何 勇,李禧堯,楊國(guó)峰,俞澤宇,楊寧遠(yuǎn),馮旭萍,2,許麗佳
·農(nóng)業(yè)信息與電氣技術(shù)·
室內(nèi)高通量種質(zhì)資源表型平臺(tái)研究進(jìn)展與展望
何 勇1,李禧堯1,楊國(guó)峰1,俞澤宇1,楊寧遠(yuǎn)1,馮旭萍1,2※,許麗佳3
(1. 浙江大學(xué)生物系統(tǒng)工程與食品科學(xué)學(xué)院,杭州 310058;2. 浙江大學(xué)新農(nóng)村發(fā)展研究院,杭州 310058;3. 四川農(nóng)業(yè)大學(xué)機(jī)電學(xué)院,雅安 625014)
種質(zhì)資源是作物育種改良的重要物質(zhì)基礎(chǔ),精確高通量獲取作物表型信息是進(jìn)行種質(zhì)資源評(píng)估的重要途徑?;诒硇蛿?shù)據(jù)獲取、解析與管理技術(shù)的室內(nèi)高通量植物表型平臺(tái)具備環(huán)境精確調(diào)控、成像自動(dòng)無(wú)損、效率顯著提升等特點(diǎn),為種質(zhì)資源評(píng)估提供了高效、集成且規(guī)?;慕鉀Q方案,對(duì)作物育種改良、種業(yè)高質(zhì)量發(fā)展具有深遠(yuǎn)影響。該研究主要闡述了四類室內(nèi)高通量種質(zhì)資源表型平臺(tái)的研究現(xiàn)狀,對(duì)目前室內(nèi)植物表型數(shù)據(jù)解析及管理技術(shù)進(jìn)行介紹與分析,并總結(jié)了室內(nèi)高通量表型平臺(tái)與數(shù)據(jù)解析管理方法存在的問題與挑戰(zhàn),對(duì)未來(lái)種質(zhì)資源表型評(píng)估研究方向與趨勢(shì)進(jìn)行展望,以期為植物表型研究提供指導(dǎo)和建議。
作物;植物表型;室內(nèi)表型平臺(tái);表型數(shù)據(jù)解析與管理;高通量;種質(zhì)資源評(píng)估
種業(yè)是農(nóng)業(yè)的“芯片”,農(nóng)業(yè)種質(zhì)資源則是種業(yè)的“芯片”,保護(hù)好、利用好農(nóng)業(yè)遺傳資源,對(duì)提升現(xiàn)代種業(yè)發(fā)展水平、推動(dòng)農(nóng)業(yè)高質(zhì)量發(fā)展、實(shí)施鄉(xiāng)村振興戰(zhàn)略意義重大[1-3]。收集作物種質(zhì)資源,進(jìn)行綜合評(píng)價(jià),保證資源的多樣性是作物改良育種工作的基礎(chǔ),也是獲得優(yōu)異育種材料的關(guān)鍵[4-6]。近年來(lái),高通量測(cè)序技術(shù)的迅猛發(fā)展使植物基因組信息呈指數(shù)級(jí)別上升,大大促進(jìn)了對(duì)調(diào)控作物重要性狀基因的研究與挖掘,為闡明作物起源和演化、全面評(píng)估種質(zhì)資源群體多樣性提供了核心理論和技術(shù)支撐[7-10]。
在種質(zhì)評(píng)估過程中,很多數(shù)量表型性狀,比如植株高度、葉片大小,葉傾角等性狀,由微效多基因控制,易受環(huán)境影響,需要進(jìn)行多年多點(diǎn)的表型鑒定[11-14]。相對(duì)于高通量的測(cè)序技術(shù)來(lái)說,種質(zhì)表型考察主要依靠人力手工測(cè)量,效率低、精度差且破壞性大,是功能基因組學(xué)研究和作物育種改良進(jìn)展的關(guān)鍵制約因素[15]。由此導(dǎo)致了大量可用的基因組信息與表型性狀無(wú)法相匹配,限制了作物育種改良效率和能力[16-18]。因此,為篩選、培育出更高產(chǎn)、優(yōu)質(zhì)、耐脅迫的作物,進(jìn)一步研究提升植物表型獲取與解析技術(shù)及裝備迫在眉睫[19-20]。
隨著大數(shù)據(jù)時(shí)代的到來(lái),高通量種質(zhì)資源表型平臺(tái)的開發(fā)與應(yīng)用為提高表型信息獲取效率提供了新思路和新方案[21-24]。其采用自動(dòng)化傳輸裝置、高度集成化傳感器(包括可見光、多光譜、高光譜、熒光、熱成像、激光成像等)和數(shù)據(jù)管理分析系統(tǒng),每天可對(duì)至少數(shù)百株的植物進(jìn)行無(wú)損、自動(dòng)、快速、高通量成像與數(shù)據(jù)解析,進(jìn)而實(shí)現(xiàn)與遺傳變異等密切相關(guān)的植物動(dòng)態(tài)生長(zhǎng)發(fā)育表型數(shù)據(jù)的監(jiān)測(cè)、量化和評(píng)估,是深入挖掘作物“基因型-表型-環(huán)境型”內(nèi)在關(guān)聯(lián)的有效途徑[25-27]。目前,世界各地的表型研發(fā)機(jī)構(gòu)大規(guī)模開發(fā)了多生境、多維度的室內(nèi)高通量種質(zhì)資源表型平臺(tái)和田間高通量種質(zhì)資源表型平臺(tái),實(shí)現(xiàn)多尺度植株的表型數(shù)據(jù)獲取[28-29]。田間高通量種質(zhì)資源表型平臺(tái)主要包含車載式、自走式、懸索式、軌道式、無(wú)人機(jī)和遙感衛(wèi)星等形式[30-32]。采用田間平臺(tái)獲取的表型數(shù)據(jù)通常時(shí)效性高、信息量大、環(huán)境信息、地理信息等附加數(shù)據(jù)多,但具有獲取表型數(shù)據(jù)之間標(biāo)準(zhǔn)不統(tǒng)一及重復(fù)性低等問題[33]。
與野外大田試驗(yàn)相比,采用室內(nèi)高通量種質(zhì)資源表型平臺(tái)進(jìn)行室內(nèi)試驗(yàn)雖然不能提供土壤系統(tǒng)的真實(shí)性以及植物生物和非生物脅迫的復(fù)雜性,但可通過對(duì)各種環(huán)境條件的精細(xì)調(diào)控和對(duì)植物生長(zhǎng)發(fā)育條件的嚴(yán)格控制,定性、定量地研究代表性或感興趣的植物對(duì)特定環(huán)境的響應(yīng),避免了基因型與自然環(huán)境相互作用引起的不可預(yù)測(cè)的表型變異,并可針對(duì)植物生長(zhǎng)情況開展復(fù)雜試驗(yàn)條件下的模擬分級(jí)研究[34]。此外,室內(nèi)試驗(yàn)可預(yù)選縮小數(shù)千種基因型的范圍,為進(jìn)行田間試驗(yàn)評(píng)估真實(shí)生長(zhǎng)環(huán)境下的作物性狀奠定了基礎(chǔ)。
因此,部署在溫室或生長(zhǎng)室中的室內(nèi)高通量種質(zhì)資源表型平臺(tái)具備精確調(diào)控、分級(jí)模擬和自動(dòng)化精準(zhǔn)采集等優(yōu)勢(shì),滿足種質(zhì)資源相關(guān)表型的精準(zhǔn)鑒定需求,是提升種質(zhì)資源表型評(píng)估研究廣度和深度的有效手段。本文主要闡述室內(nèi)高通量種質(zhì)資源表型平臺(tái)的研究現(xiàn)狀,綜述室內(nèi)植物表型數(shù)據(jù)解析及管理技術(shù),并分析目前高通量表型平臺(tái)存在的問題與挑戰(zhàn),對(duì)未來(lái)種質(zhì)資源表型評(píng)估研究方向與趨勢(shì)進(jìn)行展望。
在Web of Science中搜索2002—2022年間“植物高通量表型平臺(tái)”“室內(nèi)表型平臺(tái)”“室內(nèi)高通量種質(zhì)資源表型平臺(tái)”相關(guān)論文,并分別按照年份、國(guó)家和地區(qū)、文獻(xiàn)類型及研究領(lǐng)域進(jìn)行統(tǒng)計(jì)分析,如圖1所示。近二十年各國(guó)對(duì)室內(nèi)高通量表型平臺(tái)的研究日益增長(zhǎng),特別是美國(guó)、中國(guó)和歐洲國(guó)家。這些國(guó)家研發(fā)應(yīng)用了大量表型平臺(tái)和設(shè)施,為各類種質(zhì)資源表型提供可靠的鑒定評(píng)估工具,如圖2所示為各類高通量種質(zhì)資源表型平臺(tái)及測(cè)量植株尺度。而植物科學(xué)、農(nóng)業(yè)和遺傳學(xué)是相關(guān)出版物貢獻(xiàn)最多的3個(gè)領(lǐng)域。由于室內(nèi)表型平臺(tái)仍屬于技術(shù)研發(fā)階段,因此研究論文仍然是出版物的主要類型。由此可見,室內(nèi)高通量種質(zhì)資源表型平臺(tái)蘊(yùn)含著廣闊的研究發(fā)展前景。
圖1 2002-2022年室內(nèi)高通量種質(zhì)資源表型平臺(tái)相關(guān)出版物統(tǒng)計(jì)分析
圖2 各類高通量種質(zhì)資源表型平臺(tái)及測(cè)量植株尺度
基于平臺(tái)的結(jié)構(gòu)設(shè)計(jì)及與傳感器之間的運(yùn)動(dòng)模式,室內(nèi)高通量種質(zhì)資源表型平臺(tái)主要分為臺(tái)式、傳送帶式、軌道式和室內(nèi)植物表型機(jī)器人。表1詳細(xì)羅列了各類典型室內(nèi)高通量表型平臺(tái)所搭載傳感器、通量及適用作物等。
表1 室內(nèi)高通量種質(zhì)資源表型平臺(tái)介紹
注:“√”代表各類典型室內(nèi)高通量種質(zhì)資源表型平臺(tái)所搭載的傳感器類型。
Note: “√” represents the type of sensors mounted on various typical indoor high-throughput germplasm phenotyping platforms.
