目錄:北京易科泰生態技術有限公司>>植物類>>植物表型>> PlantScreen高通量植物表型成像分析平臺
產地類別 | 進口 | 應用領域 | 食品,生物產業,農業 |
---|
PlantScreen高通量植物表型成像分析平臺由公司PSI公司研制生產,整合了LED植物智能培養、自動化控制系統、葉綠素熒光成像測量分析(可擴展多光譜熒光成像)、植物熱成像分析、植物近紅外成像分析、RGB真彩3D成像、高光譜成像、3D激光掃描成像分析、RhizoTron根系成像分析、自動條碼識別管理、自動稱重與澆灌系統等多項*進技術。PlantScreen高通量植物表型成像分析平臺以優化的方式實現大量植物樣品的多方位生理功能與形態結構自動成像分析,用于玉米、水稻、小麥、大豆及椰樹等熱帶作物高通量表型成像分析測量、脅迫響應成像分析測量、生長分析測量、生態毒理學研究、性狀識別、抗性篩選、作物遺傳育種及植物生理生態分析研究等。
PlantScreen技術特點:
1.模塊式結構,配置靈活,可選配不同的功能模塊,系統具備強大的可擴展性
2.的FluorCam葉綠素熒光成像技術,是作物生理生態功能性狀的*分析技術,配備*的高靈敏度葉綠素熒光成像鏡頭,成像面積可選配35cm x 35cm或80cm x 80cm
3.可選配不同的表型成像分析模塊:
1)葉綠素熒光成像單元,單幅成像面積35cm x 35cm或選配80cm x 80cm
2)多激發光、多光譜熒光成像模塊,包括GFP等熒光蛋白成像、多光譜熒光成像分析等
3)3D RGB可見光成像分析單元,包括頂部和側面兩個高分辨率RGB鏡頭、0-360度旋轉平臺、光源燈
4)高光譜成像分析單元,有VNIR高光譜和SWIR高光譜供選配
5)紅外熱成像分析單元(標配頂部2維成像分析,可選配頂部與側面3D成像分析),用于對植物干旱脅迫、氣孔導度成像分析
6)3D激光掃描單元,用于對作物3D點云模型和形態結構分析,PSI專業技術,可以把葉綠素熒光成像、高光譜成像等投射到3D點云模型上進行3D分析、作物生長模型研究等
7)根系成像分析單元,RhizoTron根窗技術
8)NIR(近紅外)成像單元,用于對植物水分狀態分析,可選配3D近紅外成像
9)自動稱重與澆灌系統
4.世界*創的智能LED光適應室,確保作物表型成像分析前穩定可比的光適應和暗適應
5.Shoot & Root Phenotyping全面分析植物表型
6.植物傳送系統可根據客戶需求定制、擴展
7.客戶定制智能LED溫室或作物生長室(選配),可模擬晝夜節律、多云天氣等,傳送系統可自動將植物從生長室中傳送至光適應室然后進入成像室進行成像分析,并遠程在線瀏覽分析
8.功能強大的操作系統及作物表型大數據平臺,具備葉片跟蹤監測功能、3D投射功能
9.PSI表型研究中心專家團隊技術支持,每年在美國和歐洲分別組織舉辦一次世界植物表型研討會
植物表型分析技術應用情況
作為一家研制生產FluorCam植物葉綠素熒光成像系統的廠家,PSI公司在植物表型成像分析領域處于的技術前列,其FluorCam葉綠素熒光成像系統先應用于植物表型分析研究,代表性論文如Celine Rousseau等(High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysis, Plant Methods 2013)。在FluorCam技術基礎上集成RGB 3D成像分析、高光譜成像分析、近紅外成像分析、紅外熱成像分析及激光雷達掃描分析等*進技術的PlantScreen全自動高通量植物表型成像分析平臺,成為目前世界上*進的表型組學和作物遺傳育種研究設備(應用案例另附)。
系統配置與工作原理:
整套系統由自動化植物傳送系統、光適應室、FluorCam葉綠素熒光成像、RGB成像、高光譜成像、根系成像、植物紅外熱成像、植物近紅外成像、自動澆灌與稱重系統、植物標識系統、控制系統及表型大數據平臺等組成,溫室或生長室內植物通過自動識別傳送系統運送到光適應室內,然后進行必要的澆灌稱重,再由傳送帶到成像室進行成像分析等,后植物自動返回原位。系統服務器及數據分析平臺在線采集分析并自動存儲至數據庫系統
