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PlantScreen高通量植物表型成像分析平臺(tái)
更新時(shí)間:2020-04-22
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PlantScreen高通量植物表型成像分析平臺(tái)由公司PSI公司研制生產(chǎn),整合了LED植物智能培養(yǎng)、自動(dòng)化控制系統(tǒng)、葉綠素?zé)晒獬上駵y(cè)量分析(可擴(kuò)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(dòng)條碼識(shí)別管理、自動(dòng)稱(chēng)重與澆灌系統(tǒng)等多項(xiàng)*進(jìn)技術(shù)。
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 PlantScreen高通量植物表型成像分析平臺(tái)由公司PSI公司研制生產(chǎn),整合了LED植物智能培養(yǎng)、自動(dòng)化控制系統(tǒng)、葉綠素?zé)晒獬上駵y(cè)量分析(可擴(kuò)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(dòng)條碼識(shí)別管理、自動(dòng)稱(chēng)重與澆灌系統(tǒng)等多項(xiàng)*進(jìn)技術(shù)。PlantScreen高通量植物表型成像分析平臺(tái)以?xún)?yōu)化的方式實(shí)現(xiàn)大量植物樣品的多方位生理功能與形態(tài)結(jié)構(gòu)自動(dòng)成像分析,用于玉米、水稻、小麥、大豆及椰樹(shù)等熱帶作物高通量表型成像分析測(cè)量、脅迫響應(yīng)成像分析測(cè)量、生長(zhǎng)分析測(cè)量、生態(tài)毒理學(xué)研究、性狀識(shí)別、抗性篩選、作物遺傳育種及植物生理生態(tài)分析研究等。

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PlantScreen技術(shù)特點(diǎn):

1.模塊式結(jié)構(gòu),配置靈活,可選配不同的功能模塊,系統(tǒng)具備強(qiáng)大的可擴(kuò)展性

2.的FluorCam葉綠素?zé)晒獬上窦夹g(shù),是作物生理生態(tài)功能性狀的*分析技術(shù),配備*的高靈敏度葉綠素?zé)晒獬上耒R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?zé)晒獬上駟卧?,單幅成像面積35cm x 35cm或選配80cm x 80cm

2)多激發(fā)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(jiàn)光成像分析單元,包括頂部和側(cè)面兩個(gè)高分辨率RGB鏡頭、0-360度旋轉(zhuǎn)平臺(tái)、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(biāo)配頂部2維成像分析,可選配頂部與側(cè)面3D成像分析),用于對(duì)植物干旱脅迫、氣孔導(dǎo)度成像分析

6)3D激光掃描單元,用于對(duì)作物3D點(diǎn)云模型和形態(tài)結(jié)構(gòu)分析,PSI專(zhuān)業(yè)技術(shù),可以把葉綠素?zé)晒獬上瘛⒏吖庾V成像等投射到3D點(diǎn)云模型上進(jìn)行3D分析、作物生長(zhǎng)模型研究等

7)根系成像分析單元,RhizoTron根窗技術(shù)

8)NIR(近紅外)成像單元,用于對(duì)植物水分狀態(tài)分析,可選配3D近紅外成像

9)自動(dòng)稱(chēng)重與澆灌系統(tǒng)

image.png

4.世界*創(chuàng)的智能LED光適應(yīng)室,確保作物表型成像分析前穩(wěn)定可比的光適應(yīng)和暗適應(yīng)

5.Shoot & Root Phenotyping全面分析植物表型

6.植物傳送系統(tǒng)可根據(jù)客戶(hù)需求定制、擴(kuò)展

7.客戶(hù)定制智能LED溫室或作物生長(zhǎng)室(選配),可模擬晝夜節(jié)律、多云天氣等,傳送系統(tǒng)可自動(dòng)將植物從生長(zhǎng)室中傳送至光適應(yīng)室然后進(jìn)入成像室進(jìn)行成像分析,并遠(yuǎn)程在線(xiàn)瀏覽分析

8.功能強(qiáng)大的操作系統(tǒng)及作物表型大數(shù)據(jù)平臺(tái),具備葉片跟蹤監(jiān)測(cè)功能、3D投射功能

9.PSI表型研究中心專(zhuān)家團(tuán)隊(duì)技術(shù)支持,每年在美國(guó)和歐洲分別組織舉辦一次世界植物表型研討會(huì)