臺(tái)式室內(nèi)高通量種質(zhì)資源表型平臺(tái)主要通過在氣候生長(zhǎng)箱中安裝可見光、近紅外、紅外、熒光等成像傳感器小批量獲取小型植物的表型參數(shù)。該類平臺(tái)沒有傳送裝置,需要手工更換植物樣本。
德國(guó)Lemna Tec公司研發(fā)的Scanalyzer HTS是一款專為小型植物(如擬南芥)和單子葉、雙子葉植物幼苗而設(shè)計(jì)的2D高通量表型平臺(tái)。該平臺(tái)一次可容納6個(gè)托盤,每個(gè)托盤18株植物。其通過搭載可見光、熒光、近紅外和紅外攝像頭獲取高分辨率圖像,從而提取葉面積、葉綠素含量、莖直徑、植物高度及寬度、生長(zhǎng)速率、生物量、顏色、葉卷等多種植物性狀[49]。
近年來(lái),Lemna Tec公司研發(fā)的多傳感器實(shí)驗(yàn)室表型分析平臺(tái)HyperAlxpert添加了3D傳感器選項(xiàng),實(shí)現(xiàn)了三維點(diǎn)云圖像采集,同時(shí)可對(duì)擬南芥等植物幼苗、真菌或微生物培養(yǎng)皿等多種樣品獲取表型數(shù)據(jù)。特別地,其通過配備自動(dòng)托盤裝載模塊TrayProvider實(shí)現(xiàn)了高通量樣本測(cè)量[50]。
奧地利科學(xué)院研究所研發(fā)的臺(tái)式表型分析平臺(tái)PhenoBox/PhenoPipe將RGB相機(jī)或高光譜相機(jī)固定在植物側(cè)端,通過轉(zhuǎn)盤旋轉(zhuǎn)植物實(shí)現(xiàn)不同角度的拍攝,進(jìn)而提供了一種更為靈活且經(jīng)濟(jì)實(shí)惠的自動(dòng)化開源成像工具和表型數(shù)據(jù)分析解決方案[51]。但該平臺(tái)一次只能拍攝一株,需要手工更換樣品,自動(dòng)化程度相對(duì)較低。
除了對(duì)小型植株的葉片等組織器官進(jìn)行表型獲取外,臺(tái)式室內(nèi)高通量種質(zhì)資源表型平臺(tái)還可結(jié)合X射線計(jì)算機(jī)斷層掃描技術(shù)(X-CT)對(duì)植物根系進(jìn)行無(wú)損三維成像[61-63]。三英精密儀器有限公司研制的nanoVoxel系列顯微CT系統(tǒng)采用微納焦點(diǎn)X射線源,分辨率達(dá)到微米或亞微米級(jí),具有無(wú)損性、無(wú)需制樣、三維全息結(jié)構(gòu)等優(yōu)點(diǎn),可獲得植物根系、果實(shí)、莖稈和葉片的諸多三維表型結(jié)構(gòu)信息[52]。
綜上,臺(tái)式室內(nèi)高通量種質(zhì)資源表型平臺(tái)主要針對(duì)小型植株,或是幼苗進(jìn)行表型研究,高通量程度相對(duì)較低。但相較于其他類型溫室高通量表型平臺(tái),其可以更為精確地控制溫度、水、CO2、光照條件、病害感染程度以及其他生物和非生物脅迫,進(jìn)而更準(zhǔn)確地量化受控環(huán)境條件下植物表型性狀[64-65]。
傳送帶式室內(nèi)高通量種質(zhì)資源表型平臺(tái)利用“植物到傳感器”的原理,將多種傳感器固定放置于暗室的上方和側(cè)面進(jìn)行成像,盆栽植物放置于傳送帶上向前運(yùn)輸,當(dāng)植株運(yùn)輸至暗室則隨即進(jìn)行表型數(shù)據(jù)的采集,采集完成后再繼續(xù)移動(dòng)并返回原本生長(zhǎng)的位置。荷蘭WPS公司研發(fā)的大型傳送帶式室內(nèi)表型平臺(tái)WPScan Conveyor是世界上第一臺(tái)高通量表型分析平臺(tái)。該平臺(tái)利用傳送帶將植物傳輸至指定成像位置,獲取植物可見光圖像、多光譜圖像、高光譜圖像、熒光圖像、激光三維圖像和熱圖像等,進(jìn)而對(duì)圖像進(jìn)行參數(shù)提取,掌握植物生長(zhǎng)發(fā)育情況[12]。每個(gè)植株均通過條形碼加以標(biāo)識(shí),因此在不同生長(zhǎng)發(fā)育階段產(chǎn)生的各種表型數(shù)據(jù)均能定時(shí)、自動(dòng)地加以檢測(cè),適合于盆栽和小型作物。
德國(guó)Lemna Tec公司研制的Scanalyzer 3D作為世界上最為經(jīng)典的傳送帶式全自動(dòng)高通量表型平臺(tái),在全球范圍內(nèi)安裝了各種版本。如內(nèi)布拉斯加大學(xué)基于Scanalyzer 3D建立的高通量種質(zhì)資源表型平臺(tái)可同時(shí)容納672株高達(dá)2.5 m的植物進(jìn)行表型分析,并可在植物的頂部和側(cè)視圖收集可見光、熒光、紅外和高光譜圖像[53]。同時(shí),該平臺(tái)每個(gè)成像室均配備一個(gè)旋轉(zhuǎn)電梯以獲取植株360°側(cè)視圖[66]。另外,澳大利亞國(guó)家植物表型中心建造的植物加速器同樣安裝了2套Scanalyzer 3D平臺(tái),每套容量達(dá)2 400株植物,并配備4個(gè)成像室獲取可見光圖像、近紅外圖像、根系近紅外圖像、熒光圖像和熱圖像。同時(shí),所有植物均使用條碼或射頻標(biāo)簽進(jìn)行各生長(zhǎng)發(fā)育階段表型數(shù)據(jù)的定期檢測(cè)[54]。
捷克Photon Systems Instruments公司研發(fā)的超大型植物表型成像平臺(tái)PlantScreen集成了LED植物智能培養(yǎng)、RGB真彩三維成像、葉綠素?zé)晒鈭D像分析、熱圖像分析、近紅外圖像分析、高光譜圖像分析、自動(dòng)條形碼標(biāo)識(shí)管理等多種先進(jìn)技術(shù),實(shí)現(xiàn)從模式植物擬南芥到小麥、玉米等大規(guī)模糧食作物的全方位表型性狀獲取,可對(duì)植物表型組進(jìn)行全面、自動(dòng)、高通量且無(wú)人值守的長(zhǎng)期研究分析,進(jìn)而獲取和研究植物整個(gè)生活史的生理變化和表型分析的所有相關(guān)海量數(shù)據(jù)[67-68]。
中國(guó)針對(duì)高通量表型數(shù)據(jù)測(cè)量平臺(tái)的自主研發(fā)起步相對(duì)較晚,目前研制的種質(zhì)資源表型平臺(tái)大多只能實(shí)現(xiàn)某一種植物固定性狀的獲取,整體研發(fā)水平相較于國(guó)外仍有一定差距。其中,華中農(nóng)業(yè)大學(xué)建造的室內(nèi)水稻高通量表型平臺(tái)HRPF為中國(guó)自主開發(fā)的典型平臺(tái)。該平臺(tái)總?cè)萘繛? 472株植物,可通過RGB成像和X射線計(jì)算機(jī)斷層掃描監(jiān)測(cè)多達(dá)1 920種水稻植物種群中的至少15種農(nóng)藝性狀,一些難以手動(dòng)評(píng)估的基于圖像的性狀(如耐旱水稻的卷葉)也可進(jìn)行量化[55-56]。同時(shí),通過對(duì)圖像分析軟件的微小調(diào)整,HRPF平臺(tái)還可以擴(kuò)展到分析其他物種的表型,包括獲取小麥植物三維點(diǎn)云圖像、油菜幼苗的葉片性狀和玉米生長(zhǎng)變異的遺傳結(jié)構(gòu)等[69-71]。
大多數(shù)傳送帶式高通量表型平臺(tái)需要將植物運(yùn)送到特定的成像室,可能導(dǎo)致小氣候異質(zhì)性,影響植物的生長(zhǎng)和對(duì)環(huán)境變化的反應(yīng),從而使獲取的表型數(shù)據(jù)不準(zhǔn)確。因此,Tisné等設(shè)計(jì)了一種可獲取可見光圖像的循環(huán)傳送帶式表型平臺(tái)Phenoscope,通過連續(xù)旋轉(zhuǎn)平臺(tái)上735盆植株使其經(jīng)歷相同的外部條件,從而最大限度地減少單個(gè)植物的環(huán)境變化,補(bǔ)償環(huán)境異質(zhì)性,進(jìn)而獲取植物的芽生長(zhǎng)和含水量信息[57]。類似的,普渡大學(xué)建造的可進(jìn)行高光譜成像的傳送帶式室內(nèi)高通量表型平臺(tái)使植物在整個(gè)生育期均生長(zhǎng)在循環(huán)傳送帶上,克服了微氣候差的干擾[72]。
雖然傳送帶式室內(nèi)高通量種質(zhì)資源表型平臺(tái)可以攜帶大尺寸(如高粱和玉米)和大容量的植物,但傳送帶的搖晃可能會(huì)影響莖稈脆弱的植物,從而降低表型信息質(zhì)量,增大圖像噪聲。此外,植物的光譜信息并非在原位收集,生長(zhǎng)位置和成像室之間存在環(huán)境差異,因此表型數(shù)據(jù)存在一定程度的不準(zhǔn)確。另一方面,建立基于傳送帶的表型平臺(tái)昂貴且耗時(shí),單位成本取決于吞吐量,且靈活性較弱,功能性單一。未來(lái),傳送帶式室內(nèi)高通量種質(zhì)資源表型平臺(tái)應(yīng)致力于提高運(yùn)行穩(wěn)定性、環(huán)境均勻性、功能多樣性和設(shè)施靈活性,為植物提供平穩(wěn)的運(yùn)輸和精確的氣候控制,并在整個(gè)生育期實(shí)現(xiàn)多種植物性狀的完整精確表型分析。
根莖較為脆弱的小型植物的表型性狀易受環(huán)境變化(溫度、風(fēng)等)的影響。軌道式室內(nèi)高通量種質(zhì)資源表型平臺(tái)采用“傳感器到植物”的原理,無(wú)需植物移動(dòng),使其最接近自然生長(zhǎng)狀態(tài)。通過計(jì)算機(jī)控制搭載多個(gè)成像傳感器的機(jī)械臂沿著軌道在XYZ軸方向移動(dòng),對(duì)植物生長(zhǎng)位置進(jìn)行自動(dòng)定位并獲取其表型數(shù)據(jù)。因此,軌道式室內(nèi)高通量種質(zhì)資源表型平臺(tái)更傾向于研究尺寸較小的植物,通過確保生長(zhǎng)環(huán)境的同質(zhì)性使植物不受干擾的生長(zhǎng)發(fā)育,進(jìn)而獲取與細(xì)微表型變化相關(guān)的性狀數(shù)據(jù)。
中國(guó)科學(xué)院研發(fā)的小型軌道式種質(zhì)資源表型平臺(tái)Crop 3D以激光雷達(dá)為核心傳感器,采用俯拍方式實(shí)現(xiàn)單行掃描、多行掃描和定點(diǎn)定位掃描,對(duì)室內(nèi)作物的各生長(zhǎng)時(shí)期進(jìn)行可見光圖像、多光譜圖像、熱圖像和激光掃描圖像的多源數(shù)據(jù)獲取。同時(shí),通過自主研發(fā)軟件對(duì)圖像數(shù)據(jù)進(jìn)行解析,可獲得株高、株幅、葉長(zhǎng)、葉寬、葉傾角和葉面積等物理表型性狀[58]。
荷蘭PhenoSpex公司研發(fā)的軌道式高通量平臺(tái)FieldScan通過集成多光譜激光三維掃描測(cè)量?jī)xPlantEye及其他多種成像傳感器和環(huán)境氣象傳感器,可在任何光照條件下,同時(shí)實(shí)現(xiàn)田間和溫室環(huán)境下的植物表型高通量、高精度、全自動(dòng)無(wú)損測(cè)量。其中,PlantEye通過獲取植物頂部三維多光譜圖像可實(shí)現(xiàn)葉面積、葉傾角、冠層等表型數(shù)據(jù)獲取,且相較于其他傳感器精度與通量更高。該平臺(tái)一天可對(duì)上萬(wàn)個(gè)植株及小區(qū)進(jìn)行十余次重復(fù)測(cè)量,以去除低質(zhì)量數(shù)據(jù)的負(fù)面影響,為使用者提供有實(shí)用價(jià)值的表型信息,便于實(shí)時(shí)掌握植株生長(zhǎng)發(fā)育狀況。
中國(guó)PhenoTrait公司設(shè)計(jì)的軌道式高通量種質(zhì)資源表型平臺(tái)TraitDiscover同時(shí)適用于溫室和大田,可搭載葉綠素?zé)晒獬上駜x、光合表型測(cè)量?jī)x、三維激光掃描儀、高光譜相機(jī)、熱成像儀、可見光相機(jī)等多種表型傳感器進(jìn)行全自動(dòng)測(cè)量[59]。根據(jù)系統(tǒng)設(shè)計(jì)的大小每天最多可以實(shí)現(xiàn)上萬(wàn)株植物或成百上千個(gè)群體小區(qū)的測(cè)量。目前,該平臺(tái)已在水稻、小麥、大豆及園藝作物等植物中得到應(yīng)用與推廣。
