Apdo. Postal 6-641 CDMX, Mexico 06600 Email: … · Kang Yu, Achim Walter, Andreas Hund 7 ......

Apdo. Postal 6-641 CDMX, Mexico 06600Email: [email protected]

www.cimmyt.org

CIMMYTHeadquartered in Mexico, the International Maize and Wheat Improvement Center (CIMMYT) is the global leader in publicly funded research for development for wheat and maize and for wheat- and maize-based farming systems. CIMMYT works throughout the developing world with hundreds of partners, belongs to CGIAR and leads the CGIAR Research Programs on Wheat and Maize. CIMMYT receives support from CGIAR Fund Donors, national governments, foundations, development banks and other public and private agencies. For more information, visit: www.cimmyt.org.

IPPNInternational Plant Phenotyping Network is an association representing the major plant phenotyping centers. IPPN aims to provide all relevant information about plant phenotyping. The goal is to increase the visibility and impact of plant phenotyping and enable cooperation by fostering communication between stakeholders in academia, industry, government, and the general public. Through workshops and symposia, IPPN seeks to establish different working groups and distribute all relevant information about plant phenotyping in a web-based platform. For more information, visit: www.plant-phenotyping.org.

IWYPThe International Wheat Yield Partnership (IWYP), established under the Wheat Initiative is a new and unique coordinated international research initiative with a goal to generate the breakthroughs that will raise the genetic yield potential of wheat by up to 50% in the next two decades with the ultimate goal of generating significant yield improvements in farmers’ fields. IWYP is a voluntary consortium of international public funders, research organizations and private industry partners who share the common goal of significantly increasing the genetic yield potential of wheat in the near future. IWYP embodies an integrated, multi-disciplinary, international research program involving a combination of public and private funded research, in-kind support, and where possible, aligned with other current relevant research programs worldwide. For more information, visit: www.wheatinitiative.org.

MasAgroMasAgro is a CIMMYT-led project sponsored by Mexico’s Agriculture Department (SAGARPA) that strengthens agricultural productivity at all levels of Mexico’s maize and wheat value chains. MasAgro develops molecular atlases of maize and wheat to help researchers identify genetic traits that determine grain yield and adaptation capacity, and transfer those traits to the varieties farmers and consumers demand. MasAgro has established networks with public research institutions and Mexican seed companies that evaluate and exchange pre-commercial non-transgenic seed of maize varieties and hybrids adapted to tropical and subtropical production areas and to the Mexican Highlands. More than 300,000 farmers (21 percent are women) who participate in MasAgro apply sustainable conservation agriculture practices on more than 970,000 hectares sown to maize, wheat and associated crop varieties. MasAgro trains and provides technical advice to farmers who produce maize, wheat and associated crops so they can sow improved seed, reduce costs and increase productivity and income sustainably. For more information, visit: masagro.mx.

SAGARPAThe Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food, is a unit from the Federal Executive Branch of the Government of Mexico, which has among its objectives promoting the execution of a policy of support, which allows producers to improve their production practices, utilizing in a more efficient manner the competitive advantages from our agricultural, livestock and fisheries sectors, and integrating the economic activities from rural areas into larger productive chains, encouraging the participation of organizations of producers with economic projects on their own, as well as with the proposal of goals and objectives for the agricultural sector within the National Development Plan. For more information, visit: www.gob.mx/sagarpa.

This publication’s copyright (© 2016) is shared by the International Maize and Wheat Improvement Center (CIMMYT) and the International Plant Phenotyping Network (IPPN). All rights are reserved by these parties. Rights to all original content supplied for this publication remain with the original authors.

The designations employed in the presentation of materials in this publication do not imply the expression of any opinion whatsoever on the part of the Organizers of the Symposium, concerning the legal status of any country, territory, city, or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries. The opinions expressed are those of the author(s), and are not necessarily those of CIMMYT or IPPN. The organizers encourage fair use of this material.

Printed in Mexico.

About the International Plant Phenotyping Symposium

Phenotyping is key to understanding the physiological and genetic bases of plant growth and adaptation and their application for crop improvement. A revolution in phenomics is taking place, using non-invasive technologies based on spectral reflectance from plant tissue. Automated proximal sensing in controlled environments has sufficient resolution for detailed genetic and physiological dissection in model plant species. Aerial imaging using manned or unmanned low-flying vehicles or even satellites is transforming field phenotyping, at scales ranging from individual plots for breeding to entire fields to characterize agro-ecosystems.

The 4th annual Symposium focuses on three themes:

Advances in Plant Phenotyping Technologies to explore the frontiers of what can be sensed remotely and other technological breakthroughs.

Phenotyping for Crop Improvement to consider the application of phenotyping technologies for crop improvement (breeding, crop husbandry, and estimating the productivity of agro-ecosystems).

Adding Value to Phenotypic Data to review how phenomics and genomics can combine to improve crop simulation models and breeding methodologies (e.g., genomic selection).

For more information, go to www.cimmyt.org/event/4th-international-plant-phenotyping-symposium.

Event organizing committees

MexicoMatthew Reynolds, Chair, CIMMYTBruno Gerard, CIMMYTMartin Kropff, CIMMYTJennifer Nelson, CIMMYTCarolina Saint Pierre, CIMMYTUrs Schulthess, CIMMYTSamuel Trachsel, CIMMYTViridiana Silva, CIMMYTValeria Ordaz, CIMMYTCatherine Casas Saavedra, CIMMYT

International Ulrich Schurr, Chair IPPN, Forschungszentrum Juelich (Germany)April Carroll, Purdue University (United States)Maurice Moloney, Global Institute for Food Security (Canada)Roland Pieruschka, Forschungszentrum Juelich (Germany)Xavier Sirault, High Resolution Plant Phenomics Centre / CSIRO Agriculture and Food (Australia)

CONTENTSTuesdayDecember13:Advancesinplantphenotypingtechnologies

Comprehensivedataharvestingforfieldphenomicsandknowledgediscovery.M.Hirafuji,A.Itoh,S.Ikeda,H.Tsuji,K.Usuki,Y.Usui,K.Nagasawa,M.Okada,K.Taguchi,H.Matsuhira,T.Fukatsu,K.Tanaka,T.Kiura,N.Hoshi,S.Ninomiya,W.Guo,H.Iwata,K.Watanabe,H.Nobughara,I.Kitahara,M.Kato,M.Ono,H.Muhamad,S.Sukisaki,R.Simomura,L.Hongyang,Z.Heming,T.Kamiya,K.Sekiya,H.Koizumi,S.Taniguchi

1

PhénoField®,ahigh-throughputphenotypingplatformtoscreengenotyperesponsetodroughtunderfieldconditions.AntoineFournier,KatiaBeauchêne,BenoitDeSolan,SamuelThomas,BenoitPiquemal,MichelBonnefoy,YanFlodrops,JosianeLorgeou,JeanPiereCohan

1

Exploringtherhizosphere:Imaginingroot-soilinteractionusingx-raycomputedtomography.SaoirseTracy 2

Automatedsegmentationofpotatotubercomputedtomographymeasurementsfornon-destructivebiomassdetermination.StefanGerth,NorbertWörlein,JoelleClaußen,NormanUhlmann

3

3Dimagingapproachesinquantitativeplantphenotyping:Applicationscenariosinthelabandinthefield.MarkMüller-Linow,FranciscoPinto,LukaOlbertz,FabioFiorani,UweRascher

3

High-throughputestimationofincidentlight,lightinterceptionandradiation-useefficiencyofthousandsofplantsinaphenotypingplatform.LlorençCabrera-Bosquet,ChristianFournier,Tsu-WeiChen,NicolasBrichet,BenoîtSuard,ClaudeWelcker,FrançoisTardieu

4

Holisticandcomponent-baseddynamicvegetative-stageplantphenotypinganalysis.SrutiDasChoudhury,AshokSamal,JamesSchnable

4

UsingLIDARtomeasureforageyieldofperennialryegrass(LoliumperenneL.)fieldplots.KioumarsGhamkhar,KenjiIrie,MichaelHagedorn,JeffreyHsiao,JacoFourie,SteveGebbie,JasonTrethewey,CaseyFlay,BrentBarrett,AlanStewart,ArminWerner

5

Plantphenotypingrevealsgeneticandphysiologicalfactorsofplantperformance.ThomasAltmann,MosesMuraya,JiantingChu,YushengZhao,AstridJunker,ChristianKlukas,JochenC.Reif,DavidRiewe,RhondaC.Meyer,Hea-JungJeon,MarcHeuermann,JudithSchmeichel,MoniqueSeyfarth,JanLisec,LotharWillmitzer

6

Trackingshort-termgrowthresponsetoenvironmentalvariablesduringwheatstemelongation.LukasKronenberg,KangYu,AchimWalter,AndreasHund

7

TuesdayDecember13:Addingvaluetophenotypicdata

Willhigh-throughputphenotypingandgenotypingtechniqueshelpustobetterpredictGxEinteractions?Perspectivesfromstatisticsandcropgrowthmodeling.FredA.vanEeuwijk,DanielaV.Bustos-Korts,MarcosMalosetti,MartinP.Boer,WillemKruijer,KarineChenu,ScottC.Chapman

8

Phenotypingforcropimprovementinadiversityofclimaticscenarios.FrançoisTardieu,EmilieJ.Millet,SantiagoAlvarezPrado,AudeCoupel-Ledru,LlorençCabrera-Bosquet,SébastienLacube,BorisParent,ClaudeWelcker

9

Strategiestohandleroothydraulicarchitectureatmultiplescales.XavierDraye,ValentinCouvreur,GuillaumeLobet,FelicienMeunier,MathieuJavaux

10

Usingstructuralmodelstovalidateandimproverootimageanalysispipelines.GuillaumeLobet,IkoKoevoets,ManuNoll,LoïcPagès,PierreTocquinandClairePérilleux

10

Combininghigh-throughputphenotypingandQTLmappingtorevealthedynamicgeneticarchitectureofmaizeplantgrowth.ChenglongHuang,XuehaiZhang,DiWu,FengQiao,KeWang,YingjieXiao,GuoxingChen,LizhongXiong,WannengYang,JianbingYan

11

Useofhigh-throughputphenotypingatCIMMYT:Newmodels,challengesandperspectives.JuanBurgueño,JoséCrossa,SamTrachsel,SuchismitaMondal

11

Usinghigh-throughputphenotypingtoimproveaccuracyingenomicprediction:Examplesforcropsimulatedtraits.DanielaBustos-Korts,MarcosMalosetti,MartinP.Boer,ScottC.Chapman,KarineChenu,FredA.vanEeuwijk

12

PosterSession:Advancesinplantphenotypingtechnologies

AutomatedmeasurementtoolforphenotypingrootandhypocotylgrowthofArabidopsisplants.AdedotunAkintayo,TrevorNolan,YanhaiYin,SoumikSarkar

13

High-throughputscreeningtechniquesforstomatalresponsetoABAbasedonchlorophyllfluorescenceundernon-photorespiratoryconditions.SasanAliniaeifard,UulkevanMeeteren

14

Advancedmathematicalalgorithmstocharacterizeolivevarietiesthroughmorphologicalparameters.KonstantinosN.Blazakisa,LucianaBaldonib,AbdelmajidMoukhlic,MarinaBufacchid,PanagiotisKalaitzisa

15

AIRPHEN:Amultispectralcameradedicatedtofieldphenotypingfromdroneobservations.A.Comar,F.Baret,G.Collombeau,M.Hemmerlé,B.deSolan,D.Dutartre,M.Weiss,S.Madec,F.Toromanoff

15

Derivingcanopyheightfromdroneobservations:Overviewoftheexpectedaccuracyandmaininfluentialfactors.S.Madec,F.Baret,G.Collombeau,S.Thomas,A.Comar,M.Hemmerlé,B.deSolan,D.Dutartre

16

Awirelessenvironmentaldatacollectionsystemforhigh-throughputfieldphenotyping.ThomasTruong,AnhDinh,KhanWahid

17

Decipheringthephenotypiccode:Fromlabtofield-scale.MarcusJansen,StefanPaulus,TinoDornbusch 18

Studiesofrootsystemarchitectureinsoybeanusingcomputervisionandmachinelearning.KevinFalk,TalukdarJubery,SayedVahidMirnezami,KyleParmley,JohnathonShook,ArtiSingh,SoumikSarkar,BaskarGanapathysubramanian,AsheeshK.Singh

18

Recentadvancementindevelopmentofautonomousmobilerobotsforplantphenotyping.RezaFotouhi,AryanSaadatMehr,PierreHucl,MostafaBayati,QianWeiZhang

19

Anautomatedsoybeanmulti-stressdetectionframeworkusingdeepconvolutionalneuralnetworks.SambuddhaGhosal,DavidBlystone,HomagniSaha,DarenMueller,BaskarGanapathysubramanian,AsheeshK.Singh,ArtiSingh,SoumikSarkar

19

High-throughputricephenotypingfacility:Strategiesandchallenges.WannengYang,ChenglongHuang,LingfengDuan,HuiFeng,XiuyingLiang,QianLiu,GuoxingChen,LizhongXiong

20

FieldScanalzyer:Highprecisionphenotypingoffieldcrops.MarcusJansen,StefanPaulus 21

Integratedanalysisofplantgrowthanddevelopmentusinghigh-throughputmulti-sensorplatformsatIPK.AstridJunker,Jean-MichelPape,HenningTschiersch,DanielArend,MatthiasLange,UweScholz,andThomasAltmann

21

DevelopingtheEnviratron:Afacilityforautomatedphenotypingofplantsgrowingundervariedconditions.StephenH.Howell,LieTang,CarolynJ.Lawrence-Dill,ThomasLubberstedt,StevenWhitham

22

Hyperspectralimagingsystem,individualriceplants,andaccuratepredictionofabove-groundbiomass,greenleafareaandchlorophyll.FengHuietal.

23

Comparingrobotsanddronesasphenotypingtoolsinfieldtrials.MortenLillemo,EivindBleken,GunnarLange,LarsGrimstad,PålJohanFrom,IngunnBurud

23

Theneedtoaccountfordirectionaleffectsandthepresenceofreproductiveorgansoncanopyreflectanceandtemperaturerecordedfromdrones.F.Baret,W.Li,S.Madec,A.Comar,M.Hemmerlé,P.Burger

24

Precisionphenotyping:Wheatdiscotofosteryieldpotential.GemmaMolero,MatthewP.Reynolds 25

Fieldphenotyping:Quantifyingdynamicplanttraitsacrossscalesinthefield.OnnoMuller,M.PilarCendreroMateo,HendrikAlbrecht,BeatKeller,FranciscoPinto,AnkeSchickling,MarkMüller-Linow,RolandPieruschka,UlrichSchurr,UweRascher

25

3Dpointcloud-basedmonitoringofsoybeangrowthinearlystages.KojiNoshita,WeiGuo,AkitoKaga,HiroyoshiIwata

26

Greenhouse,fieldandrootphenotypinginfrastructureattheDepartmentofPlantandEnvironmentalSciencesoftheUniversityofCopenhagen.SvendChristensen,JesperSvensgaard,DominikGrosskinsky,JesperCairoWestergaard,RenéHvidbergPetersen,SigneMarieJensen,JesperRasmussen,HanneLipczakJakobsen,ThomasRoitsch

26

Morphometricsforgenomicpredictionofplantmorphologicaltraits:Itsapplicationtogeneticallydissectsorghumgrainshape.LisaSakamoto,HiromiKanegae,KojiNoshita,MotoyukiIshimori,HidekiTakanashi,WaceraFiona,WataruSakamoto,TsuyoshiTokunaga,NobuhiroTsutsumi,HiroyoshiIwata

27

Image-basedphenotypingandmachinelearningtoadvancegenome-wideassociationandpredictionanalysisinsoybean.JiaopingZhang,HsiangSingNaik,TeshaleAssefa,SoumikSarkar,R.V.ChowdaReddy,ArtiSingh,BaskarGanapathysubramanian,AsheeshK.Singh

28

Proposalforevaluatingplantstressviasteadystatefluorescence.S.Summerer,A.Petrozza,V.Nuzzo,F.Cellini 29

Developmentofhigh-throughputphenotypingsoftwareforplantbreeding,geneticsandphysiologystudies.TakanariTanabata,AtsushiHayashi,SachikoIsobe

30

NationalScienceFoundationprogramsthataddressareaswithhighimpactonfoodsecurity.C.EduardoVallejos,JamesW.Jones