植物表型分析技術(shù)應(yīng)用情況

作為一家研制生產(chǎn)FluorCam植物葉綠素?zé)晒獬上裣到y(tǒng)的廠(chǎng)家,PSI公司在植物表型成像分析領(lǐng)域處于的技術(shù)前列,其FluorCam葉綠素?zé)晒獬上裣到y(tǒng)先應(yīng)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(shù)基礎(chǔ)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(dá)掃描分析等*進(jìn)技術(shù)的PlantScreen全自動(dòng)高通量植物表型成像分析平臺(tái),成為目前世界上*進(jìn)的表型組學(xué)和作物遺傳育種研究設(shè)備(應(yīng)用案例另附)。

系統(tǒng)配置與工作原理:

整套系統(tǒng)由自動(dòng)化植物傳送系統(tǒng)、光適應(yīng)室、FluorCam葉綠素?zé)晒獬上瘛GB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(dòng)澆灌與稱(chēng)重系統(tǒng)、植物標(biāo)識(shí)系統(tǒng)、控制系統(tǒng)及表型大數(shù)據(jù)平臺(tái)等組成,溫室或生長(zhǎng)室內(nèi)植物通過(guò)自動(dòng)識(shí)別傳送系統(tǒng)運(yùn)送到光適應(yīng)室內(nèi),然后進(jìn)行必要的澆灌稱(chēng)重,再由傳送帶到成像室進(jìn)行成像分析等,后植物自動(dòng)返回原位。系統(tǒng)服務(wù)器及數(shù)據(jù)分析平臺(tái)在線(xiàn)采集分析并自動(dòng)存儲(chǔ)至數(shù)據(jù)庫(kù)系統(tǒng)

image.png

技術(shù)指標(biāo):

1. image.png光適應(yīng)室:

·對(duì)作物成像分析前進(jìn)行均一穩(wěn)定的光適應(yīng)或暗適應(yīng),以確保植物表型分析數(shù)據(jù)的可靠性

·智能冷白LED(6500K)+遠(yuǎn)紅LED(735nm)光源,對(duì)植物無(wú)輻射升溫效應(yīng),光強(qiáng)1000 μmoles /m2/s 0-100%(步進(jìn)增幅1%)可調(diào)

·適應(yīng)室內(nèi)由通風(fēng)系統(tǒng)保持空氣交流通風(fēng)

·具備植物高度激光監(jiān)測(cè)系統(tǒng),以根據(jù)高度調(diào)整成像高度等

·具備激光定位系統(tǒng),以調(diào)整控制植物移動(dòng)與成像程序(imaging protocols)的同步性

·垂直簾門(mén)確保與環(huán)境光線(xiàn)及成像系統(tǒng)的隔離

·具備IP監(jiān)測(cè)鏡頭以始終保持對(duì)系統(tǒng)運(yùn)行和植物移動(dòng)狀況的監(jiān)視

·規(guī)格容量8盆/培養(yǎng)托

2.RGB 3D結(jié)構(gòu)成像分析單元?

a)2個(gè)高分辨率RGB鏡頭(頂部和側(cè)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(hù)定義或設(shè)置,范圍0-1050mm,精確度3mm

c)360度旋轉(zhuǎn)平臺(tái)、LED均一光源照明

d)數(shù)據(jù)傳輸:千兆以太網(wǎng)

e)測(cè)量參數(shù):葉面積、植物緊實(shí)度/緊密度、葉片周長(zhǎng)、偏心率、葉圓度、葉寬指數(shù)、植物圓直徑、凸包面積、植物質(zhì)心、生長(zhǎng)高度、植物高度和寬度、相對(duì)生長(zhǎng)速率等

f)可進(jìn)行顏色分割分析、植物適合度評(píng)價(jià)、實(shí)驗(yàn)生長(zhǎng)期葉面積動(dòng)態(tài)變化比較分析、綠度指數(shù)、顏色分級(jí)分析(健康綠色、亮綠色、暗綠色、其他顏色)等表型參數(shù)

image.png

3.FluorCam葉綠素?zé)晒獬上駟卧?/span>

a)成像面積:35×35cm或選配80x80cm

b)橙色620nm LED脈沖調(diào)制測(cè)量光源

c)雙色光化學(xué)光,橙色620nm LED和冷白LED光源

d)冷白LED飽和光閃,大光強(qiáng)4000 µmol(photons)/m².s

e)735nm LED紅外光源用于測(cè)量Fo’等

f)可選配藍(lán)色光源與7位濾波輪,用于GFP穩(wěn)態(tài)熒光測(cè)量

g)高靈敏度葉綠素?zé)晒獬上駥?zhuān)業(yè)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(cè)量參數(shù):Fo、Fm、Fv、Fo'Fm'、Fv'、Ft、Fv/FmFv'/Fm'、PhiPSII、NPQ、qNqP、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(fù)力等