軌道式室內(nèi)高通量種質(zhì)資源表型平臺(tái)自動(dòng)化程度高,可以收集高分辨率的時(shí)間序列表型數(shù)據(jù)。同時(shí),軌道式表型平臺(tái)有效載荷大,連續(xù)運(yùn)行能力強(qiáng),為探究植物晝夜節(jié)律的動(dòng)力學(xué)機(jī)制提供了途徑與可能。但受平臺(tái)規(guī)模限制,多項(xiàng)試驗(yàn)無(wú)法同時(shí)進(jìn)行,且每次試驗(yàn)對(duì)于植株的擺放布局要求較高。此外,平臺(tái)從植物上方進(jìn)行掃描無(wú)法獲得隱藏在冠層下方的莖、葉的表型性狀。未來(lái),軌道式溫室高通量表型平臺(tái)應(yīng)注重?cái)U(kuò)大獲取單株植物表型面積,解決冠層葉片遮擋問題,縮小傳感器載荷與體積,降低植株手工擺放要求,設(shè)置植株側(cè)面信息獲取的傳感器,提升使用操作的便利性與智能化,降低建造、安裝和運(yùn)行成本。
農(nóng)業(yè)機(jī)器人是精準(zhǔn)農(nóng)業(yè)、數(shù)字農(nóng)業(yè)的重要組成部分,在農(nóng)業(yè)現(xiàn)代化進(jìn)程中發(fā)揮著重要作用[73-74]。目前,農(nóng)業(yè)機(jī)器人在病蟲草害防治、收獲采摘等農(nóng)業(yè)生產(chǎn)作業(yè)環(huán)節(jié)得到研究與應(yīng)用[75-77]。其中,室內(nèi)植物表型機(jī)器人是近年來(lái)快速興起與發(fā)展的一種室內(nèi)高通量種質(zhì)資源表型平臺(tái)。其將機(jī)械臂和無(wú)人駕駛地面車輛作為平臺(tái)并連接各類傳感器,進(jìn)而實(shí)現(xiàn)植物表型性狀的快速感知獲取。相較于其他室內(nèi)高通量種質(zhì)資源表型平臺(tái),表型機(jī)器人大范圍提升了植物表型性狀測(cè)量的能力、速度、覆蓋范圍、可重復(fù)性和成本效益[78]。
針對(duì)軌道式表型平臺(tái)因冠層葉片遮擋無(wú)法獲取完整植株的表型數(shù)據(jù)問題,室內(nèi)表型機(jī)器人能夠靈活的操縱相機(jī)定位與定向,以獲得最佳視點(diǎn),為解決遮擋問題提供了可行性方案。如Wu等[79]研發(fā)了一種自動(dòng)表型機(jī)器人并基于深度學(xué)習(xí)設(shè)計(jì)了“下一個(gè)最佳視點(diǎn)”選取算法評(píng)估最優(yōu)視點(diǎn)。該機(jī)器人由3個(gè)機(jī)械臂組成,每個(gè)機(jī)械臂均配備一個(gè)深度相機(jī),根據(jù)最佳視點(diǎn)位置操縱機(jī)械臂獲取植物的三維點(diǎn)云數(shù)據(jù)。相較于其他表型機(jī)器人,其在解決遮擋問題方面更加高效與靈活。然而,系統(tǒng)能否找到最佳視點(diǎn)取決于深度學(xué)習(xí)網(wǎng)絡(luò)的訓(xùn)練效果與預(yù)測(cè)能力,目前仍然具有一定難度和挑戰(zhàn)。
此外,大多數(shù)室內(nèi)表型機(jī)器人試圖接觸和探測(cè)植物器官,進(jìn)而將機(jī)器人獲取植物表型性狀信息的范圍從外部形態(tài)結(jié)構(gòu)延伸擴(kuò)展至生理生化性狀[80-81]。因此,此類表型機(jī)器人被設(shè)計(jì)成模仿人類動(dòng)作來(lái)操縱植物并測(cè)量目標(biāo)植物器官的表型性狀。如美國(guó)愛荷華州立大學(xué)定制設(shè)計(jì)的植物生長(zhǎng)室Enviratro 采用表型機(jī)器人定期進(jìn)入并進(jìn)行植物精確表型測(cè)量[60]。該機(jī)器人由無(wú)人駕駛地面車輛,六軸機(jī)械臂和一系列傳感器組成,采用同步定位和建圖技術(shù)、兩臺(tái)SICK S300激光掃描儀實(shí)現(xiàn)室內(nèi)自主導(dǎo)航。傳感器具體包括RGB相機(jī)、高光譜相機(jī)、近紅外相機(jī),熱成像相機(jī)、飛行時(shí)間相機(jī),激光輪廓儀和電流脈沖幅度調(diào)制熒光計(jì)。上述傳感器通過機(jī)械臂自主定位實(shí)現(xiàn)植物俯視圖與側(cè)視圖成像,并根據(jù)計(jì)算機(jī)視覺算法獲取各種生理測(cè)量值。特別的是,該機(jī)器人可在葉片表面精確放置傳感器探針以實(shí)現(xiàn)高精度測(cè)量。
室內(nèi)植物表型機(jī)器人大大提高了表型數(shù)據(jù)獲取的速度、容量、可重復(fù)性和準(zhǔn)確性。但在傳感、定位、路徑規(guī)劃、物體檢測(cè)和避障方面仍存在許多問題,需要通過跨學(xué)科深入研究開發(fā)更有效、更強(qiáng)大的傳感器、控制器和算法,以提高其精確性、安全性和可靠性。
綜上所述,各類室內(nèi)高通量種質(zhì)資源表型平臺(tái)采用先進(jìn)的傳感器和數(shù)據(jù)采集系統(tǒng),無(wú)損且高通量的獲取大規(guī)模農(nóng)業(yè)試驗(yàn)的特定表型,可以有效完成傳統(tǒng)表型獲取方法無(wú)法完成的任務(wù)。表2闡述了各類室內(nèi)表型平臺(tái)的優(yōu)勢(shì)、使用限制及未來(lái)研究方向。但如何利用大數(shù)據(jù)分析挖掘技術(shù)從海量多源表型數(shù)據(jù)中整合發(fā)現(xiàn)有生物學(xué)意義的信息,進(jìn)而為種質(zhì)資源評(píng)估提供指導(dǎo)意見,目前的表型數(shù)據(jù)解析與管理技術(shù)仍處于探索階段。因此,海量數(shù)據(jù)管理,圖像數(shù)據(jù)處理和表型性狀分析是新一代植物表型組學(xué)面臨的主要挑戰(zhàn)。
表2 各類室內(nèi)高通量種質(zhì)資源表型平臺(tái)的應(yīng)用范圍、使用限制及研究方向
隨著非侵入性成像技術(shù)在高通量表型獲取中的廣泛應(yīng)用,高通量種質(zhì)資源表型平臺(tái)通過搭載各類成像技術(shù)所對(duì)應(yīng)的傳感器,對(duì)植物的形態(tài)結(jié)構(gòu)、生理生化等信息進(jìn)行精確、自動(dòng)和重復(fù)的成像[82-84]。但植物表型數(shù)據(jù)獲取只是表型研究的第一步。如何從原始圖像數(shù)據(jù)中提取表型性狀,并進(jìn)行表型數(shù)據(jù)的標(biāo)準(zhǔn)化存儲(chǔ)管理和跨尺度、跨維度的性狀分析,從而實(shí)現(xiàn)種質(zhì)資源篩選與鑒定是研究的重點(diǎn)與難點(diǎn)。因此,本節(jié)重點(diǎn)闡述了室內(nèi)植物表型圖像數(shù)據(jù)的解析與管理技術(shù)。
為實(shí)現(xiàn)針對(duì)植物農(nóng)藝性狀、產(chǎn)量性狀、品質(zhì)性狀及生物與非生物脅迫性狀的種質(zhì)資源評(píng)估,采集表型圖像與數(shù)據(jù)的各類室內(nèi)高通量種質(zhì)資源表型平臺(tái)迅猛發(fā)展,而研究開發(fā)相應(yīng)準(zhǔn)確的圖像數(shù)據(jù)解析技術(shù)與算法,進(jìn)而自動(dòng)提取多樣的植物表型性狀顯得至關(guān)重要[85-88]。但由于不同品種之間的植物外觀形態(tài)表型千差萬(wàn)別,且即便是同一品種的植株,隨著其生長(zhǎng)發(fā)育,植株的形態(tài)結(jié)構(gòu)、顏色大小等外觀表型也將不斷變化,導(dǎo)致自動(dòng)化解析圖像的難度陡然增大。
大型表型平臺(tái)公司主要通過研發(fā)較為成熟的集成解析軟件以實(shí)現(xiàn)表型性狀提取與表型數(shù)據(jù)分析。如德國(guó)LemnaTec公司基于Scanalyzer 3D平臺(tái)研發(fā)的LemnaGrid軟件能夠依據(jù)表型圖像進(jìn)行表型性狀解析[89-91]。荷蘭PhenoSpex公司基于多光譜激光三維掃描測(cè)量?jī)xPlantEye研發(fā)的Leasyscan系統(tǒng)能夠根據(jù)葉面積、葉面積指數(shù)和蒸騰作用評(píng)估影響水分利用的冠層性狀,并開發(fā)了Hortcontrol軟件按照動(dòng)態(tài)時(shí)間序列實(shí)時(shí)可視化上述參數(shù)信息[92]。然而,大多數(shù)開發(fā)的圖像分析工具與軟件是為特定任務(wù)和特定植物種類設(shè)計(jì)。當(dāng)面對(duì)熒光、高光譜、激光雷達(dá)等不同傳感器獲取的信息或是小麥、玉米、擬南芥等不同物種的表型圖像時(shí),分析軟件往往無(wú)法較好的滿足新的分析要求,調(diào)整時(shí)缺乏靈活性。Klukas等[93]對(duì)此基于Lemna Tec系統(tǒng)研發(fā)了一種開放式的集成分析平臺(tái)IAP,可進(jìn)行小麥、大麥及玉米等禾本科作物的可見光圖像、熒光圖像、近紅外圖像和紅外圖像的高通量表型分析。
雖然上述各種商業(yè)表型圖像數(shù)據(jù)分析軟件層出不窮,但需依賴于特定硬件平臺(tái)定制,安裝維護(hù)成本高且使用門檻高,阻礙了圖像分析工具向通用性、實(shí)用性和標(biāo)準(zhǔn)化的趨勢(shì)方向發(fā)展與廣泛應(yīng)用。因此,許多研究團(tuán)隊(duì)與學(xué)者針對(duì)各類作物多尺度、多時(shí)序的表型圖像研發(fā)了獨(dú)立于平臺(tái)系統(tǒng)的表型分析軟件。如Zhou等[94]研發(fā)了一種開源可拓展且易于使用的植物生長(zhǎng)表型分析軟件Leaf-GP,可輕松解析低成本成像設(shè)備捕獲的擬南芥、小麥等作物的圖像,有效提取葉片數(shù)、投影葉面積、葉周長(zhǎng)、緊湊度及顏色等表型性狀。Minervini等[95]開發(fā)了一種經(jīng)濟(jì)實(shí)惠且便于部署使用的表型解析軟件Phenotiki,并采用擬南芥二維俯視圖像驗(yàn)證了軟件獲取的形狀、顏色、葉片數(shù)及生長(zhǎng)曲線等表型信息的可靠性。Gaggion等[96]基于卷積神經(jīng)網(wǎng)絡(luò)設(shè)計(jì)了一種植物根系高通量表型分析軟件ChronoRoot,實(shí)現(xiàn)根系生長(zhǎng)動(dòng)態(tài)表型參數(shù)的自動(dòng)提取。表3列出了上述典型植物表型圖像數(shù)據(jù)分析軟件。
表3 典型植物表型圖像數(shù)據(jù)分析軟件
研發(fā)穩(wěn)健、準(zhǔn)確、自動(dòng)的種質(zhì)資源表型解析算法是實(shí)現(xiàn)植物表型原始圖像數(shù)據(jù)的解析和上述各類表型分析軟件開發(fā)的基礎(chǔ)與關(guān)鍵。目前,常用的表型原始數(shù)據(jù)解析方法主要包括經(jīng)典統(tǒng)計(jì)分析方法、圖像處理方法、傳統(tǒng)機(jī)器學(xué)習(xí)和深度學(xué)習(xí),如表4所示。傳統(tǒng)的經(jīng)典統(tǒng)計(jì)分析方法主要針對(duì)數(shù)字類型的表型數(shù)據(jù),如為了驗(yàn)證高通量表型平臺(tái)獲取表型數(shù)據(jù)的準(zhǔn)確性和可靠性,將其與傳統(tǒng)手工測(cè)量的表型數(shù)據(jù)進(jìn)行相關(guān)性分析[55-56, 67-68]。為建立植物生長(zhǎng)參數(shù)反演模型,將其與平臺(tái)獲取表型數(shù)據(jù)進(jìn)行回歸分析等。該方法簡(jiǎn)單易行,計(jì)算設(shè)備無(wú)需較高的硬件性能,但通常只可提煉和總結(jié)基礎(chǔ)數(shù)據(jù)中的一般規(guī)律與趨勢(shì),無(wú)法對(duì)植物表型原始圖像數(shù)據(jù)進(jìn)行深層次解析。圖像處理方法在表型原始二維、三維圖像解析中使用最多且范圍最廣,其通過形態(tài)學(xué)和顏色特性進(jìn)行植株個(gè)體和莖、葉、果實(shí)等植物器官與復(fù)雜背景的目標(biāo)邊緣和輪廓的提取,進(jìn)而獲取植物外在形態(tài)、顏色等表型性狀[97-100]。因此,開發(fā)更為精準(zhǔn)且魯棒的圖像提取與分割算法非常重要。