30

Planthealthmonitoringusingmultispectralimagingandvolatileanalysisforspaceandterrestrialapplications.AaronI.Velez-Ramirez,JokeBelza,JoeriVercammen,AlexVanDenBossche,DominiqueVanDerStraeten

31

High-throughputfield-basedphenotypinginbreedingwithUAVplatforms.GuijunYang,ChunjiangZhao,JiangangLiu,HaiyangYu,XiaoqingZhao,BoXu

32

CapabilitiesoftheFieldPhenotypingPlatformFIPdemonstratedbycharacterizationofthetimeseriesofcanopycoverinwheatasrelatedtoGxEinteractions.KangYu,NorbertKirchgessner,FrankLiebisch,AchimWalter,AndreasHund

32

Next-generationmaizefieldphenotypingapproachesusinginnovativesensingtechniquesforimprovedbreedingefficiency.MainassaraZaman-Allah,JillCairns,CosmosMagorokosho,AmsalTarekegne,MikeOlsen,BoddupalliM.Prasanna

33

Phenotypingleaftraitsinwheatgenotypesbasedonhyperspectraldata.MarekZivcak,MarianBrestic,KatarinaOlsovska,MarekKovar,ViliamBarek,PavolHauptvogel,XinghongYang

34

PosterSession:Addingvaluetophenotypicdata

Findinganeedleinahaystack–UsingZegamitovisualizephenotypicdatasets.BettinaBerger,GeorgeSainsbury,TrevorGarnett,RogerNoble

35

Takingthenexthurdle:ManagementofphenotypicdatawithPIPPA.StijnDhondt,RaphaelAbbeloos,NathalieWuyts,DirkInzé

35

OPENSIMROOT:Computationalfunctionalplantmodelingandsimulation.ChristianKuppe,JohannesA.Postma 36

Determinationofdifferentialpathogensensitivityofbarleycultivarsbymulti-reflectanceand-fluorescenceimagingincombinationwithdeepphysiologicalphenotyping.DominikGrosskinsky,JesperSvensgaard,SvendChristensen,ThomasRoitsch

37

MaizePhenomap1andPhenomap2datasets:Integrationwithgenomestofields.ZhikaiLiang,SrinidhiBashyam,BhushitAgarwal,GengBai,SrutiDasChaudhury,OscarRodriguez,YumouQiu,YufengGe,AshokSamal,JamesC.Schnable

38

Low-cost3Dimagingsystemsforhigh-throughputfieldphenotyping.ThangCao,KarimPanjvani,AnhDinh,KhanWahid

39

WednesdayDecember14:Phenoptypingforcropimprovement

Phenotypingforroot-basedgainsincropproductivity.MichelleWatt,UlrichSchurr 40

Multi-modalityremotesensinganddataanalysisforhighthroughputphenotyping.M.Crawford 40

PhenotypingatBayerHyperCarefarms.ElisaLiras,GretaDeBoth,StephanieThepot,RandallHess,WalidElfeki,RaphaelDumain

41

Reducinglodginginirrigatedwheat:FocusonstemandrootcharacteristicsandHTPmethods.M.FernandaDreccer,TonyCondon,M.GabrielaBorgognone,GregRebetzke,LynneMcIntyre

41

LeasyScanre-loaded:3Dscanningplusseamlessmonitoringofcropcanopyandwateruse.VincentVadez,JanaKholova

42

Extendingthephenotype:Integrationoffieldandglasshousephenotypingwithcropmodeling.ScottC.Chapman,GraemeL.Hammer,AndriesPotgieter,DavidJordan,BangyouZheng,TaoDuan,RobertFurbank,XavierSirault,JoseJimenez-Berni

43

PhenotypingandGWASforriceimprovement:Astrategyandpartialresultstowardsmulti-traitideotypeconstructionbygenomeediting.MichaelDingkuhn,ArvindKumar,TobiasKretzschmar,ChristophePerin,UttamKumar,MariaCamilaRebolledo,HeiLeung,JulieMaeCristePasuquin,PaulQuick,AnindyaBandyopadhyay,DelphineLuquet

43

Scalablein-fieldphenotypingplatformsfordynamicmeasurementofperformance-relatedtraitsinbreadwheat.JiZhou,DanielReynolds,ThomasLeCornu,ClareLister,SimonOrford,StephenLaycock,MattClark,MikeBevan,SimonGriffiths

44

Effectivedeliveryofphenomicsincommercialbreedingismoreaquestionofwhatandwhen,nothow.G.J.Rebetzke,J.Jimenez-Berni,W.D.Bovill,R.A.James,D.M.Deery,A.Rattey,D.Mullans,M.Quinn

45

Plantdiseasephenomics:Identificationofquantitativeresistanceincropplantsusingphenomicapproaches.StephenRolfe,SarahSommer,FokionChatziavgerinos,EstrellaLuna,PierrePétriacq,BruceGrieve,DiegoCoronaLopez,CharlesVeys,JohnDoonan,KevinWilliams

46

Strategiesforcropfield-basedhigh-throughputphenotyping(FB-HTP)inbreedingusingUAVplatforms.JiangangLiu,GuijunYang,HaiyangYu,XiaoqingZhao,BoXu

46

Remotesensingforcropimprovement:Fromresearchtoindustry.SebastienPraud,FredericBaret,AlexisComarand,DavidGouache

47

Testingtheefficacyoflarge-scalefieldphenotypingingenomicselectiontoacceleratewheatbreeding.EricOber,RobertJackson,R.ChrisGaynor,AlisonBentley,PhilHowell,JohnHickey,IanMackay

48

Phenotypingforabioticstresstoleranceincrops:Indianinitiatives.JagadishRane,PrashantKumar,MamruthaMadhusudhan,MaheshKumar,SaiPrasad

49

ImprovingtheprecisionofphenotypicdatausingUAV-basedimagery.FranciscoPinto,MatthewReynolds 50

Phenotypingforbreedingandphysiologicalpre-breeding.MatthewReynolds,GemmaMolero,FranciscoPinto,CarolinaRivera,FranciscoPinera,SivakumarSukumaran,MartaLopes,andCarolinaSaintPierre

50

Documentingadvancesandideasinplantphenotyping

Harmonizingeffortsamongphenotypinginitiatives:Bottlenecksandopportunities.JoséL.Araus 51

PosterSession:Phenotypingforcropimprovement

Optimizingwheatrootarchitecturebyexploitingdiversegermplasm.JonathanA.Atkinson,Cai-yunYang,StellaEdwards,SurbhiMehra,JulieKing,IanKing,MalcolmBennett,DarrenM.Wells

52

WaterstressfieldphenotypingandPHENOMOBILE-LVmonitoringofwheat.B.deSolan,F.Baret,S.Thomas,O.Moulin,G.Meloux,S.Liu,S.Madec,M.Weiss,K.Beauchene,A.Comar,A.Fournier,D.Gouache

52

High-throughputphenotypingofearlyvigorinwheat:Rapidmeasuresofleafareaasproxyofearlyplantgrowthparametersandyield.AviyaFadida-Myers,AvivTsubary,AiaSulkoviak,AharonBellalou,KamalNashef,YehoshuaSaranga,DavidJ.Bonfil,ZviPeleg,RoiBen-David

53

Anefficienttooltodescribeolivevarietiesthroughstrictlymathematicallydefinedmorphologicalparameters.KonstantinosN.Blazakis,LucianaBaldoni,AbdelmajidMoukhli,MarinaBufacchi,PanagiotisKalaitzis

54

High-throughputlaboratoryphenotypingoflettucetoassessdroughttolerance.MarianBrestic,MarekKovar,KlaudiaBruckova,MarekZivcak,KatarinaOlsovska,XinghongYang

55

Animage-basedautomatedpipelineformaizeearandsilkdetectioninahigh-throughputphenotypingplatform.NicolasBrichet,LlorençCabrera-Bosquet,OlivierTurc,ClaudeWelcker,FrançoisTardieu

55

IdentificationofresistancetoramulosiscausedbyColletotrichumgossypiivar.cephalosporioidesinadvancedcottonbreedinglinesandmonitoringoframulosisdiseasebyRGB-imageanalysis.OscarBurbano-Figueroa,MilenaMoreno-Moran,KeyraSalazarPertuz,LorenaOsorioAlmanza,KarenMontesMercado,ElianaReveloGomez,MariadelValleRodriguez

56

PredictingyellowrustandSeptoriatriticiblotchinwheatbyhyperspectralphenotypingandmachinelearning.RitaArmoniené,AlexanderKoc,SalvatoreCaruso,TinaHenriksson,AakashChawade

57

Exploringthepotentialofspectralreflectancefordetectionoforganandcanopypropertiesinwheat.M.FernandaDreccer,JoseJimenez-Berni,StephenGensemer,EthanGoan

58

Improvingnitrogenuseefficiency:Canbetterphenomicsmakeadifference?TrevorGarnett,NicholasSitlington-Hansen,DarrenPlett,MalcolmHawkesford,BettinaBerger

58

Identifyingwheatroottraitsandregulatorygenesthatcontrolnitrogenuptakeefficiency.MarcusGriffiths,JonathanAtkinson,MalcolmJ.Bennett,SachaMooney,DarrenM.Wells

59

Canhigh-throughputphenotypinghelppredictsoybeanyieldincontrastingenvironments?RaceHiggins,KyleParmley,AsheeshK.Singh

59

High-throughputfieldphenotypingofwheatplantheightandgrowthrateinfieldplottrialsusingUAVremotesensing.FennerH.Holman,AndrewB.Riche,AdamMichalski,MarchCastle,MartinJ.Wooster,MalcolmJ.Hawkesford

60

Phenotypingtraitstoimprovedroughttoleranceinwheat.Yin-GangHu,LiangChen 61

Hyperspectraldiseasesignaturesfordetectingcharcoalrotinsoybean.SarahJones,AsheeshK.Singh,SoumikSarkar,BaskarGanapathysubramanian,DarenMueller,ArtiSingh

61

Identificationofwaterusestrategiesatearlygrowthstagesindurumwheatusingmodernshootimagephenotyping.NiklasKörber,AlirezaNakhforoosh,ThomasBodewein,KerstinA.Nagel,FabioFiorani,GernotBodner

62

TacklingamajorepidemicofmaizelethalnecrosisineasternAfrica.L.M.Suresh,YosephBeyene,ManjeGowda,JumboMacDonaldBright,MichaelOlsen,BiswanathDas,DanMakumbi,B.M.Prasanna

62

TheMaizeGenomestoFields(G2F)initiative:Datamanagementandavailability.NaserAlKhalifah,DarwinA.Campbell,ReneeWalton,CintaRomay,Cheng-TingYeh,RamonaWalls,GenomestoFieldsCollaborators,PatrickS.Schnable,DavidErtl,NataliadeLeon,JodeEdwards,CarolynJ.Lawrence-Dill

63

NSFresearchtraineeship–P3,predictiveplantphenomics.JulieDickerson,ThedoreHeindel,CarolynJ.Lawrence-Dill,PatrickSchnable

64

AnalysisofwaterlimitationeffectsonthephenomeandionomeofArabidopsisatthePlantImagingConsortium.LuciaMAcosta-Gamboa,SuxingLiu,ErinLangley,ZacharyCampbell,NormaCastro-Guerrero,DavidMendoza-Cozatl,ArgeliaLorence

64

High-throughputphenotypicexplorationofgeneticvariationfromwildrelativesforbreedinghighbiomassandyieldinwheat.LornaMcAusland,LauraBriers,StellaEdwards,Cai-yungYang,JulieKing,IanKing,ErikMurchie

65

High-throughputphenotypingofcanopydevelopmentinsoybean.FabianaFreitasMoreira,AnthonyA.Hearst,KeithCherkauer,KatyMRainey

65

Mechanismofheattoleranceevaluationinwheatthroughphenotypicparameters.RenuMunjalandS.S.Dhanda 66

IdentificationofroottraitsthatcanserveassuitableselectiontargetstoenhancewinterwheatproductionintheSouthernGreatPlainsoftheUSA.AnaPaez-Garcia,JoshAnderson,FrankMaulana,XuefengMa,andElisonB.Blancaflor

66

High-throughputscreeningtoolsforidentificationoftraitscontributingtosalinitytolerance.KláraPanzarová,MariamAwlia,ArianaNigro,JiříFajkus,ZuzanaBenedikty,MarkTester,MartinTrtílek

67

Customizingsoybeancultivardevelopmentthroughaerialandgroundphenotyping.KyleParmley,VikasChawla,AkintayoAdedotun,RaceHiggins,TalukdarJubery,SayedVahidMirnezami,NigelLee,BaskarGanapathysubramanian,SoumikSarkar,AsheeshK.Singh

68

Phenotypingandgeneticanalysisoflodging-relatedtraits.FranciscoJ.Piñera-Chavez,PeterM.Berry,MichaelJ.Foulkes,GemmaMolero,SivakumarSukumaran,MatthewP.Reynolds

68

Phenotypingshowsassociationofhighseedyieldwithstemwatersolublecarbohydratesandchlorophyllcontentduringanthesisorgrain-fillinginwheatcultivarsunderheatstress.HafeezurRehman,AbsaarTARIQ,MakhdoomHussain,MatthewP.Reynolds

69

Phenotypingtoolsandphysiologicalbreeding:Optimizingbiomassdistributionwithintheplanttoincreaseharvestindexinwheatcultivars.CarolinaRivera-Amado,GemmaMolero,EliseoTrujilloNegrellos,JohnFoulkes,MatthewReynolds

70

Phenotypingtoolstodissectmorphologicaldiversityinsweetpotato.AmparoRosero,LeiterGranda,José-LuisPérez,DeisyRosero,WilliamBurgos-Paz,RembertoMartínez,IvánPastrana

71

NDVIvaluesandgrainmineralcontentpredictthepotentialofsyntheticspringwheatandwildrelatives.A.I.Abugaliyeva,A.I.Morgounov,K.Kozhakhmetov,T.V.Savin,A.S.Massimgaziyeva,A.Rsymbetov

72

Globalnetworkforprecisionfield-basedwheatphenotyping.CarolinaSaintPierre,AmorYahyaoui,PawanSingh,MatthewReynolds,MichaelBaum,HansBraun

72

Rootcorticalsenescenceinfluencesmetaboliccostsandradialwaterandnutrienttransportinbarley.HannahM.Schneider,TobiasWojciechowski,JohannesA.Postma,DagmarvanDusschoten,JonathanP.Lynch

73

Nutrient-relatedtraitsforimprovedgrowthandgrainqualityinIndianwheat.JaswantSingh,LolitaWilson,ScottD.Young,SindhuSareen,BhudevaS.Tyagi,IanP.King,MartinR.Broadley

74

Large-scalephenotypingfornext-generationwheatvarietalimprovement.SukhwinderSingh,PrashantVikram,CarolinaSaintPierre,JuanAndersBurgueño,HuihuiLi,CarolinaSansaloni,DeepmalaSehgal,SergioCortez,G.EstradaCampuzano,N.Espinosa,PedroFigueroa,GuillermoFuentes,C.G.Martínez,ErnestoSolísMoya,H.E.Villaseñor,VictorZamora,IvanOrtiz-Monasterio,CarlosGuzmán,CesarPetroli,GilbertoSalinas,ThomasPayne,KateDreher,RaviPrakashSingh,VelluGovindan,MathewReynolds,PawanSingh,JoseCrossaandKevinPixley

74

Usingshovelomicstoidentifyrootingtraitsforimprovedwateruptakeunderdroughtinawinterwheatdoubledhaploidpopulation.ShaunaghSlack,LarryM.York,MalcolmBennett,JohnFoulkes

75

Shootandrootphenotypingofspringwheatunderwaterloggedconditionsinfieldandrhizotronexperiments.T.Sundgren,A.K.Uhlen,T.Wojciechowski,W.Waalen,M.Lillemo

75

Predictingsorghumbiomassusingaerialandground-basedphenotypes.AddieThompson,KarthikeyanRamamurthy,ZhouZhang,FangningHe,MelbaMCrawford,AymanHabib,CliffordWeil,MitchellR.Tuinstra

76

Alterationsinrootproteomeofsalt-sensitiveandsalt-tolerantbarleylinesundersaltstressconditions.StanisławWeidner,AgnieszkaMostekA.,AndreasBörner,AnnaBadowiec

