度)、根冠宽度、高度与宽度比值、根冠面积、根冠紧实度、根系总长、轴对称性、根尖数、根节数等

 

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11.image.png自动浇灌与称重单位

·丈量参数:现实重量、浇水体积、最终重量、每个作育盆的相对重量

·操作指令:每个作育盆浇相同量的水(绝对克数或者现实重量的百分比);;;;;坚持相对重量;;;;;自界说每个作育盆的浇灌量模拟差别干旱或者内涝胁迫;;;;;称重前自动零校准,,, ,,,,,还可通过已知重量(如砝码)物品自动举行再校准

·每个作育盆的浇水量、日期、时间可划分程序控制纪录以建设差别干旱胁迫梯度等,,, ,,,,,并且与整个系统的表型大数据无缝团结剖析

·称重精度:大型植物±2g,,, ,,,,,小型植物±0.2g

·浇灌单位:流速3L/min,,, ,,,,,浇灌口高度可自动上下前后调解,,, ,,,,,包管最佳浇灌位置

 

12.自动化植物传送系统

·441.jpg传送植物巨细!!!�。浩局た突枨螅�,, ,,,,,最高可达200cm

·传送带容纳量:50盆植物(1000株小型植物),,, ,,,,,可扩展100盆、200盆、400盆等更大容量 ;;;;;表型剖析通量依差别的protocol而定,,, ,,,,,100分钟可以完成整个系统载荷植物样品的表型剖析,,, ,,,,,可随机传送至成像室举行成像剖析、随机浇灌

·作育盆:防UV聚丙烯质料,,, ,,,,,标准5L(口径24cm)作育盆,,, ,,,,,可通过适配器应用3L作育盆,,, ,,,,,可360度旋转

·具备手动载样环(manual loading loop)以便在系统待机模式下手动载样剖析实验、小组实验剖析等

·具备激光植物高度丈量监测系统和激光定位系统

·环形传送通道:具变速箱的三相异步马达,,, ,,,,,功率200-1000W,,, ,,,,,最大负载500kg,,, ,,,,,速率150mm/s,,, ,,,,,传送带质料为防UV高耐用PVC

·移动控制系统:中央处置惩罚单位CJ2M-CPU33;;;;;数字输入/输出最大2560点;;;;;输入/输出单位最大40;;;;;温度传感器Pt1000,,, ,,,,,Pt100,,, ,,,,,PTC;;;;;PLC通讯百兆以太网;;;;;OMRON MECHATROLINK-II 最大16轴准确定位

·RFID标签和QR植物辨识系统,,, ,,,,,自动读取每个样品托盘上的二维编码;;;;;辨识距离2-20cm;;;;;通讯RS485;;;;;可读取1维、2维和QR码;;;;;配备LED光源便于弱光下辨识

·情形监测传感器:温湿度传感器、PAR光合有用辐射传感器

·由主控制系统划分自动调控每一个样品托盘的丈量时间、丈量顺序、丈量参数、浇灌时间和浇灌量,,, ,,,,,从丈量单位到作育室的样品运转整个历程可实现完全自动控制,,, ,,,,,在无人值守情形下凭证预想程序自行完玉成部实验丈量事情。。。。。

 

13.主控制表型大数据平台

·组成:控制调理服务器、客户端应用服务器、数据服务器、可编程序逻辑控制器及专业剖析软件等,,, ,,,,,数据容量12TB

·自动控制与剖析功效:具备用户界说、可编辑自动丈量程序(protocols),,, ,,,,,凭证用户设定程序自动完玉成部实验。。。。。数据效果自动存储并剖析,,, ,,,,,剖析的数据效果可自动以动态曲线的形式显示。。。。。

 

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·MySQL数据库管理系统,,, ,,,,,可以处置惩罚拥有上万万条纪录的大型数据库,,, ,,,,,支持多种存储引擎,,, ,,,,,相关数据自动存储于数据库中的差别表中