而近年來(lái)飛速演變發(fā)展的計(jì)算機(jī)視覺與機(jī)器學(xué)習(xí)在處理高通量表型平臺(tái)獲取的大型圖像數(shù)據(jù)集方面具有不可替代的優(yōu)勢(shì)。傳統(tǒng)機(jī)器學(xué)習(xí)采用各種工具和方法從大量作物表型數(shù)據(jù)中“學(xué)習(xí)”特征,以便對(duì)新的數(shù)據(jù)進(jìn)行識(shí)別、分類、評(píng)估與預(yù)測(cè)[101]。較為經(jīng)典常見的機(jī)器學(xué)習(xí)算法包括K-Means聚類、支持向量機(jī)、多層感知機(jī)、決策樹、線性判別分析、人工神經(jīng)網(wǎng)絡(luò)等[102-107]。如Ebersbach等[108]基于Scanalyzer 3D平臺(tái)獲取油菜株高、寬度、體積、葉面積及花序數(shù)量等表型性狀,并采用有監(jiān)督機(jī)器學(xué)習(xí)算法實(shí)現(xiàn)了花序個(gè)數(shù)預(yù)測(cè),準(zhǔn)確率達(dá)91%。Zhou等[109]利用室內(nèi)高通量表型平臺(tái)收集了2個(gè)生長(zhǎng)階段的75株大豆植物的三維點(diǎn)云圖像,并采用boosting算法、支持向量機(jī)和K-Means聚類3種機(jī)器學(xué)習(xí)方法進(jìn)行葉片分割,結(jié)果表明K-Means聚類針對(duì)無(wú)重疊大豆圖像的分割效果最佳,錯(cuò)誤率為0.2%,支持向量機(jī)對(duì)有重疊大豆圖像的分割效果最佳,錯(cuò)誤率為2.57%。
表4 植物表型原始數(shù)據(jù)解析方法
深度學(xué)習(xí)是機(jī)器學(xué)習(xí)領(lǐng)域中一個(gè)迅速興起且蓬勃發(fā)展的研究方向,其利用大型植物表型數(shù)據(jù)集,直接進(jìn)行端到端的訓(xùn)練,被有效地應(yīng)用于植物的圖像目標(biāo)識(shí)別、分割、分類和計(jì)數(shù)等[110-113]。如Misra等[114]基于LemnaTec平臺(tái)獲取的200種小麥的可見光圖像,提出一種新的深度學(xué)習(xí)網(wǎng)絡(luò)SpikeSegNe,實(shí)現(xiàn)了小麥穗數(shù)的識(shí)別和計(jì)數(shù),計(jì)數(shù)平均精度、準(zhǔn)確率和魯棒性分別為99%、95%和97%。Du等[115]采用室內(nèi)高通量表型平臺(tái)構(gòu)建了500個(gè)生菜品種的2 000張RGB圖像數(shù)據(jù)集,并提出一種基于卷積神經(jīng)網(wǎng)絡(luò)的新型目標(biāo)檢測(cè)-語(yǔ)義分割-表型分析方法,對(duì)圖像中植物的定位檢測(cè)精度達(dá)到99.8%,劃分花盆尺寸的測(cè)量誤差小于3%。同時(shí)其解析了生菜15個(gè)靜態(tài)性狀和動(dòng)態(tài)性狀,與人工測(cè)量值進(jìn)行擬合2達(dá)0.88,實(shí)現(xiàn)了生菜生長(zhǎng)狀況監(jiān)測(cè)。相較于傳統(tǒng)機(jī)器學(xué)習(xí),深度學(xué)習(xí)在卷積網(wǎng)絡(luò)中具有多個(gè)隱藏層,每層連續(xù)對(duì)圖像執(zhí)行簡(jiǎn)單的操作,從而提高了其識(shí)別分類和預(yù)測(cè)能力,通常具有更高的準(zhǔn)確率、精確率和召回率[116-118]。但目前公開可用的植物表型數(shù)據(jù)集仍然十分有限,無(wú)法通過遷移學(xué)習(xí)方法提高模型性能、減少數(shù)據(jù)搜集時(shí)間和訓(xùn)練時(shí)間。此外,環(huán)境因素、成像角度、葉片遮擋及背景噪聲等問題均在一定程度上影響圖像數(shù)據(jù)質(zhì)量,對(duì)訓(xùn)練結(jié)果準(zhǔn)確率具有較大影響。因此,應(yīng)用于植物表型圖像解析的深度學(xué)習(xí)模型應(yīng)具有較強(qiáng)的魯棒性和泛化能力。
為進(jìn)行高效的種質(zhì)資源鑒定,高通量、自動(dòng)化、準(zhǔn)確解析海量表型原始圖像數(shù)據(jù),獲取感興趣的表型信息具有重要意義。深度學(xué)習(xí)在提高植物表型解析準(zhǔn)度與通量,擴(kuò)展表型性狀數(shù)量方面蘊(yùn)藏著深遠(yuǎn)前景。因此,進(jìn)一步提升針對(duì)不同成像方式的圖像數(shù)據(jù)的定制化表型解析算法,突破多尺度、多維度、多模態(tài)表型數(shù)據(jù)之間的融合解析,升級(jí)應(yīng)用于海量表型數(shù)據(jù)分析挖掘的硬件與軟件系統(tǒng),構(gòu)建人機(jī)友好交互的可視化表型分析平臺(tái)是未來(lái)的研究方向與必然趨勢(shì)。
傳統(tǒng)的植物表型數(shù)據(jù)管理大多是針對(duì)結(jié)構(gòu)化的數(shù)字類型及字符串類型數(shù)據(jù)的管理,通過常見的關(guān)系型數(shù)據(jù)庫(kù)即可實(shí)現(xiàn)此類數(shù)據(jù)的高效存儲(chǔ)、查詢與維護(hù)。而采用高通量表型平臺(tái)采集表型信息將會(huì)產(chǎn)生諸如圖像、點(diǎn)云、光譜等大量復(fù)雜且非結(jié)構(gòu)化的數(shù)據(jù)。此類數(shù)據(jù)格式不一、長(zhǎng)度各異,利用傳統(tǒng)數(shù)據(jù)庫(kù)的二維邏輯表現(xiàn)數(shù)據(jù)并不方便。如何有效管理類型各異的海量表型數(shù)據(jù),進(jìn)而實(shí)現(xiàn)數(shù)據(jù)整合與共享,推動(dòng)種質(zhì)資源評(píng)估和多重組學(xué)分析進(jìn)一步發(fā)展是當(dāng)今研究的熱點(diǎn)與難點(diǎn)。
而隨著計(jì)算機(jī)技術(shù)的日新月異,針對(duì)非結(jié)構(gòu)化植物表型數(shù)據(jù)構(gòu)建數(shù)據(jù)庫(kù),以實(shí)現(xiàn)標(biāo)準(zhǔn)化與集成化的數(shù)據(jù)管理是行之有效的解決方案。植物表型數(shù)據(jù)庫(kù)采用計(jì)算機(jī)硬件與軟件技術(shù),對(duì)各類非結(jié)構(gòu)化數(shù)據(jù)添加充足的注釋并儲(chǔ)存為標(biāo)準(zhǔn)化文件格式,進(jìn)而將多個(gè)不同來(lái)源的數(shù)據(jù)集有效整合鏈接在一起,便于數(shù)據(jù)的檢索、管理與共享,打破信息孤島。
近年來(lái),多樣的植物表型組學(xué)數(shù)據(jù)庫(kù)及管理系統(tǒng)得到開發(fā)與使用,助力推動(dòng)全球作物育種改良、種質(zhì)資源評(píng)估和作物產(chǎn)量提升的研究,如表5所示。雖然表型數(shù)據(jù)庫(kù)的建立使數(shù)據(jù)源數(shù)量和多樣性增加,但每個(gè)數(shù)據(jù)庫(kù)間相互獨(dú)立,阻礙了表型大數(shù)據(jù)之間的比較、組合與集成。對(duì)數(shù)據(jù)創(chuàng)建應(yīng)用程序編程接口是解決這一瓶頸的有效途徑。如Selby等[119]設(shè)計(jì)的植物育種應(yīng)用程序編程接口BrAPI,通過構(gòu)建Web服務(wù)標(biāo)準(zhǔn)與規(guī)范,促進(jìn)育種應(yīng)用程序之間的互操作性,使客戶端應(yīng)用程序可以依靠標(biāo)準(zhǔn)接口實(shí)現(xiàn)與任何BrAPI數(shù)據(jù)源的集成。
目前,數(shù)據(jù)庫(kù)通??砂凑諗?shù)據(jù)對(duì)象組織方式劃分為結(jié)構(gòu)化數(shù)據(jù)庫(kù)和非結(jié)構(gòu)化數(shù)據(jù)庫(kù);按照數(shù)據(jù)處理場(chǎng)景劃分為事務(wù)性數(shù)據(jù)庫(kù)、分析型數(shù)據(jù)庫(kù)和HTAP數(shù)據(jù)庫(kù);按照數(shù)據(jù)分布方式劃分為集中式數(shù)據(jù)庫(kù)和分布式數(shù)據(jù)庫(kù)[120-122]。本文基于數(shù)據(jù)分布方式,將現(xiàn)有主流植物表型數(shù)據(jù)庫(kù)劃分為集中式植物表型數(shù)據(jù)庫(kù)和分布式植物表型數(shù)據(jù)庫(kù)分別闡述。
表5 典型植物表型數(shù)據(jù)庫(kù)
2.2.1 集中式植物表型數(shù)據(jù)庫(kù)
集中式植物表型數(shù)據(jù)庫(kù)作為一種傳統(tǒng)數(shù)據(jù)庫(kù)類型,是表型數(shù)據(jù)被數(shù)據(jù)庫(kù)實(shí)例集中管理且不與其他計(jì)算機(jī)系統(tǒng)交互的數(shù)據(jù)庫(kù)系統(tǒng)。
如Reynolds等構(gòu)建的如圖3所示的小麥表型數(shù)據(jù)管理平臺(tái)CropSight,基于云端服務(wù)器和分布式設(shè)備對(duì)表型數(shù)據(jù)進(jìn)行集中式實(shí)時(shí)存儲(chǔ),為表型分析和農(nóng)業(yè)決策提供了先進(jìn)手段[123]。此外,該平臺(tái)通過存儲(chǔ)歷史試驗(yàn)的地理、環(huán)境及傳感器信息,可進(jìn)行作物多站點(diǎn)多年數(shù)據(jù)的比較分析。如圖4所示的數(shù)據(jù)集成管理和信息系統(tǒng)(DMIS)考慮到作物生長(zhǎng)發(fā)育過程中對(duì)時(shí)間與空間的依賴性,基于動(dòng)態(tài)PostgreSQL數(shù)據(jù)庫(kù)建立能夠存儲(chǔ)和管理包括圖像數(shù)據(jù)的CPED數(shù)據(jù)庫(kù),并使時(shí)間與地理坐標(biāo)自動(dòng)鏈接到所有數(shù)據(jù),從而精確解釋與環(huán)境相關(guān)的表型性狀數(shù)據(jù)[124]。
2.2.2 分布式植物表型數(shù)據(jù)庫(kù)
隨著表型數(shù)據(jù)的指數(shù)級(jí)增長(zhǎng)與數(shù)據(jù)庫(kù)技術(shù)的不斷革新,分布式存儲(chǔ)技術(shù)與系統(tǒng)成為表型數(shù)據(jù)庫(kù)的主流選擇[129]。分布式植物表型數(shù)據(jù)庫(kù)由多個(gè)位置上的多臺(tái)計(jì)算機(jī)構(gòu)成,通過將表型數(shù)據(jù)分散存儲(chǔ)到多個(gè)通過網(wǎng)絡(luò)連接的數(shù)據(jù)存儲(chǔ)節(jié)點(diǎn)上,實(shí)現(xiàn)了更大的存儲(chǔ)容量與并發(fā)訪問量。同時(shí),其可根據(jù)需要增減結(jié)點(diǎn),個(gè)別結(jié)點(diǎn)發(fā)生故障時(shí)仍可降級(jí)工作,數(shù)據(jù)分散管理、統(tǒng)一協(xié)調(diào),具有堅(jiān)固性強(qiáng)、可靠性高、可擴(kuò)充性好且自治性優(yōu)等特點(diǎn)[130]。
注:A:CropSight方便用戶使用有線(即以太網(wǎng)電纜)或無(wú)線連接(如WiFi網(wǎng)絡(luò))與分布式表型設(shè)備進(jìn)行交互;CropSight 客戶端支持遠(yuǎn)程控制和板載數(shù)據(jù)管理。B:用戶可以通過集中式CropSight服務(wù)器近乎實(shí)時(shí)地連接、監(jiān)控和管理試驗(yàn);通過專用網(wǎng)絡(luò),CropSight 后端服務(wù)器在SQL數(shù)據(jù)庫(kù)中整理和集成基于傳感器的大規(guī)模表型數(shù)據(jù)集。
注:用戶通過瀏覽器對(duì)服務(wù)器上的應(yīng)用程序進(jìn)行身份驗(yàn)證和訪問。系統(tǒng)組件安裝在Dockers中,并通過HTTP相互通信。客戶端在網(wǎng)絡(luò)層中本地化,數(shù)據(jù)層包括PostgreSQ和PostGIS數(shù)據(jù)庫(kù)組件,GeoServer和中間件組成的服務(wù)層連接客戶端應(yīng)用程序和數(shù)據(jù)庫(kù)。
Note: Users authenticate and access the application on the server via browsers. The system components are installed in Dockers and communicate with each other via HTTP. The GUI (Client) is localized in the Weblayer. The PostgreSQL and PostGIS database components in the Datalayer. The GeoServer and a Middleware are acting in the Servicelayer, connecting the client application and the database.