77

1

ADVANCESINPLANTPHENOTYPINGTECHNOLOGIES

Tuesday13thDecember(Morning)ADVANCESINPLANTPHENOTYPINGTECHNOLOGIES

ComprehensivedataharvestingforfieldphenomicsandknowledgediscoveryM.Hirafuji1,2,A.Itoh1,S.Ikeda1,H.Tsuji1,K.Usuki1,Y.Usui1,K.Nagasawa1,M.Okada1,K.Taguchi1,H.Matsuhira1,T.Fukatsu1,K.Tanaka1,T.Kiura1,N.Hoshi1,S.Ninomiya3,W.Guo3,H.Iwata3,K.Watanabe3,H.Nobughara2,I.Kitahara2,M.Kato2,M.Ono2,H.Muhamad2,S.Sukisaki2,R.Simomura2,L.Hongyang2,Z.Heming2,T.Kamiya4,K.Sekiya4,H.Koizumi4,S.Taniguchi4

1NARO(NationalAgricultureandFoodResearchOrganization),Japan.E-mail:[email protected],Japan3TheUniversityofTokyo,Japan4NECSolutionInnovators,Ltd.,JapanRecentlydataonyieldandrateoffertilizerapplicationinthefieldcanbecollectedautomaticallyasgeolocationdatameasuredbyRTK-GPS.However,suchsnapshotdataarenotsufficient,sinceplantsgrowdynamicallyunderadiversityofenvironmentalconditionsandsymbioticmicroorganisms.WearedevelopingData-Harvestertocollecttime-seriesdatainthefieldaswellasaKnowledgeDiscoverySupportSystem(KDSS).Data-Harvesterconsistsofsensornetworks,dronesandwalkingrobotsandcollectstime-seriesdatasuchasairtemperature,soilmoistureandhourly/dailyimagesofplants.KDSSprovidesservicessuchasdatamanagement,3D-reconstructionfrom2D-images,machinelearning,patternrecognition,dataassimilationandmeta-computing.IdeallyData-Harvestershouldalsoautomaticallycollecttime-seriesdataonmicroscopicphenotypes,microorganismsandsymptomexpression,sothewalkingrobotsareequippedwithmanipulators.Asforpracticalapplications,collectedbigdatacanbeminedforsimpleknowledge.However,forthefirsttimewearetacklingdifficultproblemssuchastheelucidationofhybridvigortoevaluatethecapabilitiesofData-HarvesterandKDSS.Wewillimprovethembyappendingrequiredfunctionstosolveproblemsquicklywithartificialintelligence,crowdintelligence,etc.ThisworkissupportedbyCREST,JST.PhénoField®,ahigh-throughputphenotypingplatformtoscreengenotyperesponsetodroughtunderfieldconditionsAntoineFournier1,KatiaBeauchêne1,BenoitDeSolan1,2,SamuelThomas1,2,BenoitPiquemal1,MichelBonnefoy1,YanFlodrops1,JosianeLorgeou1,JeanPiereCohan2

1ARVALIS–Institutduvégétal,France.Email:[email protected],UMTCAPTE,FranceTheFrenchPlantPhenotypingNetwork(FPPN-PHENOME)providesacademicandprivatecommunitieswithanetworkofnineinstrumentedplatformsabletodealwithmostcropspecies,biologicalquestionsandenvironmentalstresses.PhénoField®,theplatformmanagedbyARVALIS,isahighlyinstrumentedfieldresearchfacilityallowingdetailanalysisofyieldbuild-upofmaizeandothercropgenotypesundermanagedwaterdeficit.LocatedintheBeauceRegioninFrance,itconsistsofeightmobilerainsheltersequippedwithcontrolledirrigation.Theavailablewatercapacityismonitoredbyanensembleof

2


sensorsdistributedinmanyplacesselectedtorepresentthevariabilityofsoilpropertiesthatarescaledupusingasub-metricspatialresolutionmap.PhenotypictraitsaremeasuredbyasuiteofnovelsensorsincludingRGBcameras,LIDARsandspectrometerswhichperiodicallyacquiredatafromafullyautomatedgantry,alongwithcontinuousmonitoringofplotmicro-meteorology.RGBandmultispectralacquisitionofthefullplatformisregularlyperformedfromunmannedaerialvehiclestocomplementspatialandtemporalsampling.Ateamofspecialistsininstrumentation,signalprocessing,plantmodeling,plantphysiologyandappliedagronomyisrunningtheexperimentandtransformingrawmeasurementsintopertinenttraitstobedirectlyusedbybreeders.In2015,PhénoFieldscreenedahistoricpanelofmaizehybridsintheframeworkoftheAMAIZINGprojectsoastoevaluatethegeneticprogressindroughtresponseandidentifythephysiologicaltraitsunderpinningit.In2016,PhénoFieldwelcomedacomprehensivepanelrepresentingthelast25yearsofwheatbreedingintheframeworkoftheBREEDWHEATproject.Thistrialshouldprovideinsightintothegeneticandphysiologicalarchitectureofthedroughtresponseofwheat.Thefirstinstrumentalresultsandmethodologicalstudieswillbepresentedalongwithatestimonyofourexperienceduringthecommissioningofsuchsemi-controlledfieldplatform,includingcalibrationconstraints,real-timecampaignmonitoring,datamanagementandsignalvalidationforagronomicuse.Exploringtherhizosphere:Imagingroot-soilinteractionsusingX-raycomputedtomographySaoirseTracySchoolofAgriculture,UniversityCollege,Dublin,Ireland.Email:saoirse.tracy@ucd.ieAlthoughrootsplayacrucialroleinplantgrowthanddevelopmentthroughtheiracquisitionanddeliveryofwaterandnutrientstoabove-groundorgans,ourunderstandingofhowtheyinteractwiththeirimmediatesoilenvironmentlargelyremainsamystery,astheopaquenatureofsoilhaspreventedundisturbedinsiturootvisualization.Thespatialarrangementofrootsandthesoilstructureareextrinsicallylinkedtotheoverallproductivityofaplant,astheycontroltheabilityofaplantroottoextractessentialresourcesforgrowth.Inaworldwitharapidlyincreasingpopulationandthethreatofclimatechange,maximizingplantproductivityisvital.Therefore,visualizationandquantificationofrootgrowthinsoilisneededtounderstandplantrootgrowthdynamics.Theuseofnon-invasivetechniquessuchasX-raycomputedtomography(CT)meansthatitisnowpossibletovisualizeagrowingrootwithinanundisturbedsoilcore.TheX-rayCTtechniqueenablesnon-destructive3Dinvestigationsintoroot:soilinteractionsatthemicro-scale.Byimagingtheactual3Dgeometriesofsoilstructureandvisualizingtheinterfacesbetweenroots,soil,waterandair-filledpores,anaccuraterepresentationofwatermovementandrootgrowthinsoilisachieved.Destructivemethodssuchasrootwashingthatwerecommonlyemployedforrootstudiescouldnotprovidedetailedinformationonrootarchitecture,includingbranchingcharacteristicsandextensionrate,whichareinherentlylinkedtoconditionswithinthesoilmatrix.Incontrast,thistechniqueenablesrootphenotypingofdifferentcropspeciesandvarietiesinsoil.Thisinformationiscrucialiflaboratoryresearchistobetranslatedintoanunderstandingofresponsesunderfieldconditions.

3


Automatedsegmentationofpotatotubercomputedtomographymeasurementsfornon-destructivebiomassdeterminationStefanGerth,NorbertWörlein,JoelleClaußen,NormanUhlmann

DevelopmentCenterX-RayTechnologyEZRT,Germany.Email:stefan.gerth@iis.fraunhofer.deThereconstructedvolumedatasetofX-raycomputedtomography(CT)consistsofgraylevelswhichrepresentinformationaboutX-rayattenuation.Thisattenuationcoefficientdependsonthedensitydistributionofthematerial,theelementalcompositionoftheX-raysourceanditsspectraldistribution.A3Dimagesegmentationisneededtostudythebelow-groundgrowthoftubers.Increasingthedimensionalityfrom2Dtoreal3Denhancesthecomplexityofimagesegmentationalgorithms.Atthesametime,volumeinformationisaccessiblefordirectcorrelationwithvolumetrictraits,forexample,tubervolumesorformfactors.Additionally,thecalculatedX-rayattenuationcoefficientofthesegmentedtuberscanbeusedtotrackdensitychangeswithinthetuber.Automaticsegmentationandanalyticalroutinesareneededtocreatecomparableresultsbetweenthedifferentpointsandbetweendifferentsamplingtimes.Wewillpresenttheimagesegmentationprinciplesandthegathereddataonvolumetrictubergrowthofdifferentvarieties.Usingthismethod,itispossibletomonitorthegrowthofeachindividualtuber.Additionally,thedestructivesamplingofasubsetofallmeasuredpotsallowedustocorrelatefreshanddryweightwithabsorptionandvolumetricparametersfromtheX-raymeasurement.UsingthepresentedprincipleofbiomassdeterminationfromX-rayCTdata,thesamecorrelationcanbeobtainedforallkindsofplantmaterialsusingdifferentsegmentationalgorithms.3Dimagingapproachesinquantitativeplantphenotyping:ApplicationscenariosinthelabandinthefieldMarkMüller-Linow1,FranciscoPinto2,LukaOlbertz1,FabioFiorani1,UweRascher1

1ForschungszentrumJuelichGmbH,Germany.Email:[email protected],MexicoInplantphenotyping,3Dimagingapproachesareincreasinglyusedtostudytheimpactofgeneticvariabilityandenvironmentalfactorsthatinfluenceleafanglesandlightinterceptionresultinginvaryingcanopyarchitecture.Thenon-invasiveacquisitionof3Dstructureofdifferentplantorganssuchasleaves,roots,fruitsorseedsrequiresusingdifferentmethodologicalapproaches.Apartfromactivemethods,whichmakeuseofanartificiallightsource(e.g.,LightDetectionandRanging[LIDAR],LightSectioningorStructuredLight),camera-basedmethodsarewidelyusedandsubstantiallydifferintermsofimagedataprocessing.Herewegiveanoverviewof3Dimagingapproaches,whichweredevelopedattheInstituteofBiosciences(IBG-2,ForschungszentrumJuelich,Germany)withafocusondevelopingandbenchmarkingmeasurementsaspartofthetwoGermanprojectclustersCrop.Sense.net(www.cropsense.uni-bonn.de)andtheGermanPlantPhenotypingNetwork(DPPN;www.dppn.de).Wewilldemonstratetheuseofdifferentsensortechniques,rangingfromstructuredlightmethodsinthelabuptolight-sectioningapproachesandstereo-imagingindifferentapplicationscenariosinthelaband

4


inthefield,therebycoveringthescaleofsmallplantpopulationstosmallerscalesofsingleplants.Usingstructuredlight,wewereabletoresolveandquantitativelycharacterizesingleleavesuptoasizeof2mm.Wewillhighlighttheapplicationofmulti-camerasetupsundernaturalenvironmentalconditionsatthescaleofexperimentalplots(upto2m2)alongwithnewimageprocessingpipelinestoestimateleafareaandleafangledistributioninsugarbeetexperiments.Hereplantswereanalyzedonthebasisofindividualleaf3Dmodelsfromsegmentedstereoimages.High-throughputestimationofincidentlight,lightinterceptionandradiation-useefficiencyofthousandsofplantsinaphenotypingplatformLlorençCabrera-Bosquet,ChristianFournier,Tsu-WeiChen,NicolasBrichet,BenoîtSuard,ClaudeWelcker,FrançoisTardieuUMRLEPSE,INRA,MontpellierSupAgro,34060,Montpellier,France.Email:[email protected]

Understandingthegeneticcontrolofbiomassproductionandyieldisamajorchallengeinthecontextofclimatechange.Wedevelopedasuiteofmethodstomeasurelightinterception,interceptionefficiencyandradiation-useefficiency(RUE)inthousandsofplantsusingahigh-throughputphenotypingplatform.Differentmodelswereinterfacedtocalculate:(i)thespatialdistributionofincidentlight,asexperiencedbyhundredsofplantsinagreenhouse,bysimulatingsunbeamtrajectoriesthroughglasshousestructureseverydayoftheyear;(ii)theamountoflightinterceptedbyplants(IPPFD)viaafunctional-structuralmodelusing3Dreconstructionsofeachplantplacedinavirtualscenereproducingthecanopyinthegreenhouse;(iii)RUEcalculatedastheratioofplantbiomasstoIPPFD.Inputsforthesemodelswere(i)imagesofeachplanttakenevery30°everydayforestimatingleafarea,biomassandarchitecture;and(ii)environmentaldatacollectedevery15minat8siteswithinthegreenhouse.ThesemethodsweretestedusingthePhenoArchplantphenotypingplatform(www6.montpellier.inra.fr/lepse/M3P)duringsixexperimentswithpanelsof1,680maize(ZeamaysL.)hybridsorlinesgrownindifferentseasons(contrastinglightandevaporativedemand)anddifferentsoilwaterpotentials.Eachofthestudiedtraitsdisplayedlargegenotypicvariability.Bycalculating"hiddenvariables"encapsulatingspatialandtemporalenvironmentalvariationssuchasRUE,weidentifiedheritablephysiologicaltraitsusableingeneticanalysesandcropmodelingthatarecurrentlyappliedinfieldstudies,therebyopeningthewayforlarge-scalegeneticanalysesofplantperformancecomponents.Holisticandcomponent-baseddynamicvegetative-stageplantphenotypinganalysisSrutiDasChoudhury1,AshokSamal1,JamesSchnable2

1DepartmentofComputerScienceandEngineering,UniversityofNebraska-Lincoln,USA2DepartmentofAgronomyandHorticulture,UniversityofNebraska-Lincoln,USAExtractingmeaningfulnumericalphenotypesfromplantimagesremainsacriticalbottleneckinautomatedplantphenotyping.Weclassifyimage-basedphenotypingapproachesintoholisticandcomponent-based.Holisticanalysesconsiderthewholeplantasasingleobjectandmeasureits

5


attributes,whereascomponent-basedphenotypinganalyzesindividualplantparts,e.g.,leavesandstems.Twonovelholisticphenotypiccomponentsareintroduced:bi-angularconvex-hullarearatioandplantaspectratio.Bi-angularconvex-hullarearatioisdefinedastheratiooftheareaoftheconvex-hulloftheplantwhenviewedfromthesideataparticularangleandtheareaoftheconvex-hullofthesameplantwhenviewedatarotationof90°.Itprovidesinformationabouttemporalchangesinphyllotaxy,i.e.,thearrangementofleavesaroundastem.Plantaspectratioisdefinedastheratiooftheheightoftheboundingrectangleoftheplantfromthesideviewandthediameteroftheminimumenclosingcirclefromthetopview.Itcharacterizesthecanopyarchitecturethatisgeneratedbythecropaccessionsinthefield.Thefollowingcomponent-basedphenotypesarecomputed:leafcount,leafsize,stemangleandleafcurvature.Thegrowthofplantisbestinterpretedbythenumberofleavesandthesizeofeachleafduringtheplant’slifecycle.Thus,weintroduceanalgorithmforleaf-countandleaf-sizemeasurement.Thestemangle,i.e.,theanglebetweenthestemandthehorizontalaxis,awayfromvertical,canbeanearlysignalthatagivenplantisgoingtobesusceptibletolodging.YieldlossesduetolodgingreducetheUSmaizeharvestbetween5-25%ayear($2.4-12billionat2015maizeprices).Leafcurvatureismeasuredtoprovideinformationonleafdrooping.Aplant-componenttrackingalgorithmisintroducedtostudythetemporalvariationofthesecomponent-basedphenotypesinmaizeusingournewlyintroducedbenchmarkdatasetcalledComponentPhenocornDataset.UsingLIDARtomeasureforageyieldofperennialryegrass(LoliumperenneL.)fieldplotsKioumarsGhamkhar1,KenjiIrie2,MichaelHagedorn2,JeffreyHsiao2,JacoFourie2,SteveGebbie3,JasonTrethewey2,CaseyFlay1,BrentBarrett1,AlanStewart4,ArminWerner2

1ForageScience,GrasslandsResearchCentre,AgResearch,PalmerstonNorth,NewZealand.Email:[email protected]

