南宫NG28

 

 PlantScreen高通量植物表型成像分析平台（传送带版）（二）

 

10.根系成像分析

·RhizoTron根窗技术，全自动成像分析，标配根窗44x29.5x5.8cm（高x宽x厚度）

·不仅可对根系成像分析，还可对地上苗（shoot）进行成像分析，苗高最大50cm

·新一代CMOS传感器，分辨率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个水箱独立运行

·丈量参数蕴含：根深（或高度）、根冠宽度、高杜纂宽度比值、根冠面积、根冠紧实度、根系总长、轴对称性、根尖数、根节数等

 

 

 

11.自动浇灌与称沉单元

·丈量参数：现实沉量、浇水体积、最终沉量、每个造就盆的相对沉量

·操作指令：每个造就盆浇一样量的水（绝对克数或者现实沉量的百分比）；维持相对沉量；自界说每个造就盆的浇灌量仿照分歧干旱或者内涝胁迫；称沉前自动零校准，还可通过已知沉量（如砝码）物品自动进行再校准

·每个造就盆的浇水量、日期、功夫可别离法式节造纪录以创建分歧干旱胁迫梯度等，并且与整个系统的表型大数据无缝结合分析

·称沉精度：大型植物±2g，幼型植物±0.2g

·浇灌单元：流速3L/min，浇灌口高度可自动高低前后调整，保障最佳浇灌地位

 

12.自动化植物传送系统

·传送植物大。浩揪菘突枰，最高可达200cm

·传送带包容量：50盆植物（1000株幼型植物），可扩大100盆、200盆、400盆等更大容量 ；表型分析通量依分歧的protocol而定，100分钟能够实现整个系统载荷植物样品的表型分析，可随机传送至成像室进行成像分析、随机浇灌

·造就盆：防UV聚丙烯资料，尺度5L（口径24cm）造就盆，可通过适配器利用3L造就盆，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分析尝试、幼组尝试分析等

·具备激光植物高度丈量监测系统和激光定位系统

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

·移动节造系统：中央处置单元CJ2M-CPU33；数字输入/输出最大2560点；输入/输出单元最大40；温度传感器Pt1000，Pt100，PTC；PLC通讯百兆以太网；OMRON MECHATROLINK-II 最大16轴精确定位

·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；建设LED光源便于弱光下辨识

·环境监测传感器：温湿度传感器、PAR光合有效辐射传感器

·由主节造系统别离自动调控每一个样品托盘的丈量功夫、丈量挨次、丈量参数、浇灌功夫和浇灌量，从丈量单元到造就室的样品运行整个过程可实现齐全自动节造，在无人值守情况下凭据预设法式自行完玉成数尝试丈量工作。

 

13.主节造表型大数据平台

·组成：节造调度服务器、客户端利用服务器、数据服务器、可编法式逻辑节造器及专业分析软件等，数据容量12TB

·自动节造与分析职能：具备用户界说、可编纂自动丈量法式（protocols），凭据用户设定法式自动完玉成数尝试。数据了局自动存储并分析，分析的数据了局可自动以动态曲线的大局显示。

 

 

·MySQL数据库治理系统，能够处置占有上千万笔纪录的大型数据库，支持多种存储引擎，有关数据自动存储于数据库中的分歧表中

·植物编码注册职能：蕴含植物鉴别码、地点托盘的鉴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量、光线强度、分析丈量状态及了局等，轻松通过软件齐全节造所有的机械部件和成像工作站

·可用默认法式进行所有丈量，也可通过开发工具创建自界说的工作过程，或者手动操作LED光源开启或关关、RGB成像、叶绿素荧光成像、高光谱成像、红表热成像、3D激光扫描、称沉及浇灌等

·叶片跟踪监测职能（leaf tracking）？，能够持续跟踪监测叶片的成长、变动等等

·3D投射技术，能够通过高分辨率RGB镜头 或激光扫描构建3D模型，通过投射技术，将与其它传感器所得数据如叶绿素荧光、红表热成像温度数据、近红表数据、高光谱数据等投射在3D模型上一路进行对比分析等

·允许用户通过互联网远程接见，进行数据处置、下载及更改尝试设计

·所丈量的所罕见据都是通明的、能够追忆的

·具备用户权限分级职能，预防其他人员误操作影响尝试

·厂家远程故障诊断，软件平生免费升级

 

 

执行尺度：

·CE认证尺度

·CSN EN 60529 防护等级尺度

·CSN 33 01 65 导体侧鉴别尺度

·CSN 33 2000-3 基础个性尺度

·CSN 33 2000-4-41ed.2 电击；こ叨

·CSN 33 2000-4-43 电源过载；こ叨

·CSN 33 2000-5-51ed.2 通用规定尺度

·CSN 33 2000-5-523 答理电流尺度

·CSN 33 2000-5-54ed.2 接地与；さ继宄叨

·CSN EN 55011 工业、科学与医学设备丈量电磁滋扰的领域与步骤

·2006/42/EG 机械指令尺度

·73/23/EEG 低电压指令尺度

·2004/108/EG 电磁相容性指令尺度

 

附：部门参考文件

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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.

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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：其它表型分析平台：

1、FKM多光谱荧光动态显微成像系统

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

 

2、PlantScreen-R移动式表型分析平台（下左图）：用于大田植物叶绿素荧光成像分析、RGB成像分析、红表热成像分析、3D激光扫描丈量分析等

 

 

3、PlantScreen台式及移动式植物表型分析平台（拜见上右图）

1)3D RGB彩色成像分析

2)FluorCam叶绿素荧光成像分析

3)FluorCam多光谱荧光成像分析

4)高光谱成像分析

5)红表热成像分析

6)PAR吸收／NDVI成像分析

7)近红表3D成像分析

 

4、PlantScreen样带式表型分析平台

 

 

5、PlantScreen 植物表型三维自动扫描成像分析平台