i)Fv/Fm、Kautsky誘導(dǎo)效應(yīng)、熒光淬滅分析等完備自動(dòng)化測(cè)量程序(protocols)與測(cè)量參數(shù),如Fv/Fm程序測(cè)量時(shí)間僅需10s

j)葉綠素?zé)晒鈹?shù)據(jù)在線(xiàn)分析,包括柱狀圖、測(cè)量參數(shù)圖、數(shù)據(jù)表格等,具備自定義圖像分割等功能

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4. 多光譜熒光成像模塊

·不僅可以運(yùn)行PAM葉綠素?zé)晒獬上?,還可以進(jìn)行GFP/YFP等熒光蛋白成像、多光譜熒光成像

·9種LED激發(fā)光源:UV(365nm)、青色光源(440nm)、藍(lán)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(yuǎn)紅光源(730nm)及冷白光源(5700K)

·可成像分析多酚類(lèi)(黃酮醇類(lèi)、花青素等)、N素指數(shù)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(cè)熱輻射計(jì),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(dá)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(cè)量植物提供穩(wěn)定熱環(huán)境,6500K,大光強(qiáng) 1000 µmol(photons)/m².s,0-100%可調(diào)

·具備溫度動(dòng)態(tài)Protocols,光照強(qiáng)度、持續(xù)時(shí)間、熱成像分布數(shù)據(jù)同步獲取,以研究分析植物溫度分布動(dòng)態(tài)等

·具備溫度參考傳感器(reference sensors)

·測(cè)量參數(shù):植物每一點(diǎn)的實(shí)際溫度,植物表面溫度分布圖

·專(zhuān)業(yè)分析軟件用于數(shù)據(jù)獲取、分析、存儲(chǔ)等

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6. NIR成像分析單元(選配):

·用于成像監(jiān)測(cè)分析植物水分狀態(tài)分布,具備假彩調(diào)色板,可以方便對(duì)比分析,快速監(jiān)測(cè)脫水植物,因而可以監(jiān)測(cè)評(píng)估干旱脅迫條件下植物水分的動(dòng)態(tài)變化響應(yīng)及水分利用效率等

·可與RGB成像形態(tài)結(jié)構(gòu)參數(shù)及FluorCam光合效率參數(shù)進(jìn)行相關(guān)分析等;可完整記錄追溯干旱過(guò)程與復(fù)水過(guò)程的動(dòng)態(tài)響應(yīng)等

·通過(guò)測(cè)量水分吸收光譜和940nm參考光譜,有效避免環(huán)境光及陰影效應(yīng)

·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(cè)面雙鏡頭三維成像分析

·選配根系成像分析單元,以對(duì)根系進(jìn)行近紅外成像分析

7. 可見(jiàn)光-近紅外高光譜成像單元

·          image.png成像波長(zhǎng)范圍:400-950nm(或350-900nm)

·成像傳感器:推掃式線(xiàn)性?huà)呙鑲鞲衅?,配備掃描光?/span>

·像素色散:0.28nm/pixel

·光譜分辨率0.8nm FWHM

·光譜帶數(shù)(波段數(shù)):1920個(gè)波段

·空間分辨率:1000

·入射狹縫寬度:25μm

·幀頻:45fps

·CMOS檢測(cè)器,光圈F/2.0,GigE網(wǎng)絡(luò)接口

·自動(dòng)參考校準(zhǔn),線(xiàn)性?huà)呙?,高度可調(diào)

·測(cè)量參數(shù):每個(gè)波段的反射光譜成像圖及全光譜曲線(xiàn),并可自動(dòng)計(jì)算以下植被指數(shù):歸一化指數(shù)NDVI、簡(jiǎn)單比值指數(shù)SR、改進(jìn)的葉綠素吸收反射指數(shù)MCARI、改進(jìn)的葉綠素吸收反射指數(shù)1MCARI1、優(yōu)化土壤調(diào)整植被指數(shù)OSAVI、綠度指數(shù)G、轉(zhuǎn)換類(lèi)胡羅卜素指數(shù)TCARI、三角植被指數(shù)TVI、ZMI指數(shù)、簡(jiǎn)單比值色素指數(shù)SRPI、歸一化脫鎂作用指數(shù)NPQI、光化學(xué)植被反射指數(shù)PRI、歸一化葉綠素指數(shù)NPCI、Carter指數(shù)、Lichtenthaler指數(shù)、SIPI指數(shù)、Gitelson-Merzlyak指數(shù)、花青素反射指數(shù)等等

image.png

8. 短波紅外高光譜成像單元

·成像波長(zhǎng)范圍:900-1700nm

·成像傳感器:推掃式線(xiàn)性?huà)呙鑲鞲衅?,配備掃描光?/span>

·光譜分辨率:2nm(FWHM)

