PlantScreen高通量植物表型成像分析平臺（傳送帶版）（二）

10.根系成像分析

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

·不僅可對根系成像分析，還可對地上苗（shoot）進行成像分析，苗高大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（選配），3個水箱獨立運行

·測量參數包括：根深（或高度）、根冠寬度、高度與寬度比值、根冠面積、根冠緊實度、根系總長、軸對稱性、根尖數、根節數等

11.自動澆灌與稱重單元

·測量參數：實際重量、澆水體積、終重量、每個培養盆的相對重量

·操作指令：每個培養盆澆相同量的水（克數或者實際重量的百分比）；保持相對重量；自定義每個培養盆的澆灌量模擬不同干旱或者內澇脅迫；稱重前自動零校準，還可通過已知重量（如砝碼）物品自動進行再校準

·每個培養盆的澆水量、日期、時間可分別程序控制記錄以創建不同干旱脅迫梯度等，并且與整個系統的表型大數據無縫結合分析

·稱重精度：大型植物±2g，小型植物±0.2g

·澆灌單元：流速3L/min，澆灌口高度可自動上下前后調整，保證澆灌位置

12.自動化植物傳送系統

·傳送植物大小：根據客戶需求，可達200cm

·傳送帶容納量：50盆植物（1000株小型植物），可擴展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分鐘可以完成整個系統載荷植物樣品的表型分析，可隨機傳送至成像室進行成像分析、隨機澆灌

·培養盆：防UV聚丙烯材料，標準5L（口徑24cm）培養盆，可通過適配器應用3L培養盆，可360度旋轉

·具備手動載樣環（manual loading loop）以便在系統待機模式下手動載樣分析實驗、小組實驗分析等

·具備激光植物高度測量監測系統和*

·環形傳送通道：具變速箱的三相異步馬達，功率200-1000W，大負載500kg，速度150mm/s，傳送帶材料為防UV高耐用PVC

·移動控制系統：*處理單元CJ2M-CPU33；數字輸入/輸出大2560點；輸入/輸出單元大40；溫度傳感器Pt1000，Pt100，PTC；PLC通訊百兆以太網；OMRON MECHATROLINK-II 大16軸精確定位

·RFID標簽和QR植物辨識系統，自動讀取每個樣品托盤上的二維編碼；辨識距離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

·環境監測傳感器：溫濕度傳感器、PAR光合有效輻射傳感器

·由主控制系統分別自動調控每一個樣品托盤的測量時間、測量順序、測量參數、澆灌時間和澆灌量，從測量單元到培養室的樣品運轉整個過程可實現*自動控制，在無人值守情況下根據預設程序自行完成全部實驗測量工作。

13.主控制表型大數據平臺

·組成：控制調度服務器、客戶端應用服務器、數據服務器、可編程序邏輯控制器及專業分析軟件等，數據容量12TB

·自動控制與分析功能：具備用戶定義、可編輯自動測量程序（protocols），根據用戶設定程序自動完成全部實驗。數據結果自動存儲并分析，分析的數據結果可自動以動態曲線的形式顯示。

·MySQL數據庫管理系統，可以處理擁有上千萬條記錄的大型數據庫，支持多種存儲引擎，相關數據自動存儲于數據庫中的不同表中

·植物編碼注冊功能：包括植物識別碼、所在托盤的識別碼等存儲在數據庫中，測量時自動提取自動讀取條形碼或RFID標簽

·觸摸屏操作界面，在線顯示植物托盤數量、光線強度、分析測量狀態及結果等，輕松通過軟件*控制所有的機械部件和成像工作站

·可用默認程序進行所有測量，也可通過開發工具創建自定義的工作過程，或者手動操作LED光源開啟或關閉、RGB成像、葉綠素熒光成像、高光譜成像、紅外熱成像、3D激光掃描、稱重及澆灌等

·葉片跟蹤監測功能（leaf tracking）模塊，可以持續跟蹤監測葉片的生長、變化等等

·3D投射技術，可以通過高分辨率RGB鏡頭 或激光掃描構建3D模型，通過投射技術，將與其它傳感器所得數據如葉綠素熒光、紅外熱成像溫度數據、近紅外數據、高光譜數據等投射在3D模型上一起進行對比分析等

·允許用戶通過互聯網遠程訪問，進行數據處理、下載及更改實驗設計

·所測量的所有數據都是透明的、可以追溯的

·具備用戶權限分級功能，防止其他人員誤操作影響實驗

·廠家遠程故障診斷，軟件*升級

執行標準：

·CE認證標準

·CSN EN 60529 防護等級標準

·CSN 33 01 65 導體側識別標準

·CSN 33 2000-3 基礎特性標準

·CSN 33 2000-4-41ed.2 電擊保護標準

·CSN 33 2000-4-43 電源過載保護標準

·CSN 33 2000-5-51ed.2 通用規則標準

·CSN 33 2000-5-523 容許電流標準

·CSN 33 2000-5-54ed.2 接地與保護導體標準

·CSN EN 55011 工業、科學與醫學設備測量電磁干擾的范圍與方法

·2006/42/EG 機械指令標準

·73/23/EEG 低電壓指令標準

·2004/108/EG 電磁相容性指令標準

附：部分參考文獻

1.M. Sorrentino, G. Colla, Y. Rouphaelouphael, K. Panzarová, M. Trtílek. 2020. Lettuce reaction reaction to drought stress: automated high-throughput phenotyping of plant growth and photosynthetic performance. ISHS Acta Horticulturae 1268.

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 C*rs. 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–M*ria 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., Sania 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: /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: /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：

1、FKM多光譜熒光動態顯微成像系統

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

2、PlantScreen-R移動式表型分析平臺（下左圖）：用于大田植物葉綠素熒光成像分析、RGB成像分析、紅外熱成像分析、3D激光掃描測量分析等

3、PlantScreen臺式及移動式植物表型分析平臺（參見上右圖）

1)3D RGB彩色成像分析

2)FluorCam葉綠素熒光成像分析

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

5、PlantScreen 植物表型三維自動掃描成像分析平臺