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 PlantScreen高通量植物表型成像分析平臺(tái)（傳送帶版）（二）

 

10.根系成像分析

·RhizoTron根窗技術(shù)，全自動(dòng)成像分析，標(biāo)配根窗44x29.5x5.8cm（高x寬x厚度）

·不僅可對(duì)根系成像分析，還可對(duì)地上苗（shoot）進(jìn)行成像分析，苗高最大50cm

·新一代CMOS傳感器，分辨率12.3MP

·均一LED光源

·3層定位（頂部、中部、底部）根系澆灌系統(tǒng)（選配），3個(gè)水箱獨(dú)立運(yùn)行

·測(cè)量參數(shù)包括：根深（或高度）、根冠寬度、高度與寬度比值、根冠面積、根冠緊實(shí)度、根系總長(zhǎng)、軸對(duì)稱性、根尖數(shù)、根節(jié)數(shù)等

 

 

 

11.自動(dòng)澆灌與稱重單元

·測(cè)量參數(shù)：實(shí)際重量、澆水體積、最終重量、每個(gè)培養(yǎng)盆的相對(duì)重量

·操作指令：每個(gè)培養(yǎng)盆澆相同量的水（絕對(duì)克數(shù)或者實(shí)際重量的百分比）；保持相對(duì)重量；自定義每個(gè)培養(yǎng)盆的澆灌量模擬不同干旱或者內(nèi)澇脅迫；稱重前自動(dòng)零校準(zhǔn)，還可通過已知重量（如砝碼）物品自動(dòng)進(jìn)行再校準(zhǔn)

·每個(gè)培養(yǎng)盆的澆水量、日期、時(shí)間可分別程序控制記錄以創(chuàng)建不同干旱脅迫梯度等，并且與整個(gè)系統(tǒng)的表型大數(shù)據(jù)無縫結(jié)合分析

·稱重精度：大型植物±2g，小型植物±0.2g

·澆灌單元：流速3L/min，澆灌口高度可自動(dòng)上下前后調(diào)整，保證最佳澆灌位置

 

12.自動(dòng)化植物傳送系統(tǒng)

·傳送植物大小：根據(jù)客戶需求，最高可達(dá)200cm

·傳送帶容納量：50盆植物（1000株小型植物），可擴(kuò)展100盆、200盆、400盆等更大容量 ；表型分析通量依不同的protocol而定，100分鐘可以完成整個(gè)系統(tǒng)載荷植物樣品的表型分析，可隨機(jī)傳送至成像室進(jìn)行成像分析、隨機(jī)澆灌

·培養(yǎng)盆：防UV聚丙烯材料，標(biāo)準(zhǔn)5L（口徑24cm）培養(yǎng)盆，可通過適配器應(yīng)用3L培養(yǎng)盆，可360度旋轉(zhuǎn)

·具備手動(dòng)載樣環(huán)（manual loading loop）以便在系統(tǒng)待機(jī)模式下手動(dòng)載樣分析實(shí)驗(yàn)、小組實(shí)驗(yàn)分析等

·具備激光植物高度測(cè)量監(jiān)測(cè)系統(tǒng)和激光定位系統(tǒng)

·環(huán)形傳送通道：具變速箱的三相異步馬達(dá)，功率200-1000W，最大負(fù)載500kg，速度150mm/s，傳送帶材料為防UV高耐用PVC

·移動(dòng)控制系統(tǒng)：中央處理單元CJ2M-CPU33；數(shù)字輸入/輸出最大2560點(diǎn)；輸入/輸出單元最大40；溫度傳感器Pt1000，Pt100，PTC；PLC通訊百兆以太網(wǎng)；OMRON MECHATROLINK-II 最大16軸精確定位

·RFID標(biāo)簽和QR植物辨識(shí)系統(tǒng)，自動(dòng)讀取每個(gè)樣品托盤上的二維編碼；辨識(shí)距離2-20cm；通訊RS485；可讀取1維、2維和QR碼；配備LED光源便于弱光下辨識(shí)

·環(huán)境監(jiān)測(cè)傳感器：溫濕度傳感器、PAR光合有效輻射傳感器

·由主控制系統(tǒng)分別自動(dòng)調(diào)控每一個(gè)樣品托盤的測(cè)量時(shí)間、測(cè)量順序、測(cè)量參數(shù)、澆灌時(shí)間和澆灌量，從測(cè)量單元到培養(yǎng)室的樣品運(yùn)轉(zhuǎn)整個(gè)過程可實(shí)現(xiàn)完全自動(dòng)控制，在無人值守情況下根據(jù)預(yù)設(shè)程序自行完成全部實(shí)驗(yàn)測(cè)量工作。

 

13.主控制表型大數(shù)據(jù)平臺(tái)

·組成：控制調(diào)度服務(wù)器、客戶端應(yīng)用服務(wù)器、數(shù)據(jù)服務(wù)器、可編程序邏輯控制器及專業(yè)分析軟件等，數(shù)據(jù)容量12TB