圖4 數(shù)據(jù)集成管理和信息系統(tǒng)(DMIS)體系結(jié)構(gòu)圖[124]
Fig.4 System architecture of Data Management and Information System (DMIS)[124]
數(shù)據(jù)庫(kù)Planteome是一個(gè)包含植物表型組學(xué)、基因組學(xué)和遺傳學(xué)數(shù)據(jù)的可免費(fèi)訪問的共享型平臺(tái)。為實(shí)現(xiàn)數(shù)據(jù)格式的標(biāo)準(zhǔn)化,其提供了一套公開可用、相互關(guān)聯(lián)的參考本體,并采用參考本體術(shù)語(yǔ)對(duì)95個(gè)植物的表型性狀數(shù)據(jù)、基因表達(dá)數(shù)據(jù)等進(jìn)行注釋[125]。應(yīng)用于植物基因組學(xué)與表型組學(xué)研究的數(shù)據(jù)存儲(chǔ)庫(kù)PGP涵蓋了高通量表型平臺(tái)采集的圖像數(shù)據(jù)、基因分型數(shù)據(jù)、質(zhì)譜數(shù)據(jù)等各類跨域數(shù)據(jù)集,且提供對(duì)其數(shù)據(jù)的訪問功能,滿足可發(fā)現(xiàn),可訪問,可互操作,可重用的FAIR數(shù)據(jù)原則[126]。如圖5所示的開源表型混合信息系統(tǒng)(PHIS)采用本體驅(qū)動(dòng)架構(gòu)實(shí)現(xiàn)來(lái)自受控環(huán)境和野外環(huán)境下的多源、多尺度植物表型數(shù)據(jù)的集成、管理、共享與可視化,同時(shí)其可通過Web服務(wù)與外部數(shù)據(jù)庫(kù)進(jìn)行互操作[127]。類似地,植物表型組學(xué)數(shù)據(jù)存儲(chǔ)庫(kù)GnpIS可存儲(chǔ)包括環(huán)境數(shù)據(jù)的田間與溫室試驗(yàn)數(shù)據(jù),并能夠與其他數(shù)據(jù)存儲(chǔ)庫(kù)進(jìn)行互操作[128]。
挖掘并解析高通量表型平臺(tái)采集的植物表型數(shù)據(jù)是種質(zhì)資源研究的有效手段。隨著多尺度、多生境、多源異構(gòu)的表型數(shù)據(jù)的不斷積累,提升數(shù)據(jù)管理技術(shù)迫在眉睫[131]。云存儲(chǔ)技術(shù)為植物表型大數(shù)據(jù)的存儲(chǔ)、管理與共享提供了新思路。如Debauche等將云基礎(chǔ)設(shè)施鏈接到應(yīng)用程序托管平臺(tái),近乎實(shí)時(shí)地收集、存儲(chǔ)和處理大量與時(shí)間序列相關(guān)的二維、三維和高光譜圖像數(shù)據(jù),并確保了研究團(tuán)隊(duì)之間交換、共享和訪問數(shù)據(jù)的同時(shí)數(shù)據(jù)的可追溯性、隱私性和安全性[132]。因此,為提高數(shù)據(jù)傳輸、存儲(chǔ)與解析效率,推動(dòng)數(shù)據(jù)檢索、追溯與訪問的便利化,應(yīng)基于“云技術(shù)”等數(shù)據(jù)存儲(chǔ)管理手段,著力建設(shè)大型綜合性植物表型標(biāo)準(zhǔn)數(shù)據(jù)庫(kù)與平臺(tái),促進(jìn)表型組學(xué)大數(shù)據(jù)共享與協(xié)同交叉合作研究。
注:PHIS由5個(gè)主要組件組成,這些組件以不同的層構(gòu)建,其中包括Web用戶界面,數(shù)據(jù)和知識(shí)存儲(chǔ)層,Web服務(wù)層,智能層和科學(xué)計(jì)算和工作流層。
規(guī)模化環(huán)境可控的室內(nèi)高通量種質(zhì)資源表型平臺(tái)集成了不斷發(fā)展的無(wú)損成像技術(shù)、表型圖像數(shù)據(jù)解析技術(shù)、海量多源異構(gòu)表型數(shù)據(jù)管理技術(shù),是未來(lái)高通量種質(zhì)資源鑒定與評(píng)估的關(guān)鍵助推力,也為基因型-表型-環(huán)境融合育種新模式提供了可行性方案與工具[29]。但目前,其在設(shè)計(jì)研發(fā)構(gòu)建、圖像獲取解析、數(shù)據(jù)管理共享等方面仍然存在諸多問題和挑戰(zhàn)亟待完善與解決。
目前國(guó)內(nèi)外研發(fā)的各類室內(nèi)高通量種質(zhì)資源表型平臺(tái)可以實(shí)現(xiàn)自動(dòng)化、高通量、全生育期的植物表型數(shù)據(jù)獲取。但表型平臺(tái)的測(cè)量精度會(huì)受到環(huán)境變化、傳送帶移動(dòng)、葉片遮擋等因素的影響,且平臺(tái)建設(shè)安裝、運(yùn)行維護(hù)成本高,限制了大部分科研單位對(duì)植物表型、種質(zhì)資源鑒定及育種改良的研究。此外,傳送帶式和軌道式表型平臺(tái)使用區(qū)域擴(kuò)展性低,靈活性較差[28]。室內(nèi)表型機(jī)器人雖能夠有效提升表型獲取的便捷性與吞吐量,但目前仍處于研發(fā)初期,在算法、控制器及傳感器等方面有較大改善空間。
同時(shí),現(xiàn)有表型平臺(tái)采用的數(shù)據(jù)傳輸方式大多為硬盤讀寫,限制了實(shí)時(shí)查看與解析能力,通過整合在線傳輸技術(shù),提高傳輸效率是改善用戶體驗(yàn)、提高平臺(tái)可用性的有效措施。因此,為符合大多數(shù)科研機(jī)構(gòu)及實(shí)驗(yàn)室對(duì)表型平臺(tái)的需求,提升植物表型信息獲取的靈活性、準(zhǔn)確性和高效性,亟待研發(fā)改進(jìn)高精度、多樣化、便捷式、低成本的室內(nèi)高通量種質(zhì)資源表型平臺(tái)。
人工智能、深度學(xué)習(xí)等計(jì)算機(jī)技術(shù)的高速發(fā)展推動(dòng)著植物表型圖像數(shù)據(jù)解析技術(shù)進(jìn)入新紀(jì)元,但其需要足夠多的數(shù)據(jù)量和足夠長(zhǎng)的訓(xùn)練時(shí)間。由于公開植物表型數(shù)據(jù)集的缺乏,現(xiàn)有訓(xùn)練模型通常不具備較強(qiáng)的魯棒性和泛化能力。此外,深度學(xué)習(xí)雖對(duì)海量表型數(shù)據(jù)具有強(qiáng)大的特征提取與分類預(yù)測(cè)的能力,但其基于神經(jīng)網(wǎng)絡(luò)提取的特征通常缺乏生物學(xué)意義的解釋。未來(lái)應(yīng)注重窺視“黑匣子”并了解神經(jīng)網(wǎng)絡(luò)決策過程,打破“信息瓶頸”,提升特征可解釋性,從而與已識(shí)別特征相關(guān)聯(lián),進(jìn)一步提高挖掘數(shù)據(jù)能力。
對(duì)于不同表型平臺(tái)及其所搭載的不同傳感器獲取的多尺度、多生境、多模態(tài)和多維度的表型信息,目前的解析技術(shù)大多只能分析單個(gè)尺度、維度的表型數(shù)據(jù),或者對(duì)多維度表型數(shù)據(jù)解析的結(jié)果進(jìn)行簡(jiǎn)單融合與分析,數(shù)據(jù)整合效率較低,挖掘表型性狀信息有限[7]。因此,為更高精度、高效率、高通量的識(shí)別、分類、評(píng)估和預(yù)測(cè)植物表型性狀,亟待創(chuàng)新植物表型數(shù)據(jù)深度融合解析技術(shù),以建立表型-基因型-環(huán)境信息之間、不同傳感器之間的表型數(shù)據(jù)紐帶,加強(qiáng)表型信息整合提取的深度與廣度。
隨著高通量種質(zhì)資源表型平臺(tái)的研發(fā)與應(yīng)用,各類非結(jié)構(gòu)化表型數(shù)據(jù)不斷積累,植物表型標(biāo)準(zhǔn)化數(shù)據(jù)庫(kù)成為數(shù)據(jù)管理的主要解決方案。但目前構(gòu)建的表型數(shù)據(jù)庫(kù)大多規(guī)模較小,彼此獨(dú)立,挖掘潛力有限,各研究團(tuán)隊(duì)之間的表型信息難以高效訪問與共享。且由于缺乏統(tǒng)一的表型數(shù)據(jù)標(biāo)準(zhǔn)與規(guī)范體系,不同表型平臺(tái)采集的數(shù)據(jù)在命名、格式、精度、標(biāo)注、完整性等各方面大相徑庭,增大了數(shù)據(jù)解析、存儲(chǔ)、傳輸與管理的難度與成本。
盡管FAIR數(shù)據(jù)原則已被提議作為表型數(shù)據(jù)管理共享的標(biāo)準(zhǔn)并被普遍接受,但由于不同國(guó)家和不同研究團(tuán)隊(duì)試驗(yàn)獲取的表型數(shù)據(jù)缺乏可訪問性,一定程度上阻礙了數(shù)據(jù)標(biāo)準(zhǔn)的建立和開放。而國(guó)際植物表型網(wǎng)絡(luò)IPPN、歐洲植物表型網(wǎng)絡(luò)EPPN、北美植物表型網(wǎng)絡(luò)NAPPA等植物表型研究組織和相關(guān)機(jī)構(gòu)網(wǎng)絡(luò)的發(fā)展與壯大有望為實(shí)現(xiàn)表型數(shù)據(jù)的標(biāo)準(zhǔn)共享化提供途徑與可能[85]。因此,為推動(dòng)作物育種研究和植物表型組學(xué)發(fā)展,促進(jìn)全球研究團(tuán)隊(duì)與機(jī)構(gòu)交叉合作,亟待建設(shè)一套植物表型數(shù)據(jù)標(biāo)準(zhǔn)體系與一個(gè)存儲(chǔ)、處理、開放和共享的綜合性植物表型大數(shù)據(jù)平臺(tái)。
綜上所述,室內(nèi)高通量種質(zhì)資源表型平臺(tái)自動(dòng)化、規(guī)模化獲取、解析與管理植物表型信息,彌補(bǔ)了基因組數(shù)據(jù)挖掘?qū)Ρ硇蛿?shù)據(jù)的迫切需求,對(duì)種質(zhì)資源評(píng)估具有不可替代的重要作用。為推動(dòng)表型平臺(tái)建設(shè)和表型數(shù)據(jù)解析管理的進(jìn)一步發(fā)展,應(yīng)打破表型組學(xué)單一領(lǐng)域研究的壁壘,促進(jìn)基因組學(xué)、遺傳學(xué)、表型組學(xué)等多重組學(xué)和多學(xué)科的交叉協(xié)作研究,以實(shí)現(xiàn)植物表型大數(shù)據(jù)的價(jià)值最大化。
[1] 王曉鳴,邱麗娟,景蕊蓮,等. 作物種質(zhì)資源表型性狀鑒定評(píng)價(jià):現(xiàn)狀與趨勢(shì)[J]. 植物遺傳資源學(xué)報(bào),2022,23(1):12-20.