2LincolnAgritechLtd,Lincoln,NewZealand3DevelopmentEngineering,LincolnResearchCentre,AgResearch,Lincoln,NewZealand4PGGWrightsonSeeds,Christchurch,NewZealandALIDAR-basedtoolfornon-invasiveestimationofplantbiomassinperennialryegrassfieldplotswasdeveloped.ThisincludeddesigningandmakingaprototypeofamachineforLIDARdatacollection,anddevelopingalgorithmsfordataprocessing.Thebiomassestimateswerevalidatedwithregressionanalysisagainstharvestdata.Theprojectwasimplementedinthreephases.Inphase1,aprototype-carryingframeandalight-excludingcoverwasconstructedfortheLIDARscanner.Analgorithmwasdevelopedforgrassplotsegmentation,groundsurfacedetectionandestimationofplantbiomass.Phase2focusedondevelopingtheprototypetoolfurther,includingapplication-specificreal-timecaptureend-usersoftwarefordatacaptureandanalysis.Thisincludedtestingthealgorithmandin-fieldtestingofthesoftware.Anexperimentwasalsoconductedtostudyhowthevariationingroundlevelbetweendifferentscansaffectedthemeasurement.Itwasfoundthatthevariationofgroundlevelwassignificant(morethan20mm)betweenadjacentscansandwithineachsegment.Animprovedmethodwithcorrectionforsoilsurfacevariationwasdevelopedtoestimatethegroundlevelofeachscanandincreasetheaccuracyofbiomassestimation.Inphase3,86segmentsinreplicatedfieldplotsofaperennialryegrasscultivartrialinCanterbury,NewZealand,werescannedwithLIDARatearly,midandlatetimepoints,withmechanicalharvestandyielddatacollectionatthelategrowthstage.Significant(P<0.0005)correlationswereobservedbetweenprocessedLIDARdataandfreshanddryweightsof

6


plantfoliagebiomasswithR2valuesof0.78and0.76,respectively.Thelate-growthcalibrateddatawereusedtoexploreryegrassgrowthdynamicsusingLIDARscansatearlygrowthandmid-growthstages.PlantphenotypingrevealsgeneticandphysiologicalfactorsofplantperformanceThomasAltmann1,MosesMuraya1,2,JiantingChu1,YushengZhao1,AstridJunker1,ChristianKlukas1,3,JochenC.Reif1,DavidRiewe1,RhondaC.Meyer1,Hea-JungJeon1,MarcHeuermann1,JudithSchmeichel1,MoniqueSeyfarth1,JanLisec4,LotharWillmitzer41LeibnizInstituteofPlantGeneticsandCropPlantResearch(IPK)Gatersleben,Germany.E-mail:[email protected]

2ChukaUniversity,Kenya3LemnaTecGmbH,Germany4Max-Planck-InstituteofMolecularPlantPhysiology,GermanyKnowledgeofthestructuralandfunctionalgeneticarchitectureofagriculturaltraitsisaprerequisiteforthesystematicexplorationandutilizationofplantgeneticresourcesinplantbreeding.Touncovermechanisticlinksbetweengeneticvariation,physiologicalfactorsandwholeplantperformancefordevelopingnovelcropimprovementstrategies,Arabidopsis,maize,andrapeseedwerestudiedusingintegratinggenotyping,transcriptandmetaboliteprofiling,andautomatedplantphenotypingwithdedicatedplatforms.Thethreehigh-throughputplantphenotypingfacilitiesatIPKsupportautomatedwhole-plantanalysesforsmall,mediumandlargeplantsincludingcultivation,transport(plant-to-sensor)andimagingofplantsinclimatecontrolledphytotron/glasshousecabins.Theywereequippedwithcamerasandilluminationsystemsforvisible,fluorescenceandnear-infraredimagingusingtopviewandsideviews.Furthermore,3DlaserscannersandLEDpanelsandCCDcamerasforfunctionalchlorophyllfluorescencedetectionandabroadrangeofenvironmentalsensorswereinstalled.Thevalueofrepeatednon-invasive/non-destructivemonitoringoflargeplantpopulationsishighlightedbyresultsoftheanalysisofacollectionof261maizedentlinesusingaspecificallyoptimizedcultivationandphenotypingsetupcharacterizedforbiomassaccumulationandwaterconsumption,andthus,forwateruseefficiency.Combinedwith50kSNPinformation,thesedatahavebeenusedtoidentifyQTLofthesetraitsandofthegrowthdynamicsbygenome-wideassociationtesting.Twelvemain-effectQTLand6pairsofepistaticinteractionsweredetectedthatdisplayeddifferentexpressionpatternsatseveralindividualdevelopmentaltimepoints.Asubsetalsoshowedsignificanteffectsonrelativegrowthratesatdifferentintervals.Usingnonparametricfunctionalmappingandmultivariatemappingapproaches,fouradditionalQTLaffectinggrowthdynamicsweredetected.Ourresultsdemonstratethatplantbiomassaccumulationisacomplextraitgovernedbymanysmall-effectloci,mostofwhichactatcertainrestricteddevelopmentalstages.Thishighlightstheneedtodetectandinvestigatestage-specificgrowthcontrolgenesoperatingatdifferentdevelopmentalphases.IntegratedmetabolomeanalysisandwholeplantphenotypingperformedinArabidopsisrevealeddirectlinksbetweenapromoterInDelpolymorphismoftheFUM2gene,itsmRNAexpression,fumaraseenzymeactivity,andfumarate:malateratioinleaves.ThepromoterInDelthatcoincidedwithafumarateQTLfoundinArabidopsisCol-0/C24

7


RILsandILswasalsosignificantlyassociatedwiththefumarate:malateratio,withmalateandfumaratelevels,andwithdryweightin174naturalaccessionsat15daysaftersowing(DAS)andwasassociatedwithbiomassproductioninanother251accessions(at22DAS).ThissupportsaroleofthecytosolicFUM2,whichspecificallyoccursinBrassicaceae,indiurnalcarbonstorageandindicatesagrowthadvantageofaccessionscarryingtheFUM2Col-0alleleoccurringmostlyincolderclimates.Trackingshort-termgrowthresponsetoenvironmentalvariablesduringwheatstemelongationLukasKronenberg1,KangYu1,AchimWalter1,AndreasHund11SwissFederalInstituteofTechnologyETH,Switzerland.Email:[email protected](SE)inwheatiscriticalforyieldandisdrivenbytemperature.Wheatgrowthresponsetotemperatureduringthisphaseisthereforeanimportanttraitintermsofcropadaptationtodifferentenvironmentsandyield.AccurateplantheightmeasurementsinhightemporalresolutionarerequiredtoquantifySEgrowthdynamicsandtheirresponsetotemperature.Usingtraditionalmeasuringtoolsistediousandreliesonasubjectivechoiceofafewindividualplantstobemeasured.Terrestrial3Dlaserscanning(TLS)hasrecentlybeenpresentedasanoveltoolformeasuringplantheightinthefield,basedonassessingtheheightofthewholeuppercanopy.Inthisstudy,weusedTLSwithascannermountedontheETHfieldphenotypingplatform(FIP)tomeasurecanopyheightat3Dayintervalsduringthewholestemelongationperiodinafieldtrialcomprising340winterwheatvarieties.TheFIPisanovelmultisensoryphenotypingplatformthatcoversa1-hafieldbymeansofasensorheadhangingfromarope.Theaimsweretotestthemeasurementsystem’scapabilitytomeasurecanopyheightintermsofaccuracyandthroughputandinvestigategenotypicdifferencesingrowthdynamicsandingrowthresponsetotemperatureduringSE.TheTLSapproachprovedpromisingforcanopyheightmeasurements(R2=0.99comparedtomanualmeasurements)athighprecisionandthroughput.Atotalof714plotscouldbemeasuredwithin3.5hours,whichallowsforveryhighmeasurementfrequency(i.e.,twiceaday).Inthisway,growthcanbecapturedinhightemporalresolution.Genotypesdifferedsignificantlyindaystofinalheightaswellasindaysto50%finalheight.Dailygrowthratesdifferedmarkedlybetweenmeasurementintervalsandamonggenotypes.Theanalysisofvariancesuggestedsignificantgenotypiceffectsinshort-termgrowthresponsetotemperatureandvaporpressuredeficit(p<0.001),withheritabilitiesof0.4and0.6,respectively.ThemeasurementapproachpresentedgeneratesnewpossibilitiesformonitoringwheatgrowthduringSEintermsofaccuracyandthroughput.Theresultssuggestgenotype-specificgrowthhabitsareresponsivetoenvironmentalfactors,providinginsightsforfurtherinvestigationintoplantgrowthresponsetoachangingenvironmentduringSE.

8

ADDINGVALUETOPHENOTYPICDATA

Tuesday13thDecember(Afternoon)ADDINGVALUETOPHENOTYPICDATA

Willhigh-throughputphenotypingandgenotypingtechniqueshelpustobetterpredictGxEinteractions?PerspectivesfromstatisticsandcropgrowthmodelingFredA.vanEeuwijk1,DanielaV.Bustos-Korts1,2,MarcosMalosetti1,MartinP.Boer1,WillemKruijer1,KarineChenu3,ScottC.Chapman41Biometrics,WageningenUniversityandResearchCentre,theNetherlands2C.T.deWitGraduateSchoolforProductionEcology&ResourceConservation(PE&RC),TheNetherlands,email:[email protected]

3TheUniversityofQueensland,CPS,QueenslandAllianceforAgricultureandFoodInnovation,Toowoomba,Australia

4CSIROAgricultureandFood,Brisbane,AustraliaPredictionsofphenotypictraitsfordiversesetsofgenotypesacrossbroadrangesofenvironmentalconditionsareatthebasisofattemptstomaximizeselectionresponsesinplantbreedingprograms.Forthelastdecade,multi-environmenttrialsprovidedtheinformationforbuildingsuchpredictionmodels.Thesepredictionmodelsweremostlylinearmixedmodels(LMMs).LMMsareaflexibleclassofmodelswithfacilitiesformodelinggeneticandenvironmentalcorrelationsbetweentraitsandenvironmentsandallowforheterogeneityofgeneticandenvironmentalvariances.Inaddition,onthegenotypicsideofpredictionmodels,LMMsprovidethepossibilitytoincludemarkerandsequenceinformationasgenotypiccovariatestoimprovephenotypicprediction,whichisthencalledgenomicprediction.Ontheenvironmentalside,LMMscanbeextendedbyincludingenvironmentalcharacterizationsasenvironmentalcovariatestoimproveprediction.Recently,theuseofphenotypingplatformshasledtoanadditionalsourceofinformationthatmaybeusefultoimproveprediction.InthecontextofLMMs,thisinformationentersthepredictionmodelasadditionalgenotypiccovariates.Modelsforphenotypicpredictionneedtoaddressgenotypebyenvironmentinteractions(GxE).ThestatisticalobjectiveistofindfunctionsofgenotypicandenvironmentalcovariatesthatcanpredictGxE.Statisticalcriteriaforchoosingappropriategenotype-to-phenotypefunctions(linear,non-linear,parametric,non-parametric,univariate,multivariate,networks,graphicalmodels)andselectinggenotypic(markers,sequences,platformcharacterizations,resistances,tolerances)andenvironmental(environmentalcharacterizations,sensors,cropgrowthmodeloutputs,stressindices)covariatesmaybeinsufficientlycleartoguidethemodel-buildingprocessandmaynotleadtoaccuratephenotypicpredictions.Asacomplementtopurelystatisticalapproachestophenotypicprediction,cropgrowthmodelshavebeenproposed.Onthepositiveside,cropgrowthmodelsprovideacausalorderingandidentificationofphysiologicalparameters,componenttraitsandtargettraits(yield,resistance,tolerance,quality)thatcanhelpstreamlinetheprocessofphenotypicprediction,whereasstatisticalmodelsmayatbestselectandestimateapproximatetraitconfigurationsandorderingsfromthedataitself.Cropgrowthmodelsalsomakeexplicittheenvironmentalinformationthatisrequiredforphenotypicprediction.Onthenegativeside,cropgrowthmodelsoftencontainparametersandinputsthatarehardtoobtainandmeasureinpracticeondiversesetsofgenotypes.

9


Wehavebeenstudyingvariouswaysofhybridizingstatisticalpredictionmodelswithcropgrowthmodels.Astraightforwardhybridmethodofpredictioninsertsgenomicpredictionsforcomponenttraitsincropgrowthmodelsforyieldandothertargettraits.Moresophisticatedhybridizationsarepossibleandwillbepresented.Pointsofconsiderationarethedesignandanalysisofindividualphenotypingtrials,bothinfieldtrialsandonplatforms,thechoiceofgenotype-to-phenotypemodels,andtheselectionofgenotypicandenvironmentalcovariates.Specialattentionwillbegiventotheaddedvalueofhigh-throughputgenotyping,phenotypingandenvirotypinginformationforprediction.PhenotypingforcropimprovementinadiversityofclimaticscenariosFrançoisTardieu1,EmilieJ.Millet1,SantiagoAlvarezPrado1,AudeCoupel-Ledru1,LlorençCabrera-Bosquet1,SébastienLacube1,BorisParent1,ClaudeWelcker11INRALEPSE,Montpellier,France;email:francois.tardieu@supagro.inra.frTheplantsciencecommunityhastodesignnewgenotypesabletocopewithdiverseenvironmentalconditions,inparticularthoselinkedtoclimatechange.Amajorissueistocombine"blackbox"strategies,suchasgenomicselection,withtheknowledgeoriginatedfromphenotyping.Themaincomparativeadvantageofthelatteristohelpdealwiththeenvironment-dependentvariabilityofalleliceffects.Weadoptedamulti-scalemulti-environmentapproach,which,inthefield,consistsof(i)clusteringtimecoursesofenvironmentalvariablessimulatedbyacropmodelin60sitesx30yearsundercurrentandfutureconditionsintosixtemperatureandwaterdeficitscenariosexperiencedbyplants;(ii)performingfieldexperimentsincontrastingenvironmentalconditionsacrossEuropewithapanelofmaizehybrids;(iii)assigningindividualexperimentstopreviouslydefinedscenariosbasedonenvironmentalconditionsmeasuredineachfield;and(iv)analyzingthegeneticvariationofplantperformanceforeachenvironmentalscenarioviagenome-wideassociationstudies(GWAS).LargevariationsofQTLeffectsdependingonenvironmentalscenariosresultedinapatternassociatedwitheachQTL,definedbythescenariosinwhichithadpositive,negativeornoeffect.Inaphenotypingplatform(Phenoarch),weestimatedinterceptedlightandradiationuseefficiencyofeachhybridinthesamepanelviaafunctional-structuralmodelusing3Dreconstructionsofeachplant.Wealsoestimatedthesensitivityofgrowthtowaterdeficitofeachhybridviaajointanalysisofseveralexperimentswithcontrastinglight,evaporativedemandandsoilwaterpotential.Asawhole,thecombinationoffieldandplatformstepsresultsinadatasetthatallowsidentifyinggenomicregionsassociatedwithtoleranceinspecificheatanddroughtscenarios,andwithtraitsassociatedwiththesegenomicregions.Finally,modelsallowidentifyinggeographicalregionsinwhichagivencombinationofallelesislikelytohavecomparativeadvantages.Thisapproachcanbeusedforassessingthegenotypicperformanceandthecontributionofgenomicregionsundercurrentandfuturestresssituationsandthusforacceleratingcropbreedingfordiverseenvironments.

10


StrategiestohandleroothydraulicarchitectureatmultiplescalesXavierDraye1,ValentinCouvreur1,GuillaumeLobet1,2,FelicienMeunier1,MathieuJavaux1,21UCL,EarthandLifeInstitute,Belgium2FZ-Juelich,GermanyTheimportanceofadeeprootsystemhasbeenwidelyadvocatedasacontributingcomponenttodroughtadaptation.Nevertheless,recentresultsandmodellingstudiesindicatethat,beyondthedepthoftherootsystem,thedynamicsofsoilexplorationandthespatialdistributionofrootwateruptakemayhaveastronginfluenceonshorttermplanttranspirationandlong-termsoilwateravailability.Inparticular,hydraulicpropertiesofindividualrootsegmentsplayakeyroleindeterminingthedistributionofwateruptakethroughouttherootsystem.Wehaverecentlydevelopedadetailedmodelofwaterfluxacrossrootsthatstreamlinestheintegrationofmolecular(e.g.aquaporinexpression),anatomicalandhistologicaldatatopredictmacroscopicrootsegmenthydraulicproperties.Thelatterfeedintoasoil-plantmodelofsoilwaterdynamics(R-SWMS)whichscalesuplocalpropertiestoplant-andplot-levelwateruptake.Wearecurrentlydevelopingananalysisframebasedon(1)observedstandardrootsystemarchitecturesobtainedwithhighthroughputrootphenotypingplatform(usingaeroponicsasunconstrainedgrowingsystem),(2)predictedrootsegmenthydraulicproperties(guidedfrommolecular-andorgan-scaledataofhypotheses),(3)simulationsofastandardwateruptakeunderspatiallyuniformsoilmoistureand,(4)analysisofthedeviationsfromthestandarduptakeinconditionsofheterogeneousandvariablesoilmoisture.UsingstructuralmodelstovalidateandimproverootimageanalysispipelinesGuillaumeLobet1,2,5,IkoKoevoets3,ManuNoll1,LoïcPagès4,PierreTocquin1andClairePérilleux11InBioS,UniversitédeLiège,Belgium.Email:[email protected]:Agrosphare,ForschungszentrumJülich,Germany3SwammerdamInstituteofLifeSciences,UniversityofAmsterdam,TheNetherlands4PlantesetSystèmesdeCultureHorticoles,INRA,Avignon,FranceManystructuralrootmodelshavebeendeveloped,eithergenericorforspecificspecies,andthesehaverepeatedlybeenshowntofaithfullyrepresenttherootsystemstructure,aswellasbeingabletooutputground-trutheddataforeverysimulationandimage,independentofrootsystemsize.However,theyhavealmostneverbeenusedasvalidationtoolsforimageanalysisprocedure.Herewewillshowthatstructuralrootmodelscanbeusedincombinationwithimageanalysispipelinestoassessandimprovetheiroverallperformance.First,wewillshowthatanin-depthanalysisofrootimageanalysispipelinesusingsuchmodelsrevealsstronglimitationsintheirabilitytomeasurecomplexrootsystems.Secondly,wewillpresentaninnovativestrategythatcombinesrootmodelsandmachine-learningalgorithms(random-forests),thathastheabilitytoincreasethemeasurementaccuracy.