·植物编码注册功效:包括植物识别码、所在托盘的识别码等存储在数据库中,,, ,,,,,丈量时自动提取自动读取条形码或RFID标签

·触摸屏操作界面,,, ,,,,,在线显示植物托盘数目、光线强度、剖析丈量状态及效果等,,, ,,,,,轻松通过软件完全控制所有的机械部件和成像事情站

·可用默认程序举行所有丈量,,, ,,,,,也可通过开发工具建设自界说的事情历程,,, ,,,,,或者手动操作LED光源开启或关闭、RGB成像、叶绿素荧光成像、高光谱成像、红外热成像、3D激光扫描、称重及浇灌等

·叶片跟踪监测功效(leaf tracking)�??? ??? ?椋�,, ,,,,,可以一连跟踪监测叶片的生长、转变等等

·3D投射手艺,,, ,,,,,可以通过高区分率RGB镜头 或激光扫描构建3D模子,,, ,,,,,通过投射手艺,,, ,,,,,将与其它传感器所得数据如叶绿素荧光、红外热成像温度数据、近红外数据、高光谱数据等投射在3D模子上一起举行比照剖析等

·允许用户通过互联网远程会见,,, ,,,,,举行数据处置惩罚、下载及更改实验设计

·所丈量的所有数据都是透明的、可以追溯的

·具备用户权限分级功效,,, ,,,,,避免其他职员误操作影响实验

·厂家远程故障诊断,,, ,,,,,软件终身免费升级

 

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执行标准:

·CE认证标准

·CSN EN 60529 防护品级标准

·CSN 33 01 65 导体侧识别标准

·CSN 33 2000-3 基础特征标准

·CSN 33 2000-4-41ed.2 电击�;;;;;け曜�

·CSN 33 2000-4-43 电源过载�;;;;;け曜�

·CSN 33 2000-5-51ed.2 通用规则标准

·CSN 33 2000-5-523 允许电流标准

·CSN 33 2000-5-54ed.2 接地与�;;;;;さ继灞曜�

·CSN EN 55011 工业、科学与医学装备丈量电磁滋扰的规模与要领

·2006/42/EG 机械指令标准

·73/23/EEG 低电压指令标准

·2004/108/EG 电磁相容性指令标准

 

附:部分参考文献

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2.Adhikari, P., Adhikari, T. B., Louws, F.F. J., & Panthee, D. R. 2020. Advances and Challenges in Bacterial Spot Resistance Breeding in Tomato (Solanum lycopersicum L.). International Journal of Molecular Sciences, 21(5), 1734.

3.Yang, W., Feng, H., Zhang, X., Zhang, J., Doonan, J. H., Et Al. 2020. Crop Phenomics and High-throughput Phenotyping: Past Decades, Current rent Challenges and Future Perspectives. Molecular Plant, 13(2), 187-214

4.Husi?ková, A., Humplík, J. F., H?bl, M.,M., Spíchal, L., & Lazár, D. 2019. Analysis of Cold-Developed vs. Cold-Acclimated Leaves Reveals Various Strategies of Cold Acclimation of Field Pea Cultivars. Remote Sensing, 11(24), 2964

5.Singh, A.K., Yadav, B.S., Dhanapal, S., Berliner, M., Finkelshtein, A., Chamovitz, D.A. 2019. CSN5A Subunit of COP9 Signalosome Temporally Buffers Response to Heat in Arabidopsis. Biomolecules 2019, 9, 805.

6.Jane?ková, H., Husi?ková, A., Lazár, D., Ferretti, U., Pospí?il, P., & ?pundová, M. 2019. Exogenous application of cytokinin during dark senescence eliminates the acceleration of photosystem II impairment caused by chlorophyll b deficiency in barley. Plant Physiology and Biochemistry, 136, 43–51

7.Marchetti, C. F., Ugena, L., Humplík, J. F., Polák, M., et al. 2019. A Novel Image-Based Screening Method to Study Water-Deficit Response and Recovery of Barley Populations Using Canopy Dynamics Phenotyping and Simple Metabolite Profiling. Frontiers in Plant Science, 10, 1252.