Wang Xiaoming, Qiu Lijuan, Jing Ruilian, et al. Evaluation on phenotypic traits of crop germplasm: Status and development[J]. Journal of Plant Genetic Resources, 2022, 23(1): 12-20. (in Chinese with English abstract)
[2] Bohra A, Kilian B, Sivasankar S, et al. Reap the crop wild relatives for breeding future crops[J]. Trends in Biotechnology, 2021, 40(4): 412-431.
[3] 張鳳蘭. 我國(guó)蔬菜種業(yè)發(fā)展成效和趨勢(shì)[J]. 蔬菜,2022,41(5):1-5.
Zhang Fenglan. The development effect and trend of vegetable seed industry in China[J]. Vegetables, 2022, 41(5): 1-5. (in Chinese with English abstract)
[4] 張雪松,蘇彥斌,陳小文,等. 我國(guó)植物種質(zhì)資源的搜集、保護(hù)與發(fā)展[J]. 中國(guó)野生植物資源,2022,41(3):96-102.
Zhang Xuesong, Su Yanbin, Chen Xiaowen, et al. Collection, conservation and development of plant germplasm resources in China[J]. China Wild Plant Resources, 2022, 41(3): 96-102. (in Chinese with English abstract)
[5] Reynolds M, Atkin O K, Bennett M, et al. Addressing research bottlenecks to crop productivity[J]. Trends in Plant Science, 2021, 26(6): 607-630.
[6] Breman E, Ballesteros D, Castillo-Lorenzo E, et al. Plant diversity conservation challenges and prospects: The perspective of botanic gardens and the millennium seed bank[J]. Plants, 2021, 10(11): 2371.
[7] 趙春江. 植物表型組學(xué)大數(shù)據(jù)及其研究進(jìn)展[J]. 農(nóng)業(yè)大數(shù)據(jù)學(xué)報(bào),2019,1(2):5-18.
Zhao Chunjiang. Big data of plant phenomic and its research progress[J]. Journal of Agricultural Big Data, 2019, 1(2): 5-18. (in Chinese with English abstract)
[8] Sobkowiak S, Janiszewska M, Stefańczyk E, et al. Quantitative trait loci for resistance to potato dry rot caused by fusarium sambucinum[J]. Agronomy, 2022, 12(1): 203.
[9] Amas J, Anderson R, Edwards D, et al. Status and advances in mining for blackleg (leptosphaeria maculans) quantitative resistance (QR) in oilseed rape (brassica napus)[J]. Theoretical and Applied Genetics, 2021, 134(10): 3123-3145.
[10] Jasani M D, Kamdar J H, Bera S, et al. Novel and stable major QTLs conferring resistance to peanut bud necrosis disease and identification of resistant high yielding peanut breeding lines[J]. Euphytica, 2021, 217(6): 1-13.
[11] Chen D, Neumann K, Friedel S, et al. Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis[J]. The Plant Cell, 2014, 26(12): 4636-4655.
[12] 張慧春,周宏平,鄭加強(qiáng),等. 植物表型平臺(tái)與圖像分析技術(shù)研究進(jìn)展與展望[J]. 農(nóng)業(yè)機(jī)械學(xué)報(bào),2020,51(3):1-17.
Zhang Huichun, Zhou Hongping, Zheng Jiaqiang, et al. Research progress and prospect of plant phenotyping platform and image analysis technology[J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(3): 1-17. (in Chinese with English abstract)
[13] Huang Y, Shang M, Liu T, et al. High-throughput methods for genome editing: the more the better[J]. Plant Physiology, 2022, 188(4): 1731-1745.
[14] Campos M D, Félix M R, Patanita M, et al. High throughput sequencing unravels tomato-pathogen interactions towards a sustainable plant breeding[J]. Horticulture Research, 2021, 8(1): 171.
[15] Huang Y, Liu Y, Liu C, et al. Prospects and challenges of epigenomics in crop improvement[J]. Genes & Genomics, 2021, 44(3): 251-257.
[16] Gerland P, Raftery A E, ?ev?íková H, et al. World population stabilization unlikely this century[J]. Science, 2014, 346(6206): 234-237.
[17] Deery D M, Rebetzke G J, Jimenez-Berni J A, et al. Methodology for high-throughput field phenotyping of canopy temperature using airborne thermography[J]. Frontiers in Plant Science, 2016, 7: 1808.
[18] McCouch S, Baute G J, Bradeen J, et al. Feeding the future[J]. Nature, 2013, 499(7456): 23-24.
[19] Rahaman M, Ahsan M, Chen M. Data-mining techniques for image-based plant phenotypic traits identification and classification[J]. Scientific Reports, 2019, 9(1): 1-11.
[20] Prasanna B M, Araus J L, Crossa J, et al. High-throughput and Precision Phenotyping for Cereal Breeding Programs[M]. Berlin: Springer, 2013: 341-374.
[21] Granier C, Vile D. Phenotyping and beyond: Modelling the relationships between traits[J]. Current Opinion in Plant Biology, 2014, 18: 96-102.
[22] 程曼,袁洪波,蔡振江. 田間作物高通量表型信息獲取與分析技術(shù)研究進(jìn)展[J]. 農(nóng)業(yè)機(jī)械學(xué)報(bào),2020,51(S1):314-324.
Cheng Man, Yuan Hongbo, Cai Zhenjiang. Review of field-based information acquisition and analysis of high-throughput phenotyping[J].Transactions of the Chinese Society of Agricultural Machinery, 2020, 51(S1): 314-324. (in Chinese with English abstract)
[23] Joshi S, Thoday-Kennedy E, Daetwyler H D, et al. High-throughput phenotyping to dissect genotypic differences in safflower for drought tolerance[J]. PLoS One, 2021, 16(7): e0254908.
[24] Dissanayake R, Kahrood H V, Dimech A M, et al. Development and application of image-based high-throughput phenotyping methodology for salt tolerance in lentils[J]. Agronomy, 2020, 10(12): 1992.
[25] Pieruschka R, Schurr U. Plant phenotyping: Past, present, and future[J]. Plant Phenomics, 2019, 2019: 7507131.
[26] Roitsch T, Cabrera-Bosquet L, Fournier A, et al. New sensors and data-driven approaches: A path to next generation phenomics[J]. Plant Science, 2019, 282: 2-10.
[27] Singh A, Jones S, Ganapathysubramanian B, et al. Challenges and opportunities in machine-augmented plant stress phenotyping[J]. Trends in Plant Science, 2021, 26(1): 53-69.
[28] 徐凌翔,陳佳瑋,丁國(guó)輝,等. 室內(nèi)植物表型平臺(tái)及性狀鑒定研究進(jìn)展和展望[J]. 智慧農(nóng)業(yè),2020,2(1):23-42.
Xu Lingxiang, Chen Jiawei, Ding Guohui, et al. Indoor phenotyping platforms and associated trait measurement: Progress and prospects[J]. Smart Agriculture, 2020, 2(1): 23-42. (in Chinese with English abstract)
[29] Yang W, Feng H, Zhang X, et al. Crop phenomics and high-throughput phenotyping: Past decades, current challenges, and future perspectives[J]. Molecular Plant, 2020, 13(2): 187-214.
[30] 蘇寶峰,劉昱麟,黃彥川,等. 群體小麥條銹病發(fā)病動(dòng)態(tài)無(wú)人機(jī)遙感監(jiān)測(cè)方法[J]. 農(nóng)業(yè)工程學(xué)報(bào),2021,37(23):127-135.
Su Baofeng, Liu Yulin, Huang Yanchuan, et al. Analysis for stripe rust dynamics in wheat population using UAV remote sensing[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(23): 127-135. (in Chinese with English abstract)
[31] 盧少志. 田間作物表型檢測(cè)平臺(tái)設(shè)計(jì)與試驗(yàn)[D]. 武漢:華中農(nóng)業(yè)大學(xué),2021.
Lu Shaozhi. Design and Experiment of Field Crop Phenotype Detection Platform[D]. Wuhan: Huazhong Agricultural University, 2021. (in Chinese with English abstract)
[32] 劉建剛,趙春江,楊貴軍,等. 無(wú)人機(jī)遙感解析田間作物表型信息研究進(jìn)展[J]. 農(nóng)業(yè)工程學(xué)報(bào),2016,32(24):98-106.
Liu Jiangang, Zhao Chunjiang, Yang Guijun, et al. Review of field-based phenotyping by unmanned aerial vehicle remote sensing platform[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2016, 32(24): 98-106. (in Chinese with English abstract)
[33] 胡偉娟,傅向東,陳凡,等. 新一代植物表型組學(xué)的發(fā)展之路[J]. 植物學(xué)報(bào),2019,54(5):558.
Hu Weijuan, Fu Xiangdong, Chen Fan, et al. A path to next generation of plant phenomics[J]. Chinese Bulletin of Botany, 2019, 54(5): 558. (in Chinese with English abstract)
[34] Langstroff A, Heuermann M C, Stahl A, et al. Opportunities and limits of controlled-environment plant phenotyping for climate response traits[J]. Theoretical and Applied Genetics, 2021, 135(1): 1-16.
[35] Virlet N, Sabermanesh K, Sadeghi-Tehran P, et al. Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring[J]. Functional Plant Biology, 2016, 44(1): 143-153.
[36] Murman J N. Flex-Ro: A Robotic High Throughput Field Phenotyping System[D]. Lincoln: University of Nebraska-Lincoln, 2019.
[37] Shafiekhani A, Kadam S, Fritschi F B, et al. Vinobot and vinoculer: Two robotic platforms for high-throughput field phenotyping[J]. Sensors, 2017, 17(1): 214.
[38] Messina G, Modica G. Applications of UAV thermal imagery in precision agriculture: State of the art and future research outlook[J]. Remote Sensing, 2020, 12(9): 1491.
[39] Zheng C, Abd-Elrahman A, Whitaker V. Remote sensing and machine learning in crop phenotyping and management, with an emphasis on applications in strawberry farming[J]. Remote Sensing, 2021, 13(3): 531.
[40] Jin X, Zarco-Tejada P J, Schmidhalter U, et al. High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms[J]. IEEE Geoscience and Remote Sensing Magazine, 2020, 9(1): 200-231.
[41] Candiago S, Remondino F, De Giglio M, et al. Evaluating multispectral images and vegetation indices for precision farming applications from UAV images[J]. Remote Sensing, 2015, 7(4): 4026-4047.