·自動(dòng)控制與分析功能：具備用戶定義、可編輯自動(dòng)測(cè)量程序（protocols），根據(jù)用戶設(shè)定程序自動(dòng)完成全部實(shí)驗(yàn)。數(shù)據(jù)結(jié)果自動(dòng)存儲(chǔ)并分析，分析的數(shù)據(jù)結(jié)果可自動(dòng)以動(dòng)態(tài)曲線的形式顯示。

 

 

·MySQL數(shù)據(jù)庫管理系統(tǒng)，可以處理擁有上千萬條記錄的大型數(shù)據(jù)庫，支持多種存儲(chǔ)引擎，相關(guān)數(shù)據(jù)自動(dòng)存儲(chǔ)于數(shù)據(jù)庫中的不同表中

·植物編碼注冊(cè)功能：包括植物識(shí)別碼、所在托盤的識(shí)別碼等存儲(chǔ)在數(shù)據(jù)庫中，測(cè)量時(shí)自動(dòng)提取自動(dòng)讀取條形碼或RFID標(biāo)簽

·觸摸屏操作界面，在線顯示植物托盤數(shù)量、光線強(qiáng)度、分析測(cè)量狀態(tài)及結(jié)果等，輕松通過軟件完全控制所有的機(jī)械部件和成像工作站

·可用默認(rèn)程序進(jìn)行所有測(cè)量，也可通過開發(fā)工具創(chuàng)建自定義的工作過程，或者手動(dòng)操作LED光源開啟或關(guān)閉、RGB成像、葉綠素?zé)晒獬上?、高光譜成像、紅外熱成像、3D激光掃描、稱重及澆灌等

·葉片跟蹤監(jiān)測(cè)功能（leaf tracking）模塊，可以持續(xù)跟蹤監(jiān)測(cè)葉片的生長(zhǎng)、變化等等

·3D投射技術(shù)，可以通過高分辨率RGB鏡頭 或激光掃描構(gòu)建3D模型，通過投射技術(shù)，將與其它傳感器所得數(shù)據(jù)如葉綠素?zé)晒狻⒓t外熱成像溫度數(shù)據(jù)、近紅外數(shù)據(jù)、高光譜數(shù)據(jù)等投射在3D模型上一起進(jìn)行對(duì)比分析等

·允許用戶通過互聯(lián)網(wǎng)遠(yuǎn)程訪問，進(jìn)行數(shù)據(jù)處理、下載及更改實(shí)驗(yàn)設(shè)計(jì)

·所測(cè)量的所有數(shù)據(jù)都是透明的、可以追溯的

·具備用戶權(quán)限分級(jí)功能，防止其他人員誤操作影響實(shí)驗(yàn)

·廠家遠(yuǎn)程故障診斷，軟件終身免費(fèi)升級(jí)

 

 

執(zhí)行標(biāo)準(zhǔn)：

·CE認(rèn)證標(biāo)準(zhǔn)

·CSN EN 60529 防護(hù)等級(jí)標(biāo)準(zhǔn)

·CSN 33 01 65 導(dǎo)體側(cè)識(shí)別標(biāo)準(zhǔn)

·CSN 33 2000-3 基礎(chǔ)特性標(biāo)準(zhǔn)

·CSN 33 2000-4-41ed.2 電擊保護(hù)標(biāo)準(zhǔn)

·CSN 33 2000-4-43 電源過載保護(hù)標(biāo)準(zhǔn)

·CSN 33 2000-5-51ed.2 通用規(guī)則標(biāo)準(zhǔn)

·CSN 33 2000-5-523 容許電流標(biāo)準(zhǔn)

·CSN 33 2000-5-54ed.2 接地與保護(hù)導(dǎo)體標(biāo)準(zhǔn)

·CSN EN 55011 工業(yè)、科學(xué)與醫(yī)學(xué)設(shè)備測(cè)量電磁干擾的范圍與方法

·2006/42/EG 機(jī)械指令標(biāo)準(zhǔn)

·73/23/EEG 低電壓指令標(biāo)準(zhǔn)

·2004/108/EG 電磁相容性指令標(biāo)準(zhǔn)

 

附：部分參考文獻(xiàn)

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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., Hybl, 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, á. 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, Hylová 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.ángela 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：其它表型分析平臺(tái)：

1、FKM多光譜熒光動(dòng)態(tài)顯微成像系統(tǒng)

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

 

2、PlantScreen-R移動(dòng)式表型分析平臺(tái)（下左圖）：用于大田植物葉綠素?zé)晒獬上穹治?、RGB成像分析、紅外熱成像分析、3D激光掃描測(cè)量分析等

 

 

3、PlantScreen臺(tái)式及移動(dòng)式植物表型分析平臺(tái)（參見上右圖）

1)3D RGB彩色成像分析

2)FluorCam葉綠素?zé)晒獬上穹治?/span>

3)FluorCam多光譜熒光成像分析

4)高光譜成像分析

5)紅外熱成像分析

6)PAR吸收／NDVI成像分析

7)近紅外3D成像分析

 

4、PlantScreen樣帶式表型分析平臺(tái)

 

 

5、PlantScreen 植物表型三維自動(dòng)掃描成像分析平臺(tái)