8.Rungrat T., Almonte A. A., Cheng R.,R., et al. 2019. A Genome-Wide Association Study of Non-Photochemical Quenching in response to local seasonal climates in Arabidopsis thaliana, Plant Direct, 3(5), e00138

9.Pavicic M, et al. 2019. High throughput invitro seed germination screen identifed new ABA responsive RING?type ubiquitin E3 ligases inArabidopsis thaliana. Plant Cell, Tissue and Organ Culture 139: 563-575

10.Wen Z., et al. 2019. Chlorophyll fluorescence imaging for monitoring effects of Heterobasidion parviporum small secreted protein induced cell death and in planta defense gene expression. Fungal Genetics and Biology 126: 37-49

11.Gao G., Tester M. A., Julkowska M. 2019. The use of high throughput phenotyping for assessment of heat stress-induced changes in Arabidopsis. Biorvix, 838102.

12.Paul K., Sorrentino M., Lucini L., Rouphaelouphael Y. F., Cardarelli M., Bonini P., Begona M., Reyeynaud H.E., Canaguier R., Trtílek M., Panzarová K., Colla G. 2019. A Combined Phenotypic and Metabolomic Approach for Elucidating the Biostimulant Action of a Plant-derived Protein Hydrolysate on Tomato Grown un under Limited Water Availability. Frontiers in Plant Science, 10:493

13.Wang L., Poque S., Valkonen J. P. T. 2019. Phenotyping viral infection in sweetpotato using a high-throughput chlorophyll fluorescence and thermal imaging platform. Plant Methods, 15, 116

14.Paul K, Sorrentino M, Lucini L, Rouphaelouphael Y, Cardarelli M, Bonini P, Reynaud H,H, Canaguier R, Trtílek M, Panzarová K, Colla G. 2019. Understanding the Biostimulant Action of Vegetal-Derived Protein Hydrolysates by High-Throughput Plant Phenotyping and Metabolomics: A Case Study on Tomato. Frontiers in Plant Science, 10:47.

15.Gonzalez-Bayon, R., Shen, Y., Groszman, M., Zhu, A., Wang, A., et al. 2019. Senescence and defense pathways contribute to heterosis. Plant Physiology, 180, 240–252.

16.Julkowska, M. M., Saade, S., Agarwal Al, G., Gao, G., Pailles, Y., et al. 2019. MVApp–Multivaria analysis application for streamlined data analysis and curation. Plant Physiology, 180, 1261–1276.

17.Ganguly D. R., Stone B. A B., Eichten S. E., Pogson B. J. 2019. Excess light priming in Arabidopsis thaliana genotypes with altered DNA methylomes, G3: Genes, Genomes, Genetics, 9(11), 3611-3621

18.Ameztoy, K., Baslam, M., Sánchez-Lópeópez, ?. M., Mu?oz, F. J., et al. 2019. Plant responses to fungal volatiles involve global post-translational thiol redox proteome changes that affect photosynthesis. Plant, Cell & Environment, 42(9), 2627-2644.

19.Adhikari N. D., Simko I., Mou B. 2019. Phenomic and Physiological Analysis of Salinity Effects on Lettuce. Sensors 19, 4814.

20.Ugena L, H?lová A, Podle?áková K,K, Humplík J.F., Dole?al K, Diego N, Spíchal L. 2018. Characterization of Biostimulant Mode of Action Using Novel Multi-Trait High-Throughput Screening of of Arabidopsis Germination and Rosette Growth. Frontiers in Plant Science, 9:1327.

21.Lyu, J. I., Kim, J. H., Chu, H., Taylor, M.M. A., Jung, S., et al. 2018. Natural allelic variation of GVS1 confers diversity in the regulation of leaf senescence in Arabidopsis. New Phytologist, 221(4), 2320-2334

22.Ganguly D. R., Crisp P. A., Eichten S. R., et al. 2018. Maintenance of pre-existing DNA methylation states through recurring excess-light stress. Plant Cell and Environment. 41(7), 1657-1672.

23.Rouphael Y., Spíchal L., Panzarová K.,K., et al. 2018. High-throughput Plant Phenotypin ping for Developing Novel Biostimulants: From Lab to Field or FroFrom Field to Lab? Front. Plant Sci., 9:1197.