11


Combininghigh-throughputphenotypingandQTLmappingtorevealthedynamicgeneticarchitectureofmaizeplantgrowthChenglongHuang1,2,4,XuehaiZhang1,4,DiWu2,FengQiao1,KeWang2,YingjieXiao1,GuoxingChen3,LizhongXiong1,WannengYang1,2,JianbingYan1

1NationalKeyLaboratoryofCropGeneticImprovement,NationalCenterofPlantGeneResearch(Wuhan),HuazhongAgriculturalUniversity,Wuhan430070,PRChina

2CollegeofEngineering,HuazhongAgriculturalUniversity,Wuhan430070,PRChina3MOAKeyLaboratoryofCropEcophysiologyandFarmingSystemintheMiddleReachesoftheYangtzeRiver,HuazhongAgriculturalUniversity,Wuhan430070,PRChina

4Theseauthorscontributedequallytothiswork.Correspondingauthors:WannengYang([email protected])andJianbingYan([email protected])Withtheincreasingdemandsincropbreedingfornoveltraits,theplantresearchcommunityhastoquantitativelyanalyzethestructureandfunctionoflargenumbersofplants.Acleargoalofhigh-throughputphenotypingistobridgethegapbetweengenomicsandphenomics.Inthisstudy,weobtained106traitsfromamaizerecombinantinbredlinepopulationacross16developmentstagesusingtheautomaticphenotypingplatform.Theresultsshowedthattheexponentialmodelhadbetterpredictionabilityforbiomassaccumulation,eveninearlyplantgrowthstages.QTLmappingwithahigh-densitygeneticlinkagemapwasusedtouncoverthegeneticbasisofthesecomplexagronomictraits,anditidentified2249QTLsforallinvestigatedtraits.Wealsoconductedtraits-locinetworkanalysisandthreehousekeepinglociweredetectedinthehubpointsfortwotraits.Theseresultsrevealthedynamicgeneticarchitectureofmaizeplantgrowth,whichwill,inthenearfuture,enhancethemaizeideotypeusedinbreeding.Useofhigh-throughputphenotypingatCIMMYT:Newmodels,challengesandperspectivesJuanBurgueño,JoséCrossa,SamTrachsel,SuchismitaMondalInternationalMaizeandWheatImprovementCenter(CIMMYT)CIMMYTannuallyestablishesalargenumberofmaizeandwheattrialsatsitesinMexicoandwithpartnersfromthepublicandprivatesectorindevelopingcountriesinAfrica,AsiaandCentralAmerica.Oneofthebiggestchallengesistogeneratedataofconsistentlyhighqualityfromhundredsofexperimentsperyearbecausetheseexperimentsareconductedinseveralcountriesunderdifferentenvironmentalandagronomicconditions.Commercialavailabilityofunmannedaerialvehicles(UAV)andthecontinuedimprovementofcameras(multi-spectral,hyper-spectralandthermal)haveopenednewopportunitiesforthedevelopmentandadoptionofHPPinlarge-scalemulti-environmenttrials.Thesetechnologiesobtainhighqualitydataquicklyandatrelativelowcost.Adoptionofthesetechnologieswillenablefield-levelgenetictestingatascalethatwasnotpreviouslypossible.CIMMYThasbeenintroducingsomeofthesenewtechnologiesinfieldphenotypingsincetheearly2000s.Modernhigh-resolutioncamerascanprovidereflectancedataathundredsofwavelengths.Thisinformationcanbeusedtoderivevegetationindices(VI)thatarecorrelatedwithimportantagronomic

12


andphysiologicaltraits.However,thedatageneratedbyhigh-resolutioncamerasisricherthanwhatcanbesummarizedinaVI.Tomakefulluseofrichreflectancedata,wecomparethepredictiveperformanceofseveralVIwiththatofpredictionequationsthatuseinformationfromtheentireavailablespectrum.Weconsideredthreestatisticalmethods:ordinaryleastsquares(OLS),partialleastsquares(PLS,adimensionreductionmethod)andaBayesianshrinkage/variableselectionprocedure(BayesB,whichhasbeenusedingenomicpredictionbuthereisusedtoselectthemostimportantspectralbands).Correlationsbetween0.3and0.6werefoundwhenpredictingmaizeyieldinasetof12experiments.Also,wefoundthatusingthecompletespectralbandsproduceshighercorrelationswithgrainyieldthanusingsomevegetativeindexes.Simultaneously,geneticevaluationofseveralvegetativeindexesbycalculatinggeneticvariability,heritabilityandotherrelatedmeasurementsshowedhighvariabilitybetweentrialswithrepeatabilityrangingfromto0to0.9.Resultsarepromisingbutmoreresearchisrequiredtoincreasedataqualityandprecisionatthelowestpossiblecostandtoimproveanalyticalmethodologies.Usinghigh-throughputphenotypingtoimproveaccuracyingenomicprediction:ExamplesforcropsimulatedtraitsDanielaBustos-Korts1,2,MarcosMalosetti1,MartinP.Boer1,ScottC.Chapman3,KarineChenu4,FredA.vanEeuwijk11Biometris,WageningenUniversityandResearchCentre,TheNetherlands2C.T.deWitGraduateSchoolforProductionEcology&ResourceConservation(PE&RC),TheNetherlands.Email:[email protected]

3CSIROAgricultureandFood,Brisbane,Australia4TheUniversityofQueensland,CPS,QueenslandAllianceforAgricultureandFoodInnovation,Toowoomba,Australia

Predictionofthefinalstateofatargetcomplexandcompositetrait(yield)canbeimprovedbyaddinginformationonthedynamicsofthistraitanditsconstituentcomponents.High-throughputphenotypingtechniquesprovideinformationonthedynamicsoftargetandcomponenttraits.Thisinformationcanbesummarizedbyparametricorsemi-parametricstatisticalmodelsforgrowth.Statisticalparameterscharacterizinggrowthovertimeareexpectedtohavegreaterheritabilityandofferabetterintegrationofplantresponsestoenvironmentalconditionsthansingletimemeasurementsonthesametraitsandshouldbeusefulascorrelatedtraitsinmulti-traitgenomicpredictionmodels.Multi-traitgenomicpredictionmodelsarepreferredoversingle-traitmodelsforthefollowingscenarios:(1)predictingatargettraitfrommeasurementsofcomponenttraitsatearlygrowthstages,therebyreducingtheselectioncycle;and(2)increasingaccuracyforpredictionofatargettraitbyusingmulti-traitpredictionmodelsthatcombinethetargettraitwithitscomponents.Thesuccessofthesetwopredictionscenariosdependsontheheritabilitiesofalltraitsinvolvedandthecorrelationstructureofthetargettraitwithitscomponents.Tostudytheabovepredictionscenarios,wesimulatedaRILpopulationwithsegregatingQTLsfortheAPSIM-Wheatparametersthatregulatephenology,biomasspartitioningandtheabilitytocaptureenvironmentalresources.Thephysiologicalparametervaluesdefiningthesegregatingpopulationwere

13


usedtosimulateyield,yieldcomponentsandphenologyduringthegrowingseason.Weconsidereddifferentexperimentalandmeasurementerrorscenarios.Lowheritabilityofsingletimepointswascompensatedbyusingphenotypesatmultipletimepointssimultaneously(h2increasedfrom0.2to0.6).Incorporatingtraitdynamicsovertimeinamulti-traitgenomicpredictionmodelshowedhigheraccuracy,comparedtosingletraitpredictionandcouldpotentiallybeusedtoshortentheselectioncyclebyproducingyieldpredictionsearlyinthegrowingseason.APSIM-Wheatwithgenotype-dependentparametersprovedtobeavaluabletooltoassesstheconvenienceofinvestinginmoreprecisefieldmeasurements,ortodeterminethenumberandtimingofmeasurementsrequiredtobestcharacterizecomponenttraits.

PosterSession:AdvancesinPlantPhenotypingTechnologies

AutomatedmeasurementtoolforphenotypingrootandhypocotylgrowthofArabidopsisplantsAdedotunAkintayo1,TrevorNolan2,YanhaiYin2,SoumikSarkar11DepartmentofMechanicalEngineering,IowaStateUniversity.Email:[email protected],DevelopmentandCellBiology,IowaStateUniversityPlantgrowthresponsestovariousenvironmentalconditionsorchemicaltreatmentscanbequantitativelyassayedbymeasuringhypocotylorrootlengthsofArabidopsis(Arabidopsisthaliana)seedlings.Thesemeasurementscanbeemployedtofurtherunderstandabiologicalprocessofinterest—forexample,howaclassofplantsteroidhormonescalledBrassinosteroidsregulatethousandsofgenestocontrolplantgrowth.Inthisresearch,weautomatedtheprocessofmeasuringhypocotylandrootlengthsinArabidopsisandappliedthedevelopedtooltogainfurtherinsightintotheBrassinosteroidpathwaybyperformingmeasurementsinthepresenceorabsenceofBrassinazole,aBrassinosteroidbiosynthesisinhibitor.Recentadvancesinphenotypingtechniquesarerapidlyencouragingtheubiquityoflarge-scaleimagingofplants,whilehardware-basedphenotypingmethodsbyrobotsnowrequireembeddingofintelligentalgorithms.Theautomatedmeasurementtoolproposedisanenhancedversionofthehierarchicalstructure-basedpolylinesimplificationalgorithmthathasprovensuccessfulincartographicapplications.Itwasimprovedsothatitcoulddeterminethesortedcoordinatesoftheverticesbetweentwonodesmorerobustlythanthemorphologicaloperation,thinning,whichfailswithmulti-angularforegrounddisturbancecrossingthedesiredobjects.Ourforward-backwardevaluationalgorithmadjuststheshortestpathobjectivefunctionofthebi-directionalsearchmethodtomarchingfromoneidentifiedtissuepointtoanotherontheforegroundobjectinablindpathdetectionmanner.Itsignificantlyreducesthenumberofverticesthatneedstobespecifiedoneachplanttoonlythoseoftheendpointswithhigherresolution,whilethecurrentlyapplied‘ImageJ’toolrequiresmorecoordinates.Itwasdeployedandtestedonseveralimagesinasemi-automatic,human-in-the-loopmannerforthemeasurements.WecompareitsresultswiththoseoftheImageJmeasurementtool.

14

Posters:AdvancesinPlantPhenotypingTechnologies

High-throughputscreeningtechniquesforstomatalresponsetoABAbasedonchlorophyllfluorescenceundernon-photorespiratoryconditionsSasanAliniaeifard1,UulkevanMeeteren2

1DepartmentofHorticulture,CollegeofAbureyhan,UniversityofTehran,Iran.Email:[email protected]

2HorticultureandProductPhysiology,WageningenUniversity,theNetherlandsFineregulationofstomataapertureisrequiredtoallowsufficientCO2uptakeforphotosynthesis,whilepreventingexcessivewaterlossthroughtranspiration.Althoughstomatalmovementsarecontrolledbyastrongandcomplicatedsystem,thiscontrolsystemcanbedisturbedundercertainenvironmentalconditions,causingchangesinclosureresponseofthestomata.Sinceanalysisofstomatalresponseshasbeenmainlylimitedtomicroscopicandgasexchangemeasurements,limitingthescaleofscreening,hereweusedchlorophyllfluorescenceimagingundernon-photorespiratoryconditions(20mmolmol-1O2)toanalyzestomatalresponsestoexogenousABA.AdecreaseinphotosystemIIphotochemicalefficiency(ΦPSII)iscloselyrelatedtostomatalclosurewhenphotorespirationisinhibitedthroughexposuretoadecreasedO2environment,butthisrelationshipisnotalwayslinear.ToconfirmthatdecreasedΦPSIIwasduetostomatalclosure,attheendofΦPSIIimagingsampleswerekeptinanatmospherewithhighCO2(20mmolmol-1O2,50,000µmolmol-1CO2)for5minutestotesttherecoveryofΦPSII.ClosureofstomataresultsinscarceCO2inthestomatalcavity.Inthissituation,whendecreasedFPSIIisduetolackofinternalCO2,veryhighCO2concentrationwilldiffuseintothestomatalcavityandrestoretheFPSII.ToscreenforstomatalresponsestoABA,leafdiscswerepreparedfromdifferentleaves.Theleafdiscswereputwiththeirabaxialsurfaceupinpetridishesfilledwithstomata-openingmediapfdifferentABAconcentrations.Toenablefastanduniformuptakeofthesolutions,3minvacuuminfiltrationwasused.Aftervacuuminfiltration,theleafdiscswerepre-incubatedintheabove-mentionedABA-solutionsat20°Cand40µmolm-2s-1irradiance.Thereafterthepetridisheswereplacedinaflow-throughcuvetteunderachlorophyllfluorescenceimagingsystem.Just10minutesexposuretonon-photorespiratoryconditionsplus5minutesexposuretohighCO2concentrationwereenoughtoanalyzethestomatalresponsesofmorethan40samples.Withthistechnique,notonlywerehugenumbersofstomataanalyzedinaveryshortperiodoftime,butheterogeneityinstomatalresponseovertheleafsurfacewasalsovisible.

15


AdvancedmathematicalalgorithmstocharacterizeolivevarietiesthroughmorphologicalparametersKonstantinosN.Blazakisa1,LucianaBaldonib2,AbdelmajidMoukhlic3,MarinaBufacchid4,PanagiotisKalaitzisa1

1DepartmentofHorticulturalGenetics&Biotechnology,MediterraneanAgronomicInstituteofChania(MAICh),73100Chania-Crete,Greece.

2ItalianNationalResearchCouncil,InstituteofBiosciencesandBio-Resources(CNR-IBBR),ViaMadonnaAlta,06128Perugia,Italy.

3INRAMarrakech,URAméliorationdesPlantes,Marrakech,Morocco4ItalianNationalResearchCouncil,InstituteforAgricultureandForestSystemsintheMediterranean(CNR-ISAFOM),ViaMadonnaAlta,06128Perugia,Italy.