·光譜帶數(shù):510個(gè)波段

·空間分辨率636

·測(cè)量參數(shù):每個(gè)波段的反射光譜成像圖及全光譜曲線(xiàn),無(wú)損測(cè)量植物整體及不同部位水分含量變化(右圖中藍(lán)色越深含水量越高)

image.png

9. 3D激光掃描單元:

·頂部與側(cè)面激光掃描,660nm激光,用于植物精確3D模型構(gòu)建,分辨率低于1mm

·頂部掃描距離60cm,客戶(hù)定義側(cè)面掃描距離

·3D點(diǎn)云模型,RGB成像、葉綠素?zé)晒獬上駭?shù)據(jù)等可與3D模型疊加分析

·植物結(jié)構(gòu)、生物量、葉片數(shù)量、葉面積、葉片傾斜角度、植物高度等結(jié)構(gòu)形態(tài)參數(shù)

image.png

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(chēng)性、根尖數(shù)、根節(jié)數(shù)等

image.png

11.image.png自動(dòng)澆灌與稱(chēng)重單元

·測(cè)量參數(shù):實(shí)際重量、澆水體積、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(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(chēng)重前自動(dòng)零校準(zhǔn),還可通過(guò)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(wú)縫結(jié)合分析

·稱(chēng)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,保證澆灌位置

12.自動(dòng)化植物傳送系統(tǒng)

·441.jpg傳送植物大小:根據(jù)客戶(hù)需求,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(guò)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,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/輸出大2560點(diǎn);輸入/輸出單元大40;溫度傳感器Pt1000,Pt100,PTC;PLC通訊百兆以太網(wǎng);OMRON MECHATROLINK-II 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(pán)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(pán)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(guò)程可實(shí)現(xiàn)*自動(dòng)控制,在無(wú)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(hù)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(zhuān)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(hù)定義、可編輯自動(dòng)測(cè)量程序(protocols),根據(jù)用戶(hù)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(xiàn)的形式顯示。

image.png

·MySQL數(shù)據(jù)庫(kù)管理系統(tǒng),可以處理?yè)碛猩锨f(wàn)條記錄的大型數(shù)據(jù)庫(kù),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(kù)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(pán)的識(shí)別碼等存儲(chǔ)在數(shù)據(jù)庫(kù)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(xiàn)顯示植物托盤(pán)數(shù)量、光線(xiàn)強(qiáng)度、分析測(cè)量狀態(tài)及結(jié)果等,輕松通過(guò)軟件*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(guò)開(kāi)發(fā)工具創(chuàng)建自定義的工作過(guò)程,或者手動(dòng)操作LED光源開(kāi)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(chēng)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(guò)高分辨率RGB鏡頭 或激光掃描構(gòu)建3D模型,通過(guò)投射技術(shù),將與其它傳感器所得數(shù)據(jù)如葉綠素?zé)晒?、紅外熱成像溫度數(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(hù)通過(guò)互聯(lián)網(wǎng)遠(yuǎn)程訪(fǎng)問(wèn),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(hù)權(quán)限分級(jí)功能,防止其他人員誤操作影響實(shí)驗(yàn)

·廠(chǎng)家遠(yuǎn)程故障診斷,軟件*升級(jí)

image.png

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(guò)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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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, 4351

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, 240252.

16.Julkowska, M. M., Saade, S., Agarwal Al, G., Gao, G., Pailles, Y., et al. 2019. MVAppM*ria analysis application for streamlined data analysis and curation. Plant Physiology, 180, 12611276.

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

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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.

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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(tái):

1、FKM多光譜熒光動(dòng)態(tài)顯微成像系統(tǒng)

image.png

右圖引自《Nature Plants2016, Photonic multilayer structure of Begonia chloroplasts enhances photosynthetic efficiency by Heather M. Whitney

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

image.png

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

image.png

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

image.png

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