技術指標:
1. 光適應室:
·對作物成像分析前進行均一穩定的光適應或暗適應,以確保植物表型分析數據的可靠性
·智能冷白LED(6500K)+遠紅LED(735nm)光源,對植物無輻射升溫效應,光強1000 μmoles /m2/s 0-100%(步進增幅1%)可調
·適應室內由通風系統保持空氣交流通風
·具備植物高度激光監測系統,以根據高度調整成像高度等
·具備激光定位系統,以調整控制植物移動與成像程序(imaging protocols)的同步性
·垂直簾門確保與環境光線及成像系統的隔離
·具備IP監測鏡頭以始終保持對系統運行和植物移動狀況的監視
·規格容量8盆/培養托
2.RGB 3D結構成像分析單元?
a)2個高分辨率RGB鏡頭(頂部和側面),新一代CMOS彩色傳感器,分辨率12.8Mpix(4096x3000),像素大小3.45µm
b)成像高度可客戶定義或設置,范圍0-1050mm,精確度3mm
c)360度旋轉平臺、LED均一光源照明
d)數據傳輸:千兆以太網
e)測量參數:葉面積、植物緊實度/緊密度、葉片周長、偏心率、葉圓度、葉寬指數、植物圓直徑、凸包面積、植物質心、生長高度、植物高度和寬度、相對生長速率等
f)可進行顏色分割分析、植物適合度評價、實驗生長期葉面積動態變化比較分析、綠度指數、顏色分級分析(健康綠色、亮綠色、暗綠色、其他顏色)等表型參數
3.FluorCam葉綠素熒光成像單元
a)成像面積:35×35cm或選配80x80cm
b)橙色620nm LED脈沖調制測量光源
c)雙色光化學光,橙色620nm LED和冷白LED光源
d)冷白LED飽和光閃,大光強4000 µmol(photons)/m².s
e)735nm LED紅外光源用于測量Fo’等
f)可選配藍色光源與7位濾波輪,用于GFP穩態熒光測量
g)高靈敏度葉綠素熒光成像專業CCD傳感器,1.4M分辨率, A/D 16比特,具備視頻模式和快照模式
h)測量參數:Fo、Fm、Fv、Fo'、Fm'、Fv'、Ft、Fv/Fm、Fv'/Fm'、PhiPSII、NPQ、qN、qP、Rfd、ETR等,用于分析植物光合效率、適合度、生物與非生物脅迫及作物抗性、恢復力等
i)Fv/Fm、Kautsky誘導效應、熒光淬滅分析等完備自動化測量程序(protocols)與測量參數,如Fv/Fm程序測量時間僅需10s
j)葉綠素熒光數據在線分析,包括柱狀圖、測量參數圖、數據表格等,具備自定義圖像分割等功能
4. 多光譜熒光成像模塊
·不僅可以運行PAM葉綠素熒光成像,還可以進行GFP/YFP等熒光蛋白成像、多光譜熒光成像
·9種LED激發光源:UV(365nm)、青色光源(440nm)、藍色光源(470nm)、綠色光源(530nm)、琥珀色光源(590nm)、橙色光源(630nm)、深紅色光源(660nm)、遠紅光源(730nm)及冷白光源(5700K)
·可成像分析多酚類(黃酮醇類、花青素等)、N素指數等
·分辨率1360x1024像素,binning 2x2、680x512像素
5. 紅外熱成像單元
·成像傳感器:焦平面陣列微測熱輻射計,分辨率 640×480 像素,靈敏度30mK(0.03°C),波段7.5-13μm;
·可選配高分辨率紅外熱成像,分辨率可達1024x768像素,靈敏度20mK(0.02°C)
·溫度范圍 -20 – 120℃,分辨率<0.03℃@30℃/30mK
·成像光源:冷白LED光源板,用于給測量植物提供穩定熱環境,6500K,大光強 1000 µmol(photons)/m².s,0-100%可調
·具備溫度動態Protocols,光照強度、持續時間、熱成像分布數據同步獲取,以研究分析植物溫度分布動態等
·具備溫度參考傳感器(reference sensors)
·測量參數:植物每一點的實際溫度,植物表面溫度分布圖
·專業分析軟件用于數據獲取、分析、存儲等
6. NIR成像分析單元(選配):