Themorphologicalanalysisofolivefruits,leavesandstonesmayrepresentanefficienttoolforthecharacterizationanddiscriminationofvarietiesandtheestablishmentofphenotypicrelationshipsamongthem.Inrecentyears,muchattentionhasbeenfocusedontheapplicationofDNAmolecularmarkersduetotheirhighcapacitytoefficientlyandreliablydiscriminatecultivars.Inthistalk,wewillpresentasemi-automaticmethodologyofdetectingvariousmorphologicalparametersbasedonimageanalysistools.Anumberofmorphologicalparametershavebeenusedtocharacterizeolivegermplasmcollectionsfromdifferentcountries.Thedataobtainedcouldcomplementolivedatabasescomprisedofgenetic,molecularandmorphologicaldatasupportingeffortstoefficientlydiscriminatethemandinfereithergeneticand/ormorphologicalrelations.Withtheaidofcomputingandimageanalysissoftware,wecreatedsemi-automaticalgorithmsapplyingintuitivemathematicaldescriptorsthatquantifymanyfruit,leafandendocarpfeatures.Inparticular,weexaminedquantitativeandqualitativecharacterssuchassize,shape,symmetry,surfaceroughnessandthepresenceofadditionalstructures(nipple,petiole,etc.).Finally,inthistalkwewillpresenttheefficiencyandrobustnessoftheproposedmethodologyforthedescriptionofothercropmorphologiessuchastomatoes,pears,grapes,etc.AIRPHEN:AmultispectralcameradedicatedtofieldphenotypingfromdroneobservationsA.Comar1,F.Baret2,G.Collombeau2,M.Hemmerlé1,B.deSolan3,D.Dutartre4,M.Weiss2,S.Madec2,F.Toromanoff3

1HIPHEN,Avignon,France2INRA,UMREMMAH,Avignon,France3ARVALISInstitutduvégétal,Avignon,France4ITB,Avignon,FranceTheAIRPHENmultispectralcamerawasspecificallydesignedforaccessingcanopybiophysicalvariablesfromdroneobservationswithinfieldphenotypingexperiments.Thecameraweighsabout200g,recordsGPSandIMUinformationandallowstriggeringcompaniondevicesincludingathermalinfraredorRGBhighresolutioncamera.Sixnarrowwavebandsselectedtosamplethechlorophyllspecificabsorptioncoefficientrecordthereflectedradiationataresolutionof1,280x960pixels.The8-mmfocallengthprovidesresolutionsatgroundlevelrangingfromafewmillimeterstoafewcentimeters,dependingon

16


flightaltitude.Oneofthesixcamerasisequippedwitha4.2-mmfocallengthlenstoeasetheimagealignmentprocessandgeneratemoreaccurate3Dpointclouds,whileprovidingtheincreasedthroughputallowedbythelargerswathofthecamerainthisband.Apipelinecalled‘phenoscript’wasdevelopedtoprocesstherawimagesoftheAIRPHENcamera,computeasoutputsamultispectralortho-image,andextractthemicroplotsfromtheoriginalimagesandthecorresponding3Ddensepointcloud.Theextractscanlaterbeusedtocomputevegetationindices,measurecanopyheight,andderiveafewbiophysicalvariablesfromeitherempiricalapproachesorradiativetransfermodelinversion.TheuseoftheAIRPHENcameraisillustratedinseveralexperimentsconductedonarangeofspeciesincludingwheat,maize,sugarbeetandpotatoes.Thedynamicsofthevegetationindicesshowaverygoodconsistencyofthemeasurementsovertime.Theaccuracyofestimatesofseveralbiophysicalvariablesincludingthegreenfraction(GF),theGreenAreaIndex(GAI),thefractionofinterceptedradiation(FIPAR)andcanopy(CCC)orleaf(LCC)chlorophyllcontentispresented.Thecapacitytocombinethemultispectralcamerawiththermalinfraredimageryisalsoillustratedbythecharacterizationofwaterstress.FurtherdevelopmentsontheuseoftheAIRPHENcameraarediscussed,includingadvancedinterpretationoftheimagesbasedon3Dcanopystructuremodels,installationonothervectorssuchasthephenomobileforworkinginactivemode,orahandheldsystemdedicatedtosmallandlow-costexperiments.Derivingcanopyheightfromdroneobservations:OverviewoftheexpectedaccuracyandmaininfluentialfactorsS.Madec1,F.Baret1,G.Collombeau1,S.Thomas2,A.Comar3,M.Hemmerlé3,B.deSolan2,D.Dutartre4

1INRA,UMREMMAH,Avignon,France,email:[email protected]égétal,Avignon,France3HIPHEN,Avignon,France4ITB,Avignon,FranceDronesallowcollectingimageswithlargeoverlapssothatthesamepointonthegroundcanbeseeninseveralimagesandthusfromdifferentangles.Photogrammetrictechniquesarethenappliedtoexploitthispropertyandderiveadense3Dpointcloudfromwhichthemacro-structure(i.e.,theconvexhullofthevegetation)andthecorrespondingcanopyheightcanbecomputed.Thiscanopycharacteristicisveryappealinginthecontextofhigh-throughputplantphenotypingunderfieldconditions.Althoughthistechniqueisbecomingrelativelycommon,theexpectedaccuracyandthemaininfluentialfactorsarenotpreciselyknown.Theobjectiveofthisstudywastoquantifytheaccuracywithwhichcanopyheightcanbeestimatedandtoidentifythemainfactorsthathavetobeaccountedfortoachievethebestperformance.Aseriesoffieldphenotypingtrialswereconductedonsugarbeet,wheatandmaizein2015and2016.AhexacopterwasflowncarryingeitheraSonyAlphahigh-resolutioncamera(24Mpixels)oranAIRPHENmultispectralcamera(1.3Mpixels).GroundmeasurementsofcanopyheightwereconcurrentlycompletedusingeithervisualnotationsorvaluesextractedfromLIDARobservations.TheimagesfromthecamerasaboardthedronewereprocessedusingAgisoftPhotoScansoftwarethatalignsimagesand

17


generatesadense3Dpointcloudfromwhichtheverticaldistributioncanbecomputed.Apercentileofthedistributionisusedtodeterminethecorrespondingcanopyheight.Image-basedcanopyheightwasingoodagreementwithvisualnotationsofcanopyheight,withanuncertaintyofaround7cm(sugarbeet)to15cm(maize).MoredetailedresultsgatheredonwheatusingLIDARshowedthatmostoftheseuncertaintiesareattributabletotheprecisionofthevisualnotations.SeveralfactorspotentiallyaffectingtheaccuracyofcanopyheightestimationfromUAVobservationswereanalyzed.Oneofthemostimportantfactorsisthewaythesoilreferencealtitudeiscomputed.Indensecanopieswherethesoilcanhardlybeseen,itisadvisabletouseapreviousreference3Dpointcloudcorrespondingtobaresoil.Otherfactorsincludethegroundresolutionrelativetothesizeofthevegetationelements,thenumberofimagesfromwhicheachpointonthegroundisseen,thefieldofviewandtherangeofdirectionsavailable.TheinfluenceoftheparametersusedinAgisoftPhotoScanforgeneratingthe3Dpointcloudwasalsoevaluated.Finally,anoptimalprocedureispresentedthatshouldensureaccurateestimatesofcanopyheightthatcanbeusedforgenotypecharacterizationinphenotypingexperiments.Awirelessenvironmentaldatacollectionsystemforhigh-throughputfieldphenotypingThomasTruong,AnhDinh,KhanWahidUniversityofSaskatchewan,Canada.Email:anh.dinh@usask.caTheplantresearchcommunityneedstocombinetheenvironmentalconditionswithplantphenotypingtohaveacompletepictureofthegrowingprocess.Thedataprovidedbystate-levelweatherstationsdonotaccuratelyrepresentfieldconditions.Inaddition,otherenvironmentalconditionsaroundtheplantsarenotreadilyavailablewithoutafieldtrip.Asaresult,thereisaneedforaremotedatacollectioninthefieldforenvironmentalconditions.Thisarticledescribesthedesignofalocalenvironmentstationtobeinstalledinafieldforphenotyping.Thesystemincludesvarioussensors,amicrocontroller,awirelessmodule,awirelessinternet,andaclouddatabase.Theabovegroundsensorsincludeairtemperature,relativehumidity,sunlight,leafmoisture,CO2level,windspeed,winddirection,andrainfall.Thesoilwatercontent,soiltemperature,andpHsensorsarecurrentlyusedtomeasureundergroundconditions.Allsensorlevelsarecollectedbyamicrocontrollerwhichconvertsthesignalsintoenvironmentaldata.Thedataarestoredevery10minutes(oratatimeintervaldeterminedbytheusers)inasecuredigitalcardandalsosenttoahostdatabaseinthecloudasthesystemhasanInternetofThingsfeaturebuilt-in.Updateddatacanbeviewedandretrievedanytimeonawebsite.Thesystemisalsoequippedwithalocalwirelesssensornetworktohavemoredetailedenvironmentdatainarangeof1,000mindiameter.Thesystemrequiresnomaintenanceduringoperationandhasitsownsolarcharging.Relocationofthesystemdoesnotrequireanyset-upastheGPSwillautomaticallyupdatethelocation,date,andtimetothewebsitewithcoordinatesonGoogleMap.Threesystemswerebuiltandsuccessfullycollecteddatainthespring.

18


Decipheringthephenotypiccode:Fromlabtofield-scaleMarcusJansen,StefanPaulus,TinoDornbuschLemnaTecGmbH,Germany.Email:tino.dornbusch@lemnatec.deTheBookofNatureiswritteninthelanguageofmathematics,asGalileoGalileiaptlyformulatedalmost400yearsago.Havingthelatestinventionsinbiotechnologyathand,scientistsstarteddecipheringthemolecularcodeoflife.Althoughtremendousprogressinmolecularbiologyhasbeenachieved,thetranslationofgenotypesintophenotypesispoorlyunderstood.Nowadays,progressinnon-invasiveimagingsensorsprovidesaframeworktolookintophenotypesatthesubcellular(e.g.,microscope)andfield(e.g.,airborneimagery)levels.Theamountanddiversityofdatasuchas1Dsensorreadings,2Dimagesor3Dpointcloudshavespecificstorageandanalyticaldemands.Thechallengeistotranslatedataintoinformationrelatedtothebiologicalobjectorprocess.Therefore,dataanalysisisonepartofthephenotypingbottleneck,whichisbeingaddressedbyvarioussoftwaretools.MakingthesetoolseasilyaccessibletoresearchersandbreedersisoneofLemnaTec’smainobjectives.Incontrasttocode-basedimageanalysisapproaches,LemnaTecprovidesagraphicalprogramminginterfacecalledLemnaGrid.Inotherwords,nolineofcodeneedstobewritten.LemnaGridcontainsvarioustoolboxesfor2Dimageanalysis,hyperspectraldataor3Dpointclouds.Theoutputdeliversdimensions,morphologies,andspectralreflectanceofthemeasuredobjects.LemnaGridiscurrentlyappliedstartingfrommicroscopyimagesinthelaboratory,whole-plantimagesinthegreenhouse,andcanopyimagesinthefield.Phenotypingsolutionsaredesignedtoworkforrelatedexperimentsandtobesharedamongusers.TheapplicationrangeofLemnaTecsoftwareisconstantlybeingbroadened,andnewplatformssuchasthenewFieldScanalyzerarebeingdeveloped.Recentresultsfrommulti-sensorfieldmeasurementsdelivereddataoncanopypropertiesofcereals.LinkinginformationderivedfromsuchRGB,hyperspectral,laserscanningandfluorescencedatatothecrops’biologicalandagronomicalpropertieswillenablecomprehensivefieldphenotyping.StudiesofrootsystemarchitectureinsoybeanusingcomputervisionandmachinelearningKevinFalk1,TalukdarJubery2,SayedVahidMirnezami2,KyleParmley1,JohnathonShook1,ArtiSingh1,SoumikSarkar2*,BaskarGanapathysubramanian2*,AsheeshK.Singh1*

1DepartmentofAgronomy,IowaStateUniversity,Ames,USA.Email:[email protected],IowaStateUniversity,Ames,USAWiththeadventofcomputervision,thereisrenewedinterestinuncovering“thehiddenhalf”ofplants,usingmeasurementsofrootsystemarchitecture(RSA)inconjunctionwithmachinelearningtodiscovertraitcorrelationswithinandbetweengenotypesandphenotypes.Thisstudyincludedmorethan300diversesoybeanaccessionsunder2D(undercontrolledconditions)and3D(fieldtests)imagingplatforms,supportedbyprocessinganddataanalytictoolstodeepphenotypeforimportantRSAtraitsusinganin-houseimagingsoftware,ARIA.Both2Dand3Dimagingplatformsrevealedtremendous

19


geneticvariabilityfortraitssuchasrootshape,length,massandangle.The3Dimagingplatformdevelopedmakesitpossibletophenotypehundredsofgenotypesandextractnumerousrootsystemtraits.The2Dplatformisnon-destructive,addingobservationsofseedlingrootgrowthrates.WearefocusingonuseofmachinelearningalgorithmstounveilrelationshipsbetweenRSAandcorrelatethemwithabove-groundperformancetraits,primarilyseedyieldandparametersrelatedtotheMonteithequation.Buildingontheimagingplatformdeveloped,wearenowpursuingdeepgenome-widestudies.RecentadvancementindevelopmentofautonomousmobilerobotsforplantphenotypingRezaFotouhi,AryanSaadatMehr,PierreHucl,MostafaBayati,QianWeiZhangCollegeofEngineering,UniversityofSaskatchewan,Saskatoon,Canada.Email:reza.fotouhi@usask.caConsideringthecurrentneedsoftheworldpopulationtoincreasetherateofcropproduction,thereisastrongneedforresearchonscreeningcropsbyuseoftechnologyforhigh-throughputphenotyping(HTP).Theobjectiveofourresearchistodevelopafield-basedHTPmobileplatformforrapidassessmentofmultiplequantitativeplanttraitswithafocusonwheat,canola,andlentil.Thefieldplatformwillbedesigned,fabricated,andtestedinalaboratorysettingandevaluatedincropbreedingnurseriesinSaskatchewan.Manyimagingtechnologiesarewelldevelopedandsophisticated,butnotnecessarilyoptimalforinsituhigh-throughputplantcharacterization.Whichtechnologiesareappropriateforfieldplotscaleadvancedcharacterizationofplantsinthefield,andhowcanthesebedeliveredina“locationindependentmethod”aresomeofthequestionstobeanswered.Whichsensorsaremostimportantforplantcharacterizationinabreedingprogram,andisitpossibletocharacterizeplantsbelowgroundinthefield?Aliteraturereviewofdevicesforplantphenotypingandthestateofresearchindevelopinganautonomousmobilerobotforplantphenotypingwillbepresented.Anautomatedsoybeanmulti-stressdetectionframeworkusingdeepconvolutionalneuralnetworksSambuddhaGhosal1*,DavidBlystone2*,HomagniSaha1,DarenMueller3,BaskarGanapathysubramanian1,AsheeshK.Singh2,ArtiSingh2,SoumikSarkar11DepartmentofMechanicalEngineering,IowaStateUniversity,USA.Email:[email protected],IowaStateUniversity,USA.Email:[email protected],IowaStateUniversity,USASignificantcropyieldlossesarecausedbybioticandabioticstresses.Tomitigatepotentialcroplossesfromsuchstresses,ahigh-throughputandearlydetectionsystemforregularandwidespreadmonitoringisofvitalimportance.Traditionally,soybeanabiotic/bioticstressdetectionmethodshavereliedonsubjectivesymptom-basedassessmentbyagriculturalscouts.Machinelearning-basedautomatedstressdetectionusingreadilyavailablesensorsorsmartphonecamerascanhaveatransformativeimpactonsoybeanfarmingandscouting.Thegoalofthisstudywastodevelopanautomatedframeworktoidentifyabiotic/bioticstressesonsoybean[Glycinemax(L.)Merr.]underfieldconditions,basedonimageclassification,usingDeepConvolutionalNeuralNetworks(DCNN).More

20


than25,000leafletimageswerecollectedfromIowa(USA)fieldsforfivebioticstresses(bacterialleafblight,bacterialpustule,frogeyeleafspot,Septoriabrownspotandsuddendeathsyndrome)andthreeabioticstresses(irondeficiencychlorosis,potassiumdeficiencyandherbicideinjury)aswellashealthysoybeanleafletsusingastandardimagingprotocol.Afterprocessingtheseimagesfurther,adatasetcomprisedof16,210leafletimagesfromallthecollectedimageswasusedtodeveloptheDCNNmodel.Forourinitialexperiment,themodelwastrainedbasedonatrainingsetof11,345images,whichwasthenvalidatedon3,242imagesandfinallytestedon1,630images.Preliminaryresultsshowthatthetrainedmodelisabletoefficientlydifferentiatebetweentheeightdifferentsoybeanstressesunderconsiderationaswellashealthyleaflets.Thetrainedmodelcanbeeasilyexportedtoamobileplatformutilizingvisualsensorsinadedicatedlow-costimagingdeviceorevensmartphonecamerastoperformefficientandrobustonlinedetectionofsoybeanstressesinrealtime.High-throughputricephenotypingfacility:StrategiesandchallengesWannengYang1,2,3,ChenglongHuang2,3,LingfengDuan2,3,HuiFeng2,XiuyingLiang3,QianLiu4,GuoxingChen5,LizhongXiong1

1NationalKeyLaboratoryofCropGeneticImprovement,NationalCenterofPlantGeneResearch,HuazhongAgriculturalUniversity,Wuhan430070,China

2HubeiKeyLaboratoryofAgriculturalBioinformatics,HuazhongAgriculturalUniversity,Wuhan430070,China

3CollegeofEngineering,HuazhongAgriculturalUniversity,Wuhan430070,China4BrittonChanceCenterforBiomedicalPhotonics,WuhanNationalLaboratoryforOptoelectronics,HuazhongUniversityofScienceandTechnology,Wuhan430074,China

5MOAKeyLaboratoryofCropEcophysiologyandFarmingSystemintheMiddleReachesoftheYangtzeRiver,HuazhongAgriculturalUniversity,Wuhan430070,China

Correspondingauthor:WannengYang([email protected])Theadventofnext-generationsequencingtechnologyhashadamajorimpactongenomicsinashortperiodoftime.However,phenomics,anewdisciplineinvolvingthecharacterizationofthefullsetofphenotypesofagivenspecies,stilllagsfarbehindgenomics.Traditionalphenotypingtools,whichinefficientlymeasurealimitedsetofphenotypes,havebecomeabottleneckinfunctionalgenomicsandplantbreedingstudies.Torelievethebottleneck,wedevelopedahigh-throughputricephenotypingfacilitythatincludesriceautomaticphenotyping(RAP),yieldtraitsscorer(YTS),high-throughputleafscorer(HLS),andhigh-throughputhyperspectralimagingsystem(HHIS).Equippedwithmultidisciplinarytechniques,includingphotonics,automatics,computers,andmechanics,theintegratedfacilitycanextractnumerousmorphology-relatedtraits,biomass-relatedtraits,yield-relatedtraitsandphysiologicaltraits.Whencontinuouslyoperated,thetotalthroughputofourfacilityis1,920pot-grownriceplantsoutofatotalgreenhousecapacityof5,472pots.Inaddition,wediscussthefuturechallengesofricephenotyping.