[42] Shin Y K, Bhandari S R, Cho M C, et al. Evaluation of chlorophyll fluorescence parameters and proline content in tomato seedlings grown under different salt stress conditions[J]. Horticulture, Environment, and Biotechnology, 2020, 61(3): 433-443.
[43] Li D, Quan C, Song Z, et al. High-throughput plant phenotyping platform (HT3P) as a novel tool for estimating agronomic traits from the lab to the field[J]. Frontiers in Bioengineering and Biotechnology, 2021, 8: 623705.
[44] Cheng J C, Lertpiriyapong K, Wang S, et al. The role of the Arabidopsis ELD1 gene in cell development and photomorphogenesis in darkness[J]. Plant Physiology, 2000, 123(2): 509-520.
[45] Pineda M, Pérez-Bueno M L, Paredes V, et al. Use of multicolour fluorescence imaging for diagnosis of bacterial and fungal infection on zucchini by implementing machine learning[J]. Functional Plant Biology, 2017, 44(6): 563-572.
[46] Wendt T, Holme I, Dockter C, et al. HvDep1 is a positive regulator of culm elongation and grain size in barley and impacts yield in an environment-dependent manner[J]. PLoS One, 2016, 11(12): e0168924.
[47] Koc A, Odilbekov F, Alamrani M, et al. Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning[J]. Plant Methods, 2022, 18(1): 1-11.
[48] Aboutalebi M, Torres-Rua A F, McKee M, et al. Incorporation of unmanned aerial vehicle (UAV) point cloud products into remote sensing evapotranspiration models[J]. Remote Sensing, 2019, 12(1): 50.
[49] Babst B A, Gao F, Acosta-Gamboa L M, et al. Three NPF genes in Arabidopsis are necessary for normal nitrogen cycling under low nitrogen stress[J]. Plant Physiology and Biochemistry, 2019, 143: 1-10.
[50] LemnaTec GmbH. HyperAlxpert[Z/OL]. (2018)[2022-05-25]. https://www.lemnatec.com/products-and-solutions/hyperaixpert/.
[51] Czedik‐Eysenberg A, Seitner S, Güldener U, et al. The ‘PhenoBox’: A flexible, automated, open‐source plant phenotyping solution[J]. New Phytologist, 2018, 219(2): 808-823.
[52] Hu X, Li X Y, Li Z C, et al. Linking 3-D soil macropores and root architecture to near saturated hydraulic conductivity of typical meadow soil types in the Qinghai Lake Watershed, northeastern Qinghai-Tibet Plateau[J]. Catena, 2020, 185: 104287.
[53] Das Choudhury S, Samal A, Awada T. Leveraging image analysis for high-throughput plant phenotyping[J]. Frontiers in Plant Science, 2019, 10: 508.
[54] 丁小明. 溫室環(huán)境下植物表型研究平臺(tái)[J]. 農(nóng)業(yè)工程技術(shù),2019,39(7):82-85.
Ding Xiaoming. Plant phenotype research platform in greenhouse environment[J]. Agricultural Engineering Technology, 2019, 39(7): 82-85. (in Chinese with English abstract)
[55] Yang W, Guo Z, Huang C, et al. Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice[J]. Nature Communications, 2014, 5(1): 1-9.
[56] Duan L, Han J, Guo Z, et al. Novel digital features discriminate between drought resistant and drought sensitive rice under controlled and field conditions[J]. Frontiers in Plant Science, 2018, 9: 492.
[57] Tisné S, Serrand Y, Bach L, et al. Phenoscope: An automated large‐scale phenotyping platform offering high spatial homogeneity[J]. The Plant Journal, 2013, 74(3): 534-544.
[58] Guo Q, Wu F, Pang S, et al. Crop 3D: A LiDAR based platform for 3D high-throughput crop phenotyping[J]. Science China Life Sciences, 2018, 61(3): 328-339.
[59] Wang L, Liu F, Hao X, et al. Identification of the QTL-allele system underlying two high-throughput physiological traits in the Chinese soybean germplasm population[J]. Frontiers in Genetics, 2021, 12: 600444.
[60] Bao Y, Zarecor S, Shah D, et al. Assessing plant performance in the Enviratron[J]. Plant Methods, 2019, 15(1): 1-14.
[61] Piovesan A, Vancauwenberghe V, van De Looverbosch T, et al. X-ray computed tomography for 3D plant imaging[J]. Trends in Plant Science, 2021, 26(11): 1171-1185.
[62] Hou L H, Gao W, Weng Z H, et al. Use of X-ray tomography for examining root architecture in soils[J]. Geoderma, 2022, 405: 115405.
[63] Kurogane T, Tamaoki D, Yano S, et al. Visualization of Arabidopsis root system architecture in 3D by refraction-contrast X-ray micro-computed tomography[J]. Microscopy, 2021, 70(6): 536-544.
[64] Allen L H, Boote K J, Jones J W, et al. Sunlit, controlled‐environment chambers are essential for comparing plant responses to various climates[J]. Agronomy Journal, 2020, 112(6): 4531-4549.
[65] Jez J M, Topp C N, Yao L, et al. Recent developments and potential of robotics in plant eco-phenotyping[J]. Emerging Topics in Life Sciences, 2021, 5(2): 289-300.
[66] Das Choudhury S, Bashyam S, Qiu Y, et al. Holistic and component plant phenotyping using temporal image sequence[J]. Plant Methods, 2018, 14(1): 1-21.
[67] Marchetti C F, Ugena L, Humplík J F, et al. A novel image-based screening method to study water-deficit response and recovery of barley populations using canopy dynamics phenotyping and simple metabolite profiling[J]. Frontiers in Plant Science, 2019, 10: 1252.
[68] Paul K, Sorrentino M, Lucini L, et al. Understanding the biostimulant action of vegetal-derived protein hydrolysates by high-throughput plant phenotyping and metabolomics: A case study on tomato[J]. Frontiers in Plant Science, 2019, 10: 47.
[69] Fang W, Feng H, Yang W, et al. High-throughput volumetric reconstruction for 3D wheat plant architecture studies[J]. Journal of Innovative Optical Health Sciences, 2016, 9(5): 1650037.
[70] Xiong X, Yu L, Yang W, et al. A high-throughput stereo-imaging system for quantifying rape leaf traits during the seedling stage[J]. Plant Methods, 2017, 13(1): 1-17.
[71] Zhang X, Huang C, Wu D, et al. High-throughput phenotyping and QTL mapping reveals the genetic architecture of maize plant growth[J]. Plant Physiology, 2017, 173(3): 1554-1564.
[72] Ma D, Carpenter N, Amatya S, et al. Removal of greenhouse microclimate heterogeneity with conveyor system for indoor phenotyping[J]. Computers and Electronics in Agriculture, 2019, 166: 104979.
[73] Hassanijalilian O, Igathinathane C, Doetkott C, et al. Chlorophyll estimation in soybean leaves infield with smartphone digital imaging and machine learning[J]. Computers and Electronics in Agriculture, 2020, 174: 105433.
[74] Pandey P, Dakshinamurthy H N, Young S N. Frontier: Autonomy in detection, actuation, and planning for robotic weeding systems[J]. Transactions of the ASABE, 2021, 64(2): 557-563.
[75] Yan B, Fan P, Lei X, et al. A real-time apple targets detection method for picking robot based on improved YOLOv5[J]. Remote Sensing, 2021, 13(9): 1619.
[76] Gerhards R, Andujar Sanchez D, Hamouz P, et al. Advances in site‐specific weed management in agriculture: A review[J]. Weed Research, 2022, 62(2): 123-133.
[77] Tang Y, Chen M, Wang C, et al. Recognition and localization methods for vision-based fruit picking robots: A review[J]. Frontiers in Plant Science, 2020, 11: 510.
[78] Atefi A, Ge Y, Pitla S, et al. Robotic technologies for high-throughput plant phenotyping: Contemporary reviews and future perspectives[J]. Frontiers in Plant Science, 2021, 12: 611940.
[79] Wu C, Zeng R, Pan J, et al. Plant phenotyping by deep-learning-based planner for multi-robots[J]. IEEE Robotics and Automation Letters, 2019, 4(4): 3113-3120.
[80] Schulz H, Baranska M. Identification and quantification of valuable plant substances by IR and Raman spectroscopy[J]. Vibrational Spectroscopy, 2007, 43(1): 13-25.
[81] Biskup B, Scharr H, Fischbach A, et al. Diel growth cycle of isolated leaf discs analyzed with a novel, high-throughput three-dimensional imaging method is identical to that of intact leaves[J]. Plant Physiology, 2009, 149(3): 1452-1461.
[82] Fan J, Zhang Y, Wen W, et al. The future of Internet of things in agriculture: Plant high-throughput phenotypic platform[J]. Journal of Cleaner Production, 2021, 280(1): 123651.
[83] Kim M, Lee C, Hong S, et al. High-throughput phenotyping methods for breeding drought-tolerant crops[J]. International Journal of Molecular Sciences, 2021, 22(15): 8266.
[84] Danilevicz M F, Bayer P E, Nestor B J, et al. Resources for image-based high-throughput phenotyping in crops and data sharing challenges[J]. Plant Physiology, 2021, 187(2): 699-715.
[85] Sun D, Robbins K, Morales N, et al. Advances in optical phenotyping of cereal crops[J]. Trends in Plant Science, 2021, 27(2): 191-208.
[86] Ninomiya S. High-throughput field crop phenotyping: Current status and challenges[J]. Breeding Science, 2022, 72(1): 3-18.
[87] Fernandes A F A, Dórea J R R, Rosa G J M. Image analysis and computer vision applications in animal sciences: An overview[J]. Frontiers in Veterinary Science, 2020, 7: 551269.
[88] Liu H, Bruning B, Garnett T, et al. Hyperspectral imaging and 3D technologies for plant phenotyping: From satellite to close-range sensing[J]. Computers and Electronics in Agriculture, 2020, 175: 105621.
[89] Mazis A, Choudhury S D, Morgan P B, et al. Application of high-throughput plant phenotyping for assessing biophysical traits and drought response in two oak species under controlled environment[J]. Forest Ecology and Management, 2020, 465: 118101.
[90] Arvidsson S, Pérez-Rodríguez P, Mueller-Roeber B. A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects[J]. New Phytologist, 2011, 191(3): 895-907.
[91] Padilla-Chacón D, Valdivia C B P, García-Esteva A, et al. Phenotypic variation and biomass partitioning during post-flowering in two common bean cultivars (L.) under water restriction[J]. South African Journal of Botany, 2019, 121: 98-104.
[92] Vadez V, Kholová J, Hummel G, et al. LeasyScan: A novel concept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget[J]. Journal of Experimental Botany, 2015, 66(18): 5581-5593.
[93] Klukas C, Chen D, Pape J M. Integrated analysis platform: an open-source information system for high-throughput plant phenotyping[J]. Plant Physiology, 2014, 165(2): 506-518.
[94] Zhou J, Applegate C, Alonso A D, et al. Leaf-GP: An open and automated software application for measuring growth phenotypes for arabidopsis and wheat[J]. Plant Methods, 2017, 13(1): 1-17.
[95] Minervini M, Giuffrida M V, Perata P, et al. Phenotiki: An open software and hardware platform for affordable and easy image-based phenotyping of rosette-shaped plants[J]. The Plant Journal, 2017, 90(1): 204-216.
[96] Gaggion N, Ariel F, Daric V, et al. ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture[J]. GigaScience, 2021, 10(7): giab052.
[97] Kumar J P, Domnic S. Image based leaf segmentation and counting in rosette plants[J]. Information Processing in Agriculture, 2019, 6(2): 233-246.
[98] Hartmann A, Czauderna T, Hoffmann R, et al. HTPheno: An image analysis pipeline for high-throughput plant phenotyping[J]. BMC Bioinformatics, 2011, 12(1): 1-9.
[99] Vasseur F, Bresson J, Wang G, et al. Image-based methods for phenotyping growth dynamics and fitness components in Arabidopsis thaliana[J]. Plant Methods, 2018, 14(1): 1-11.
[100] 李寒,張漫,高宇,等. 溫室綠熟番茄機(jī)器視覺檢測(cè)方法[J]. 農(nóng)業(yè)工程學(xué)報(bào),2017,33(增刊1):328-334.