·用于成像監測分析植物水分狀態分布,具備假彩調色板,可以方便對比分析,快速監測脫水植物,因而可以監測評估干旱脅迫條件下植物水分的動態變化響應及水分利用效率等
·可與RGB成像形態結構參數及FluorCam光合效率參數進行相關分析等;可完整記錄追溯干旱過程與復水過程的動態響應等
·通過測量水分吸收光譜和940nm參考光譜,有效避免環境光及陰影效應
·InGaAs傳感器,有效芯片大小9.6x7.7mm,波段范圍900-1700nm,分辨率638x510像素,幀頻118fps,A/D 14比特
·可選配頂部與側面雙鏡頭三維成像分析
·選配根系成像分析單元,以對根系進行近紅外成像分析
7. 可見光-近紅外高光譜成像單元
· 成像波長范圍:400-950nm(或350-900nm)
·成像傳感器:推掃式線性掃描傳感器,配備掃描光源
·像素色散:0.28nm/pixel
·光譜分辨率0.8nm FWHM
·光譜帶數(波段數):1920個波段
·空間分辨率:1000
·入射狹縫寬度:25μm
·幀頻:45fps
·CMOS檢測器,光圈F/2.0,GigE網絡接口
·自動參考校準,線性掃描,高度可調
·測量參數:每個波段的反射光譜成像圖及全光譜曲線,并可自動計算以下植被指數:歸一化指數NDVI、簡單比值指數SR、改進的葉綠素吸收反射指數MCARI、改進的葉綠素吸收反射指數1MCARI1、優化土壤調整植被指數OSAVI、綠度指數G、轉換類胡羅卜素指數TCARI、三角植被指數TVI、ZMI指數、簡單比值色素指數SRPI、歸一化脫鎂作用指數NPQI、光化學植被反射指數PRI、歸一化葉綠素指數NPCI、Carter指數、Lichtenthaler指數、SIPI指數、Gitelson-Merzlyak指數、花青素反射指數等等
8. 短波紅外高光譜成像單元
·成像波長范圍:900-1700nm
·成像傳感器:推掃式線性掃描傳感器,配備掃描光源
·光譜分辨率:2nm(FWHM)
·光譜帶數:510個波段
·空間分辨率636
·測量參數:每個波段的反射光譜成像圖及全光譜曲線,無損測量植物整體及不同部位水分含量變化(右圖中藍色越深含水量越高)
9. 3D激光掃描單元:
·頂部與側面激光掃描,660nm激光,用于植物精確3D模型構建,分辨率低于1mm
·頂部掃描距離60cm,客戶定義側面掃描距離
·3D點云模型,RGB成像、葉綠素熒光成像數據等可與3D模型疊加分析
·植物結構、生物量、葉片數量、葉面積、葉片傾斜角度、植物高度等結構形態參數
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,高可達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激光定位系統
·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:中央處理單元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., Hýbl, 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., Pospíšil, P., & Špundová, 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., Muñoz, 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, Hýlová A, Podlešáková 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., Schmöckel S.M., Negrão 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,
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
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