21


FieldScanalzyer:HighprecisionphenotypingoffieldcropsMarcusJansen,StefanPaulusLemnaTecGmbH,Germany.Email:Marcus.Jansen@lemnatec.deCurrentphenotypingtakesplaceinastateoftensionbetweenthroughputandaccuracy,alsodescribedasscreeningversusdeepphenotyping.Moreover,theimportanceoffielddataisbecomingincreasinglyprevalent,asmostindoorcultivationenvironmentslackcomparabilitytofieldgrowthsituations.Deepphenotypingatthefieldlevelrequiressensorfusionthatallowscollectingvarioustypesofdataontheplants,farbeyondtakingRGBimages.Sensorfusionforfieldphenotypingbringstogetherlaserscanningandthermal,fluorescence,andspectralimagingcombinedwithsensorsforenvironmentalfactors.High-precisionpositioningallowsmovingeachofthesensorstothesamespotofvegetation,therebycollectingthewholerangeofdatatypesforeachindividualinthefield.Moreover,itenablestime-resolveddatatoberecorded.TheFieldScanalyzerisadeepphenotypingplatformconsistingofhighprecisionsensorsthatmonitorexperimentalplotsdaybyday,andevenseveraltimesaday.3Dlaserscannersprovidemeasurementsofthevolumeoftheplantmaterial,whichishighlycorrelatedtothebiomass.Hyperspectralcamerasallowmeasuringvegetationindicesthatarecorrelatedtophysiologicalparameterssuchasgrowth,waterandfertilizer,ortheplants’healthstatus.Furthermore,theanalysisoffluorescenceandthermalimagesdeliversparametersthatcanbelinkedtotheplants’photosynthesisandtranspirationviadedicatedmodels.Byrecordingenvironmentaldataateachmeasurementandlinkingpositiondataofallmeasurementstogenomicinformationabouttheplantsgrowingatagivenlocation,themethodallowsanalyzinglinkagesbetweenphenotypes,genotypesandenvironmentinhighspatialandtemporalresolution.Integratedanalysisofplantgrowthanddevelopmentusinghigh-throughputmulti-sensorplatformsatIPKAstridJunker,Jean-MichelPape,HenningTschiersch,DanielArend,MatthiasLange,UweScholz,andThomasAltmann

LeibnizInstituteofPlantGeneticsandCropPlantResearch(IPK)Gatersleben,Correnstr.3,06466SeelandOTGatersleben,Germany

IPKrunsthreephenotypingfacilitiesforhigh-throughputimagingofwholeplantsofsmallsize(suchasArabidopsisthaliana),mediumsize(suchasHordeumvulgare)andlargesize(suchasZeamays).Ineachsystem,plantimagesareacquiredinnear-infrared(NIR)andvisiblespectra(forRGBandfluorescenceimaging)fromtopandsideviews.Imagingisperformedrepeatedlyandthusfollowsplantgrowthdynamics.TheImageAnalysisPlatform(IAP)automaticallyextractsplantarchitecturaltraits(heightandwidth,projectedleafarea,estimatedvolume)andcolor-relatedandphysiologicaltraits(staticfluorescence,moisturecontent-relatedparameters).Thephenotypingfacilitieshavebeenextendedforfunctionalchlorophyllfluorescenceanalysisusingpulsedamplitude-modulatedchlorophyllfluorescenceimagingsystemsandforacquiring3Dheightprofilesofplantsandplantstands.Thesesystemsareintegratedintophenotypingprocedures,allowingsimultaneousacquisitionofmultiplecomplementaryplanttraits.Co-registrationoffeaturedataderivedfromallthedifferentsensors(sensorfusion)willbe

22


usedtodeducenovelandrefinedinformationonplantarchitecturalandphysiologicaltraitsandprovidethebasisformulti-traitassociationstudies.Opportunitiesandchallengesofintegratedanalyseswillbediscussedusingdifferentusecases.Furthermore,currentdevelopmentsinphenomicsdatamanagement(e.g.,MIAPPEcompliantstandardizedmetadatarepresentationforpersistentdocumentationofhigh-throughputplantphenotypingexperiments)willbeintroduced.DevelopingtheEnviratron:AfacilityforautomatedphenotypingofplantsgrowingundervariedconditionsStephenH.Howell,LieTang,CarolynJ.Lawrence-Dill,ThomasLubberstedt,StevenWhithamIowaStateUniversityTheEnviratronisanewconceptanddesigninplantphenotypingwiththepurposeoftestingplantperformanceunderdifferentenvironmentalconditions.Incontrasttocurrentphenotypingfacilities,theEnviratronisdesignedtoanalyzeplantgrowthandperformanceunderuptoeightdifferentenvironmentalconditionsinoneexperiment.Alsounlikecurrentphenotypingfacilities,plantsarenotconveyedtoacentralanalyzingstation,insteadamobileroboticanalyzer(rover)equippedwithasensorarrayvisitsplants,minimizingdisturbancesinthegrowthenvironment.InanalyzingtherelationshipbetweenGxE,mostphenotypingfacilitiesareequippedtovaryG.TheEnviratron’sspecialfeatureisitscapacitytovaryE.TheEnviratronconsistsofeightgrowthchambersthateachcanbeprogrammedtoauniqueenvironmentthatcanvaryslightlyinasingleparameterorbydifferentclimatescenarios,includingtemperature,CO2,humidity,water,andlight(durationandintensity).Therover’ssensorarrayincludesaholographiccamera,hyperspectralsensor,fluorescencedetector,infrareddetector,andRamanscatteringspectrometer.Individualpotsaredesignedtohavesoilwaterpotentialsensorswithawateringsystemtomaintainchosensoilwaterpotentials.ToenabledownstreamdiscoveryandreuseofdiversedatacollectedusingtheEnviratronsystem,aMIAPPE-compliantmetadatacollectionandreportingmechanismisunderdevelopment.LearnmoreaboutEnviratrononlineathttp://enviratron.iastate.edu/.

23


Hyperspectralimagingsystem,individualriceplants,andaccuratepredictionofabove-groundbiomass,greenleafareaandchlorophyllFengHuietal.NationalKeyLaboratoryofCropGeneticImprovementandNationalCenterofPlantGeneResearch,HuazhongAgriculturalUniversity,Wuhan,Hubei,430070,P.R.China

WuhanNationalLaboratoryforOptoelectronics-HuazhongUniversityofScienceandTechnology,1037LuoyuRd.,Wuhan,Hubei,430074,P.R.China

CollegeofEngineering,HuazhongAgriculturalUniversity,Wuhan,Hubei,430070,P.R.ChinaCollegeofInformatics,HuazhongAgriculturalUniversity,Wuhan,Hubei,430070,P.R.ChinaEmail:[email protected];[email protected],greenleafareaandchlorophyllareimportantcomponentsofplantphenomicsandtheexistingmethodsforestimationoftheseonindividualplantsareeitherdestructiveorlackaccuracy.Hyperspectralimagingisanemergingandnondestructivetechnologythatcanacquirespectralandspatialinformationsimultaneouslyforplantphenotyping.Thesystemhasthepotentialtocharacterizecerealplants.ComparingrobotsanddronesasphenotypingtoolsinfieldtrialsMortenLillemo1,EivindBleken1,GunnarLange2,LarsGrimstad2,PålJohanFrom2,IngunnBurud2

1DepartmentofPlantSciences,NorwegianUniversityofLifeSciences.Email:morten.lillemo@nmbu.no2DepartmentofMathematicalSciencesandTechnology,NorwegianUniversityofLifeSciencesNewapproachesarenecessarytomeetthegoalsofincreasedfoodproduction.Plantbreedingcanplayakeyrolebydevelopingcultivarswithhigheryieldpotentialandbetteradaptationtostress.Progressinbreedingdependsontheabilitytodesigncrosseswithcomplementarytraits,andthenperformeffectiveselectionamongtheoffspring.Thisrequirespreciseandcost-effectivemethodstoevaluatelargenumbersofplantsacrossrelevantenvironmentsandstresses.Traditionaldatacapturebasedonlow-throughputandmanualmethodsislabor-intensiveandpronetohumanerror.Whilethecostsofsequencingandgenotypinghavedroppeddramaticallyoverthelastdecade,thelaborcosthasincreasedandphenotypinghasnowbecomethebiggestbottleneckforrealizingthefullpotentialofgenomicsinplantbreeding.Toaddressthesechallenges,astrategiccollaborationhasbeeninitiatedattheNorwegianUniversityofLifeSciences,whereresearchgroupsacrossdepartmentsanddisciplinesarejoiningforcestotestnewphenotypingtechnologiesinplantbreedingresearch.Thisincludesdevelopingandtestingmultispectralimaginginvisibleandnearinfraredwavelengthsofplantsinfieldtrials,usingcamerasmountedonanautonomousfieldrobotandonanUnmannedAerialVehicle(UAV).Thisequipmentispartofacommonsensorlabthathasbeenestablishedattheuniversity,whichalsoservesmanyotherresearchpurposes.

24


Basedonpilotprojectsrunduringthesummerof2016,thereareplanstoupgradethefieldresearchstationwithnewfacilitiesforhigh-throughputfieldphenotyping.Examplesofongoingresearchandsomepreliminaryresultsfromapilotexperimentcomparingtheuseofarobotanddronesinawheatyieldtrialwillbepresented.TheneedtoaccountfordirectionaleffectsandthepresenceofreproductiveorgansoncanopyreflectanceandtemperaturerecordedfromdronesF.Baret1,W.Li1,S.Madec1,A.Comar2,M.Hemmerlé2,P.Burger3

1INRA,UMREMMAH,Avignon,France2HIPHEN,Avignon,France3INRA,UMRAGIR,Toulouse,FranceAfterthefloweringstage,severalcropsgrowears(wheat),malepanicles(maize)orflowers(sunflower)atthetopofthecanopythathaveparticularstructuresandopticalproperties.Thissignificantlyimpactstheradiationregime,whichmayinducediscontinuityfromreflectanceandbiastheestimatesofcanopycharacteristics.Theobjectiveofthisstudywastoquantifytheinfluenceofthesereproductiveorgansontheestimationofselectedcanopycharacteristics.Specificattentionwaspaidtothedirectionalvariationofthiseffect.Threededicatedexperimentswereconductedin2016onwheat,maizeandsunfloweratspecifictimesafterflowering,whengreenleavesstillrepresentthemaincontributionofthegreenareaindex.Thetargetsitesweresquaresmeasuringabout10mperside.Thereproductiveorgansononehalfofeachsquarewereeliminated,whiletheotherhalfwasleftundisturbed.AhexacopterequippedwithanAIRPHENmultispectralcameraandaFLIRtau2thermalinfraredcamerawasflownseveraltimesovereachexperimenttocapturethevariationofthesignaldependingonthesun’sposition.Inthemiddleofthesquare,areferencepanelwaspositionedhorizontallytocalibratethesignalrecordedbytheAIRPHENcamera.ThebrightnesstemperatureofthereferencepanelwasalsorecordedcontinuouslyusingathermoradiometerconnectedtoadataloggertocalibratetheFLIRcamera.Foreachelevation,thedronesampledtheviewdirectionsbyflyingin4to6concentriccirclesatarangeofaltitudes.Thediameterandaltitudeofthecirclesweredefinedtokeepthesamedistancefromthereferencepanelatthecenterofthe10mx10msquareandmaintainapproximatelythesamespatialresolution.TheAIRPHENandFLIRcamerasonthegimbalalwayspointedtowardsthereferencepanel.Aftertheexperiment,thevegetationstructure(silhouettephotos,areaoforgans)andopticalpropertiesweremeasured.Resultsshowthatcanopyreflectanceandtemperaturearestronglydependentontheviewandsundirections,withamaximuminthebackscatteringdirectioncorrespondingtothehotspot.Reflectanceandbrightnesstemperatureshowsimilarpatterns,mainlyexplainedbythefractionofshadowobserved.Bycomparingtheareawherethereproductiveorganswereeliminatedwiththeundisturbedarea,wefoundsignificantvariationofcanopyreflectanceandbrightnesstemperature.Theimpactofcurrentvegetationindicesaswellasestimatesofvegetationstatevariablesusingradiativetransfermodelinversionwasassessed.

25


Precisionphenotyping:WheatdiscotofosteryieldpotentialGemmaMolero,MatthewP.Reynolds

InternationalMaizeandWheatImprovementCenter(CIMMYT),ElBatán,Texcoco,CP56130,MexicoFutureincreasesinwheatyieldpotentialwillrelylargelyonimprovedbiomassproductionboostedbyhigherradiationuseefficiency(RUE).Recentstudiesusingarecombinantdoubledhaploidwheatpopulationderivedfromthecrossbetweenacultivarwithhighgrainnumber(Bacanora)andonewithhighgrainweight(Weebil)showedspectaculargainsingrainyield(from23to31%)overtheparentalcultivarsassociatedwithhigherRUE.However,thesamepopulationstudiedinnorthwesternMexicoonlyshowedamodestyieldincrease.Atthelevelofplantgrowthanddevelopment,amoreoptimalbalancebetweensourceandsinkisexpectedtoimproveoverallRUE.Themainobjectivewastoprovideextrailluminationduringtherapidspikegrowthphase(frombootinginitiationuntilsevendaysafteranthesis)andanalyzetheeffectonsource-sinkrelatedtraits.Fieldphenotyping:QuantifyingdynamicplanttraitsacrossscalesinthefieldOnnoMuller,M.PilarCendreroMateo,HendrikAlbrecht,BeatKeller,FranciscoPinto1,AnkeSchickling,MarkMüller-Linow,RolandPieruschka,UlrichSchurr,UweRascherForschungszentrumJuelich,Germany.Email:[email protected]:CIMMYT,MexicoPhenotypinginthefieldisanessentialstepinthephenotypingchainfromwell-definedandcontrolledconditionsinthelaboratoryandgreenhousetotheheterogeneousandfluctuatingenvironmentinthefield.Fieldmeasurementsareasignificantreferencefortherelevanceoflaboratoryandgreenhouseapproachesandanimportantsourceofinformationonpotentialmechanismsandconstraintsforplantperformancetobetestedundercontrolledconditions.Herewepresentarangeofmethodsfocusingonplantarchitecture,photosynthesisandwaterrelations,thatarebeingdeployedwithintheGermanPlantPhenotypingNetwork(DPPN,www.dppn.de).Specializedfieldplatformsareestablished(a)totestinnovativephenotypingtechnologies;(b)toprovideaccesstosemi-controlledfieldinstallationtosupportbreedingapproachesforfutureCO2concentrations(breed-FACE);and(c)tostudythetranslationofphenotypicpropertiesfromcontrolledenvironmentstostandsinthefield.Herewereportthatstereoimagingenablesthequantificationofcanopystructure;activethermographyestimatesleafwatercontentandprovidesinformationontranspirationconditions;andsun-induced(SIF)andlight-inducedfluorescencetransient(LIFT)techniquesallowremoteestimatingofphotosynthesisatthecanopyandleaf-to-plantlevels,respectively.Forphotosynthesis,SIFwillbemeasuredbythenextEuropeanSpaceAgencysatelliteEarthExplorermission.Allmethodswillbefurthertestedandincorporatedin(semi-)automatedsystemsthatpositionsensorsinthefield.Apromisingportfoliowillbeintroducedtomeasureplanttraitsforfieldphenotypingandtoenhanceourunderstandingofrelevanttraitsundernaturalconditions.