Li Han, Zhang Man, Gao Yu, et al. Green ripe tomato detection method based on machine vision in greenhouse[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(Supp.1): 328-334. (in Chinese with English abstract)
[101] Nabwire S, Suh H K, Kim M S, et al. Application of artificial intelligence in phenomics[J]. Sensors, 2021, 21(13): 4363.
[102] Sun S, Cao Z, Zhu H, et al. A survey of optimization methods from a machine learning perspective[J]. IEEE Transactions on Cybernetics, 2019, 50(8): 3668-3681.
[103] Ghosal D, Das A, Dhal K G. A comparative study among clustering techniques for leaf segmentation in rosette plants[J]. Pattern Recognition and Image Analysis, 2022, 32(1): 129-141.
[104] Liang X, Ye J, Li X, et al. A high-throughput and low-cost maize ear traits scorer[J]. Molecular Breeding, 2021, 41(2): 1-16.
[105] Tian Y, Xie L, Wu M, et al. Multicolor fluorescence imaging for the early detection of salt stress in arabidopsis[J]. Agronomy, 2021, 11(12): 2577.
[106] 柴宏紅,邵科,于超,等.基于三維點(diǎn)云的甜菜根表型參數(shù)提取與根型判別[J]. 農(nóng)業(yè)工程學(xué)報(bào),2020,36(10):181-188.
Chai Honghong, Shao Ke, Yu Chao, et al. Extraction of phenotypic parameters and discrimination of beet root types based on 3D point cloud[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(10): 181-188. (in Chinese with English abstract)
[107] Kar S, Purbey V K, Suradhaniwar S, et al. An ensemble machine learning approach for determination of the optimum sampling time for evapotranspiration assessment from high-throughput phenotyping data[J]. Computers and Electronics in Agriculture, 2021, 182: 105992.
[108] Ebersbach J, Khan N A, McQuillan I, et al. Exploiting high-throughput indoor phenotyping to characterize the founders of a structured B. napus breeding population[J]. Frontiers in Plant Science, 2021, 12: 780250.
[109] Zhou J, Fu X, Zhou S, et al. Automated segmentation of soybean plants from 3D point cloud using machine learning[J]. Computers and Electronics in Agriculture, 2019, 162: 143-153.
[110] 孫國(guó)祥,汪小旵,劉景娜,等. 基于相位相關(guān)的溫室番茄植株多模態(tài)三維重建方法[J]. 農(nóng)業(yè)工程學(xué)報(bào),2019,35(18):134-142.
Sun Guoxiang, Wang Xiaochan, Liu Jingna, et al. Multi-modal three-dimensional reconstruction of greenhouse tomato plants based on phase-correlation method[J]. Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE), 2019, 35(18): 134-142. (in Chinese with English abstract)
[111] Jiang Y, Li C. Convolutional neural networks for image-based high-throughput plant phenotyping: A review[J]. Plant Phenomics, 2020, 2020: 4152816.
[112] 汪小旵,吳忠賢,孫曄,等. 基于葉綠素?zé)晒獬上窦夹g(shù)的番茄苗熱害脅迫智能識(shí)別方法[J]. 農(nóng)業(yè)工程學(xué)報(bào),2022,38(7):171-179.
Wang Xiaochan, Wu Zhongxian, Sun Ye, et al. Intelligent identification of heat stress in tomato seedlings based on chlorophyll fluorescence imaging technology[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2022, 38(7): 171-179. (in Chinese with English abstract)
[113] 岑海燕,朱月明,孫大偉,等. 深度學(xué)習(xí)在植物表型研究中的應(yīng)用現(xiàn)狀與展望[J]. 農(nóng)業(yè)工程學(xué)報(bào),2020,36(9):1-16.
Cen Haiyan, Zhu Yueming, Sun Dawei, et al. Current status and future perspective of the application of deep learning in plant phenotype research[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(9): 1-16. (in Chinese with English abstract)
[114] Misra T, Arora A, Marwaha S, et al. SpikeSegNet: A deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging[J]. Plant Methods, 2020, 16(1): 1-20.
[115] Du J, Lu X, Fan J, et al. Image-based high-throughput detection and phenotype evaluation method for multiple lettuce varieties[J]. Frontiers in Plant Science, 2020, 11: 563386.
[116] Arya S, Sandhu K S, Singh J. Deep learning: As the new frontier in high-throughput plant phenotyping[J]. Euphytica, 2022, 218(4): 1-22.
[117] Gutierrez A, Ansuategi A, Susperregi L, et al. A benchmarking of learning strategies for pest detection and identification on tomato plants for autonomous scouting robots using internal databases[J]. Journal of Sensors, 2019, 2019: 5219471.
[118] Flores P, Zhang Z, Igathinathane C, et al. Distinguishing seedling volunteer corn from soybean through greenhouse color, color-infrared, and fused images using machine and deep learning[J]. Industrial Crops and Products, 2021, 161: 113223.
[119] Selby P, Abbeloos R, Backlund J E, et al. BrAPI: An application programming interface for plant breeding applications[J]. Bioinformatics, 2019, 35(20): 4147-4155.
[120] Pitman N C A, Suwa T, Ulloa Ulloa C, et al. Identifying gaps in the photographic record of the vascular plant flora of the Americas[J]. Nature Plants, 2021, 7(8): 1010-1014.
[121] Singh G, Kuzniar A, Brouwer M, et al. Linked data platform for solanaceae species[J]. Applied Sciences, 2020, 10(19): 6813.
[122] Yao L, Ge Z. Industrial big data modeling and monitoring framework for plant-wide processes[J]. IEEE Transactions on Industrial Informatics, 2020, 17(9): 6399-6408.
[123] Reynolds D, Ball J, Bauer A, et al. CropSight: A scalable and open-source information management system for distributed plant phenotyping and IoT-based crop management[J]. Gigascience, 2019, 8(3): giz009.
[124] Honecker A, Schumann H, Becirevic D, et al. Plant, space and time-linked together in an integrative and scalable data management system for phenomic approaches in agronomic field trials[J]. Plant Methods, 2020, 16(1): 1-13.
[125] Cooper L, Meier A, Laporte M A, et al. The planteome database: An integrated resource for reference ontologies, plant genomics and phenomics[J]. Nucleic Acids Research, 2018, 46(D1): D1168-D1180.
[126] Arend D, Junker A, Scholz U, et al. PGP repository: A plant phenomics and genomics data publication infrastructure[J]. Database, 2016, 2016: baw033.
[127] Neveu P, Tireau A, Hilgert N, et al. Dealing with multi-source and multi-scale information in plant phenomics: The ontology-driven Phenotyping Hybrid Information System[J]. New Phytologist, 2019, 221(1): 588-601.
[128] Pommier C, Michotey C, Cornut G, et al. Applying FAIR principles to plant phenotypic data management in GnpIS[J]. Plant Phenomics, 2019, 2019: 1671403.
[129] 王肇康. 分布式圖處理若干算法與統(tǒng)一圖處理編程框架研究[D]. 南京:南京大學(xué),2021.
Wang Zhaokang. Research on Several of Distributed Graph Processing Algorithms and A Unified Graph Programming Framework[D]. Nanjing: Nanjing University, 2021. (in Chinese with English abstract)
[130] 牟帥. 分布式事務(wù)并發(fā)控制關(guān)鍵技術(shù)研究[D]. 北京:清華大學(xué), 2015.
Mu Shuai. Extract More Concurrency from Distributed Transactions[D]. Beijing: Tsinghua University, 2015. (in Chinese with English abstract)
[131] Jin S, Sun X, Wu F, et al. Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 171: 202-223.
[132] Debauche O, Mahmoudi S A, De Cock N, et al. Cloud architecture for plant phenotyping research[J]. Concurrency and Computation: Practice and Experience, 2020, 32(17): e5661.
Research progress and prospect of indoor high-throughput germplasm phenotyping platforms
He Yong1, Li Xiyao1, Yang Guofeng1, Yu Zeyu1, Yang Ningyuan1, Feng Xuping1,2※, Xu Lijia3
(1.,,310058,; 2.,,310058,; 3.,,625014,)
Germplasm resources have been the important material fundamentals to improve crop breeding. It is a high demand for the accurate and high-throughput acquisition of crop phenotype information to evaluate germplasm resources. The indoor high-throughput germplasm phenotyping platform can be expected to provide precise environmental regulation, and automatic and non-destructive imaging with high efficiency using phenotypic data acquisition, analysis, and management technology. There is also an efficient, integrated, and large-scale solution to the germplasm resource evaluation. A profound impact can be found in crop breeding improvement and high-quality development of the seed industry. According to the structural design and the movement pattern between the sensors, this review aims to evaluate the indoor high-throughput phenotyping platform in terms of four types, including the desktop, conveyor belt, orbital phenotyping platform, and greenhouse plant phenotyping robot. The acquisition of plant phenotype data was only the first step in phenotype research. The screening and identification of germplasm resources were then realized to extract the phenotypic traits from the original image data. The standardized storage of phenotypic data was conducted for the cross-scale and cross-dimensional trait analysis. Simultaneously, a systematic investigation was made on the indoor plant phenotypic data interpretation and management techniques in recent years. The raw image data of plant phenotype and software greatly contributed to the robust, accurate, and automatic phenotype analysis. The commonly-used phenotype raw data analysis mainly included classical statistical analysis, image processing, traditional machine learning, and deep learning. Among them, deep learning presented far-reaching prospects in the resolution and throughput of plant phenotypes, as well as a large number of phenotypic traits. In addition, the phenotypic information also generated large amounts of complex and unstructured data, such as images, point clouds, and spectra. The data often originated in a variety of formats and lengths. An effective solution was to build the database for the unstructured plant phenotype in the standardized and integrated data management. A variety of plant phenotype databases and systems were developed to promote global crop breeding, germplasm resource assessment, and crop yield. Finally, some challenges were summarized for the indoor high-throughput phenotyping platforms and data interpretation. Three perspectives were proposed for the plant phenotype research of germplasm resource evaluation. 1) It was an urgent need to develop the high-precision, diverse, convenient, and low-cost indoor high-throughput germplasm phenotyping platforms. 2) An urgent need for innovation was the fusion analysis to identify, classify, evaluate and predict the plant phenotypic traits. 3) A big data platform was also needed to be constructed to store, process, open, and share the plant phenotypic information.In conclusion, the indoor high-throughput phenotyping platform can greatly contribute to the phenotypic data in genomic data mining, and the evaluation of germplasm.Cross-collaborative research should be implemented in genomics, genetics, and phenomics, in order to promote the phenotyping platform and data analysis.
crops; plant phenotyping; indoor phenotyping platform; phenotypic data analysis and management; high-throughput; germplasm resource assessment
10.11975/j.issn.1002-6819.2022.17.014
S-1
A
1002-6819(2022)-17-0127-15
何勇,李禧堯,楊國(guó)峰,等. 室內(nèi)高通量種質(zhì)資源表型平臺(tái)研究進(jìn)展與展望[J]. 農(nóng)業(yè)工程學(xué)報(bào),2022,38(17):127-141.doi:10.11975/j.issn.1002-6819.2022.17.014 http://www.tcsae.org
He Yong, Li Xiyao, Yang Guofeng, et al. Research progress and prospect of indoor high-throughput germplasm phenotyping platforms[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2022, 38(17): 127-141. (in Chinese with English abstract) doi:10.11975/j.issn.1002-6819.2022.17.014 http://www.tcsae.org
2022-05-25
2022-08-02
浙江省重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2021C02023);浙江省“領(lǐng)雁”研發(fā)攻關(guān)計(jì)劃項(xiàng)目(2022C02032)
何勇,博士,教授,研究方向?yàn)閿?shù)字農(nóng)業(yè)、農(nóng)業(yè)物聯(lián)網(wǎng)、數(shù)字鄉(xiāng)村和智能農(nóng)業(yè)裝備等。Email:yhe@zju.edu.cn
馮旭萍,博士,副研究員,研究方向?yàn)槔米魑锒喑叨鹊墓庾V圖像信息結(jié)合深度學(xué)習(xí)等計(jì)算機(jī)算法實(shí)現(xiàn)作物表型的快速獲取。Email:fengxp@zju.edu.cn