24.Coe R. A., Chatterjee J., Acebron K., et al. 2018. High-throughput chlorophyll fluorescence screening of Setaria viridis for mutants with altered CO2 compensation points. Functional Plant Biology. 45(10), 1017-1025

25.Fichman Y., Koncz Z., Reznik N., et al. 2018. SELENOPROTEIN O is a chloroplast protein involved in ROS scavenging and its absence increases dehydration tolerance in Arabidopsis thaliana. Plant Science. 41(7), 1657-1672

26.Sytar O., Zivcak M., Olsovska K., Brestic M. 2018. Perspectives in High-Throughput Phenotyping of Qualitative Traits at the Whole-Plant Level. In: Sengar R., Singh A. eds Eco-friendly Agro-biological Techniques for Enhancing Crop Productivity. Springer, Singapore, 213-243.

27.De Diego N., Fürst T., Humplík J. F., et al. 2017. An Automated Method for High-Throughput Screening of Arabidopsis Rosette Growth in Multi-Well Plates and Its Validation in Stress Conditions. Frontiers in Plant Science. 8.

28.Lobos G. A., Camargo A. V., del Pozo A., et al. 2017. Editorial: Plant Phenotyping and Phenomics for Plant Breeding. Front. Plant Sci. 8.

29.Pavicic M., Mouhu K., Wang F., et al. 2017. Genomic and Phenomic Screens for Flower Related RING Type Ubiquitin E3 Ligases in Arabidopsis. Frontiers in Plant Scienc. Volume 8.

30.Rungrat T., Awlia M., Brown M. et al. 2017. Monitoring Photosynthesis by In Vivo Chlorophyll Fluorescence: Application to High-Throughput Plant Phenotyping. The Arabidopsis Book 14: e0185. 2016

31.Simko I., Hayes R. J. and Furbank R. T. 2017. Non-destructive Phenotyping of Lettuce Plants in Early Stages of Development with Optical Sensors. Frontiers in Plant Science. 2016;7:1985.

32.Sytar O., Brestic M., Zivcak M., et al. 2017. Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance. In Science of The Total Environment, 578, 90-99.

33.Sytar O., Brücková K., Kovár M., et al. 2017. Nondestructive detection and biochemical quantification of buckwheat leaves using visible VIS and near-infrared NIR hyperspectral reflectanceimaging. Journal of Central European Agriculture. 184, 864-878

34.Tschiersch H., Junker A., Meyer R. C., & Altmann, T. 2017. Establishment of integrated protocols for automated high throughput kinetic chlorophyll fluorescence analyses. Plant Methods, 13, 54.

35.Weber J., Kunz, C., Peteinatos, G., et al. 2017. Utilization of Chlorophyll Fluorescence Imaging Technology to Detect Plant Injury by Herbicides in Sugar Beet and Soybean. Weed Technology, 1-13.

36.Awlia M., Nigro A., Fajkus J., Schm?ckel S.M., Negr?o S., Santelia D., Trtílek M., Tester M., Julkowska M.M. and Panzarová K. 2016: High-throughput non-destructive phenotyping of traits contributing to salinity tolerance in Arabidopsis thaliana. Submitted Frontiers in Plant Sciences.

37.Bell J. and Dee M. H. 2016. The subset-matched Jaccard index for evaluation of Segmentation for Plant Images. Front Plant Sci. 2016; 7: 1985.

38.Bell J. and Dee M. H. 2016. Watching plants grow – a position paper on computer vision and Arabidopsis thaliana. IET Computer Vision. Volume 11, Issue 2, March 2017, p. 113 – 121.

39.Bush M.S., Pierrat O, Nibau C, et al.2016. eIF4A RNA Helicase Associates with Cyclin-Dependent Protein Kinase A in Proliferating Cells and is Modulated by Phosphorylation. Plant Physiol. 2016 Jul 7,

40.Cruz J. A., Savage L. J., Zegarac R., et al. 2016. Dynamic Environmental Photosynthetic Imaging Reveals Emergent Phenotypes. Cell Systems, Volume 2, Issue 6, 2016, Pages 365-377.

41.Sytar O., Brestic M., Zivcak M . 2016. Noninvasive Methods to Support Metabolomic Studies Targeted at Plant Phenolics for Food and Medicinal Use.  Plant Omics: Trends and Applications.

42.Humplik J.F., Lazar D., Husickova A. and Spichal L. 2015: Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses – a review. Plant Methods 11:29.

43.Humplik J.F., Lazar D., Fürst, T., Husickova A., Hybl, M. and Spichal L. 2015: Automated integrative high-throughput phenotyping of plant shoots: a case study of the cold-tolerance of pea Pisum sativum L.. Plant Methods 19;11:20.

44.Brown T.B., Cheng R., Sirault R.R., Rungrat T., Murray K.D., Trtilek M., Furbank R.T., Badger M., Pogson B.J., and Borevitz J.O. 2014: TraitCapture: genomic and environment modelling of plant phenomic data. Current Opinion in Plant Biology 18: pp. 73-79.

45.Mariam Awlia, et.al, 2016, High-Throughput Non-destructive Phenotyping of Traits that Contribute to Salinity Tolerance in Arabidopsis thaliana, Frontiers in Plant Science, DOI: 10.3389/fpls.2016.01414

46.Ivan Simko, et.al, 2016, Phenomic approaches and tools for phytopathologists, Phytopathology, DOI: 10.1094/PHYTO-02-16-0082-RVW

47.Tepsuda Rungrat, et.al, 2016, Using Phenomic Analysis of Photosynthetic Function for Abiotic Stress Response Gene Discovery, The Arabidopsis Book 14: e0185, The American Society of Plant Biologists, DOI: http://dx.doi.org/10.1199/tab.0185

48.Jorge Marques da Silva, 2016, Monitoring Photosynthesis by In Vivo Chlorophyll Fluorescence: Application to High-Throughput Plant Phenotyping, Applied Photosynthesis - New Progress, Edition 1, Chapter 1, pp:3-22, DOI: http://dx.doi.org/10.5772/62391

49.Maxwell S. Bush, et.al, 2016, eIF4A RNA Helicase Associates with Cyclin-Dependent Protein Kinase A in Proliferating Cells and is Modulated by Phosphorylation. Plant Physiol., DOI: 10.1104/pp.16.00435

50.?ngela María Sánchez-López, et.al, 2016, Volatile compounds emitted by diverse phytopathogenic microorganisms promote plant growth and flowering through cytokinin action, Plant, Cell and Environment, DOI: 10.1111/pce.12759

51.Jan Humplík, et.al, 2015, Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses – a review, Plant Methods, 11: 29

52.Jan Humplík, et.al, 2015, Automated integrative high-throughput phenotyping of plant shoots: a case study of the cold-tolerance of pea Pisum sativum L., Plant Methods, 11: 20

53.Pip Wilson, et.al, 2015, Genomic Diversity and Climate Adaptation in Brachypodium, Chapter Genetics and Genomics of Brachypodium, Volume 18 of the series Plant Genetics and Genomics: Crops and Models, pp:107-127

54.Tim Brown, et.al, 2014, TraitCapture: genomic and environment modelling of plant phenomic data, Current Opinion in Plant Biology, 18: 73-79

55. Jan Humplík, et.al, 2014, High-throughput plant phenntyping facility in Palacky University in Olomouc, International Symposium on Auxins and Cytokinins in Plant Development

 

附:其它表型剖析平台:

1、FKM多光谱荧光动态显微成像系统

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右图引自《Nature Plants》2016, Photonic multilayer structure of Begonia chloroplasts enhances photosynthetic efficiency by Heather M. Whitney等

 

2、PlantScreen-R移动式表型剖析平台(下左图):用于大田植物叶绿素荧光成像剖析、RGB成像剖析、红外热成像剖析、3D激光扫描丈量剖析等

 

image.png

 

3、PlantScreen台式及移动式植物表型剖析平台(拜见上右图)

1)3D RGB彩色成像剖析

2)FluorCam叶绿素荧光成像剖析

3)FluorCam多光谱荧光成像剖析

4)高光谱成像剖析

5)红外热成像剖析

6)PAR吸收/NDVI成像剖析

7)近红外3D成像剖析

 

4、PlantScreen样带式表型剖析平台

 

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5、PlantScreen 植物表型三维自动扫描成像剖析平台

 

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