26


3Dpointcloud-basedmonitoringofsoybeangrowthinearlystagesKojiNoshita1,WeiGuo1,AkitoKaga2,HiroyoshiIwata1

1TheUniversityofTokyo,Japan.Email:[email protected],JapanCurrently,high-resolutionpointclouddatacanbeacquiredeasilyandcost-effectively.Forexample,apipelineusingStructurefromMotion(SfM)andMulti-ViewStereo(MVS),whichisapromisingtechniqueforreconstructinga3Dsurfaceaspointclouddatafromaseriesof2Dimagestakenfromdifferentangles,hasbeenimplementedinseverallibrariesandsoftwareproducts.Inthisstudy,wedevelopedaworkflowformeasuringthecanopygrowthofthreesoybeancultivarstocomparethegrowthspeedofthecanopiesbasedonpointclouddataacquiredfromtheSfMandMVSpipeline.First,wetookmulti-view2Dimagesofsoybeanplantsgrowinginafieldwithadigitalcamera(EOS60D,Canon,Tokyo).Theimagesofplantsinasingleplotweretakenfromaround40differentdirections.Toscaletheimagesandcalibratecameraparameters,weusedCalibratedPhotogrammetricScaleBars(CulturalHeritageImaging).Fromthesemulti-view2Dimages,pointclouddataofsoybeancanopieswerereconstructedusingtheSfMandMVSpipeline.Second,wesegmentedthepointcloudofplantsandtheirleavesforeachplotfromthereconstructeddata.Finally,wefittedseveralmodelstothepointclouddataforestimatingphenotypicvaluesofplantorgansconstitutingthecanopyarchitecture(e.g.,leafarea,leafshape,curvature).Forexample,wereconstructed3DsurfacesofleavesfromthepointclouddatawiththepenalizedB-splinesurfacefittingbyregardingaleafasa2DclosedsurfaceembeddedintheEuclideanspaceℝ".Usingtheworkflow,wesuccessfullyacquiredhigh-resolutionpointclouddataofcanopiesofsoybeanplantsgrowinginthefield.Evenwhenleavesslightlyoverlappedeachother,asingleleafwassuccessfullydistinguishedfromthepointcloud.Whenfieldconditionswerenotdesirable(e.g.,wind,changeinlightconditions)foracquiringcorrespondingpointsamongmulti-viewimages,incompletepointclouddatawerereconstructed.Aphotogrammetricsystemthatallowsustotakemulti-viewimagessimultaneouslywillbenecessarytoobtainhigh-resolutionpointcloudsofplantsgrowinginafield.Greenhouse,fieldandrootphenotypinginfrastructureattheDepartmentofPlantandEnvironmentalSciencesoftheUniversityofCopenhagenSvendChristensen,JesperSvensgaard,DominikGrosskinsky,JesperCairoWestergaard,RenéHvidbergPetersen,SigneMarieJensen,JesperRasmussen,HanneLipczakJakobsen,ThomasRoitschUniversityofCopenhagen,DepartmentofPlantandEnvironmentalSciences.Email:Roitsch@plen.ku.dkTheDepartmentofPlantandEnvironmentalSciencesattheUniversityofCopenhagenhasestablishedabroadrangeofstate-of-the-artfacilitiesandequipmentforplantphenotyping.Specializedandexperiencedtechnicians,gardenersandresearchersensureoptimalandqualifieduseforbothinternalandexternalcollaborators.PhenoFieldanddronesareavailableforfieldphenotyping.PhenoFieldisamobile,closed,multi-spectralimagingsystemthatshutsoutwindandsunlighttoensurethehighestpossibleprecision.ImagesareacquiredwithatopcenteredmonochromaticCCDcamera2mabovegroundcoveringanareaof1mx1m,andaspatialresolutionof0.43mm/pixeland9narrowbandLEDs

27


astheactivelightsource.Threerotary-wingunmannedaerialvehicles(UAVs),twohexacoptersandoneoctocopterequippedwithtruecolor(RGB)andcolor-infrared(CIR)camerasareusedformeasuringcropcoverageandvegetationindices.Anin-housesemi-automatedprocesstohandleandanalyzeimagesofplotsinfieldexperimentsisused.Rootscanbestudiedinroottowers,tuberhizotrons,minirhizotronsandtheRadimaxfacility.Withminirhizotrons,rootscanbestudiedtoadepthof2.7munderfieldconditionsbysinglecameras,whichcanbeusedincombinationwithisotopeplacementandirrigationsystems.Thetuberhizotronscanbeusedforvisualinspectioninrootstudiesupto2mundergreenhouseoroutdoorconditions.Twelveroottowersconsistingoftwocompartmentsseparatedineast-westorientationallowvisualinspectionforrootstudiesuptoadepthof4m.TheRadiMaxfacilityconsistsof4individualpitswithanareaof400m2eachwithmoveablerainoutshelters,asophisticatedunder-watering-systemand150fixed-installedrhizotronsperpitforsemi-automatedmulti-camerasystemsallowingrootstudiesofupto150differentlinesatatimedowntoadepthof3m.Theautomated,conveyor-bandbased,high-throughputphenotypingplatformPhenoLabisinstalledinatemperaturecontrolledgreenhouseformonitoringandcaringforupto468pots.Thepotscanberandomized,rotated,measuredandweighed,andthesystemisabletomanageplantsindividually.Soilwaterpotentialismeasuredcontinuouslyineachpotandindividualirrigationisadjustedaccordingly.Italsohasmulti-reflectanceandmulti-fluorescenceimagingsystems,andthermalcamerasinsidetheimagingstationaswellasoutsideundercultivationlightconditions.Thesensor-basedimagingtechniquesarecomplementedbyvariousplatformsforadvancedphysiologicalphenotypingforphotosyntheticactivityandgasexchangemeasurementsandthedeterminationofactivitysignaturesofkeyenzymesofcarbohydrateandantioxidantmetabolism,antioxidantcapacityandphytohormoneprofiles.Morphometricsforgenomicpredictionofplantmorphologicaltraits:ItsapplicationtogeneticallydissectsorghumgrainshapeLisaSakamoto1,HiromiKanegae1,KojiNoshita1,2,MotoyukiIshimori1,HidekiTakanashi1,WaceraFiona3,WataruSakamoto3,TsuyoshiTokunaga4,NobuhiroTsutsumi1,HiroyoshiIwata11Grad.Sch.Agr.LifeSci.,Univ.Tokyo,Japan.Email:[email protected],JST,Japan3Inst.PlantSci.Res.,OkayamaUniv.,Japan4EARTHNOTECo.,Ltd.JapanRecentadvancesinimagingandremotesensingallowthecollectionoflargedatasetsofimagesforthegeneticdissectionofplantmorphologicaltraits.Inadditiontosize(length,area)andcolor,whicharecommoncharacteristicsmeasuredinimageanalysis,shapeisimportant,buthashigh-dimensionalcomplexity.Sincemorphometricmethodscanreducecomplexitywithoutseriouslossofinformation,theycanbeusefulforgeneticallydissectingplantmorphologicaltraits.Grainshapeisanimportantbreedingtargetincerealcrops.Weappliedtwomorphometricmethods—i.e.,ellipticFourieranalysis(EFA)andgeneralizedProcrustesanalysis(GPA)—togeneticallydissecttheremarkablediversityofgrainshapeinsorghum(Sorghumbicolor(L.)Moench).EFAtreatstheshapeofacontourasasumof

28


waves,anddescribesitwithellipticFourierdescriptors(EFDs).GPAsuperimposescontourstominimizetheProcrustesdistancebetweenthem,andobtainssuperimposedlandmarkpoints(SLPs).Principalcomponent(PC)analysisofEFDsandSLPsidentifiedandquantifiedshapecharacteristics,whichweredifficulttocapturebylengthsandtheirratios.InGWAS,ahighlysignificantassociationwasfoundinthesecondPCofSLPs,whilenosignificantassociationwasdetectedinlengthsandtheirratios.TheassociationdetectedinthePCofSLPswasconfirmedinabi-parentalsegregatingpopulation.Ingenomicprediction,thecontourshapeofagrainwaspredictedfromgenome-widemarkergenotypesbyusingkernelpartialleastsquares(PLS)regressionofEFDs.Thepredictedshapeshowedclosesimilaritytotheobservedshapeexceptinafewcultivars,suggestingthehighaccuracyofgenomicselectionofgrainshape.Theresultssuggesttheimportantrolemorphometricsplayinthegeneticdissectionofplantmorphologicaltraits,whosedatawillbemoreaccessibleviahigh-throughputimagingandremotesensing.Image-basedphenotypingandmachinelearningtoadvancegenome-wideassociationandpredictionanalysisinsoybeanJiaopingZhang1,HsiangSingNaik2,TeshaleAssefa1,SoumikSarkar2,R.V.ChowdaReddy1,ArtiSingh1*,BaskarGanapathysubramanian2*,AsheeshK.Singh1*1DepartmentofAgronomy,IowaStateUniversity,Ames,IA50011,USA.Email:[email protected],IowaStateUniversity,Ames,IA50011,USATraditionalvisualevaluationofcropbioticandabioticstressesistime-consumingandlabor-intensive,andlimitstheabilitytodissectthegeneticbasisofquantitativetraits.Toaddresstheseconstraints,amachinelearning(ML)-enabledimage-phenotypingpipelinewasdevelopedandappliedtogenome-widestudiesofirondeficiencychlorosis(IDC),oneoftheleadingabioticfactorsloweringsoybean(Glycinemax)yieldintheUnitedStates.Thepipelineconsistedofamulti-stageprocedure:(1)optimizedimagecaptureacrossplantcanopies;(2)canopyidentificationandregistrationfromclutteredbackgrounds;(3)accuratelyrepresentedIDCexpressionbyextractingdomainexpertinformedfeaturesfromtheprocessedimages;and(4)supervisedML-basedclassificationandseverityassessmentofcanopyimages.Thegenotypepanelconsistedof461diverseaccessions.GenotypesweregrowninafieldsuitableforIDCtesting,andimagedusinganRGBcamera.StatisticalanalysiswasdoneinRusingappropriatepackages.Genome-wideassociationstudiesidentifiedapreviouslyreportedlocus.Additionally,anovellocusharboringagenehomologinvolvedinironacquisitionfromlowbioavailabilitysourceswasidentified,indicatingthereliabilityandadvantageofML-enabledimage-phenotyping.Genomicpredictionanalysisusingsurrogatetraitsinthepredictionmodelhadhigherpredictionaccuracythanmarkeralonemodel(s),suggestingapromisingpathforfurtherincreaseofgeneticgainsviaintegratingthispipelineintobreedingprograms.Thisstudyprovidesasystematicframeworkthatenablesquickerrobustphenotypingofleafsymptomsinsoybean(andothercrops).

29


ProposalforevaluatingplantstressviasteadystatefluorescenceS.Summerer1,A.Petrozza1,V.Nuzzo2,F.Cellini11PlantPhenotypingPlatformPhen-Italy,ALSIAMetapontumAgrobios,75012,Metaponto,Italy2DipartimentodelleCultureEuropeeedelMediterraneo:Architettura,Ambiente,PatrimoniCulturali,75100Matera,Italy

ChlorophyllfluorescenceanalysisisapowerfultooltoestimatephotosystemII(PSII)performanceunderstandardorbioticorabioticstressconditions.Animportantlimitingfactorwhenmeasuringchlorophyllfluorescenceparametersinahigh-throughputphenomicssystemistheavailabilityofaflashinglightsource(usedtoblockthephotosyntheticapparatus),whichislargeandhomogeneousenoughforwholeplants.Untilrecently,suchsystemswerelimitedtosmallerplantssuchasArabidopsis.Largerphenomicsystemswere,andoftenstillare,builtwithimagingchambersthatmeasuredchlorophyllfluorescenceundersteadystateconditions.Undertheseconditions,asinglefluorescencevalueforeachimagepixelismeasuredandquantumefficienciescannotbecalculated.Inthisstudy,weinvestigatetheuseofthehuecomponentoftheHSI(hue,saturation,intensity)colorspace,whichisanalogoustothelightspectrum,fromplantchlorophyllfluorescencelightasaparametertomeasureplantstressandcompareittoparametersderivedfromtraditionalchlorophyllfluorescencekinetics.Tomatoplantsweresubjectedtoheatordroughtstress;subsequentlychlorophyllfluorescencekineticmeasurementsweretaken,aswellasimagesatsteadystatefluorescence.Byanalyzingthechlorophyllfluorescencehuechannel,wewereabletodetectdifferencesfromcontrolplantsinboththeheat-shockedanddrought-affectedplants,whilethestandardPSIIyieldmeasurementwasonlycapableofmeasuringdifferencesintheheat-shockedplants.Theseresultsappeartosuggestthattheanalysisofchlorophyllfluorescencelightinthehuechanneliscapableofidentifyingperturbationsduetoabioticstressintomatoplants.Currentlywearecontinuingtoinvestigatewhetherthismethodcanbeappliedformoreplantspeciesandabioticstresses.

30


Developmentofhigh-throughputphenotypingsoftwareforplantbreeding,geneticsandphysiologystudiesTakanariTanabata,AtsushiHayashi,SachikoIsobeKazusaDNAResearchInstitute,LaboratoryofPlantGenomicsandGenetics,Japan.Email:[email protected],reliableandhigh-throughputphenotypingistime-consumingandsometimesdifficult.Tosolvetheseproblems,wehavebeendevelopinghigh-throughputphenotypingsoftwareusingdigitalimagingandinformationtechnologies.Thisincludes(1)imageanalysissoftwaretomeasurecroporplantorgansizes;wearedevelopinganimageanalysismethodthatrecognizesoutlinesofcropandplantorgansfromdigitalimagesbasedoncolorsegmentationandmeasureshapeparameters(length,areaandsoon);wealsodevelopedthesoftwareSmartGrainforautomaticseedsizemeasurements;and(2)aniOSprogramforsupportingfieldphenotyping.Fieldphenotypingfocusesonscoringtargettraits.Wearedevelopinganapplicationforrecordingtasksviafunctionsoftappingthedisplayednumbersoritems,feedingshortnotesandshootingpictures.Forgreatestefficiency,weneedtomakecustomizedsoftwareforspecificmeasurementaims.Teamsite:http://www.kazusa.or.jp/phenotyping/NationalScienceFoundationprogramsthataddressareaswithhighimpactonfoodsecurityC.EduardoVallejos1,JamesW.Jones2

1PlantGenomeResearchProgram(PGRP),DivisionofIntegrativeOrganismalSystems,DirectorateforBiologicalSciences(BIO),NationalScienceFoundation,Arlington,VA

2InnovationsattheNexusofFood,Energy,andWaterSystems(INFEWS)Program,DivisionofChemical,Bioengineering,Environmental,andTransportSystems,DirectorateforEngineering(ENG),NationalScienceFoundation,Arlington,VA

TheNationalScienceFoundation(NSF)wasestablishedin1950andistheonlyUSfederalagencydedicatedtosupportingfundamentalresearchandeducationinallscientificandengineeringdisciplines,whicharerepresentedinitssevendirectorates.PGRPisintheDivisionofIntegrativeOrganismalSystemsintheBIODirectorate.PGRPsupportsbasicresearchineconomicallyimportantplants.Amongthefocusareasarethestudyofthestructureandfunctionofplantgenomesandtheirinteractionswithothergenomes,responsestotheenvironment,andthedevelopmentoftoolstoconnectthegenotypetothephenotype.ThetoolsandresourcesdevelopedbyPGRP-sponsoredprojectsareexpectedtotransformandmodernizeagricultureforthebettermentofsociety.SeveralPGRP-sponsoredprojectshaveactiveinternationalcollaborations.TheInnovationsattheNexusofFood,Energy,andWaterSystems(INFEWS)ProgramhastheparticipationofmostNSFDirectorates.TheDivisionofChemical,Bioengineering,Environmental,andTransportSystems(CBET)intheENGDirectorateisoneoftheprincipalcontributorstotheINFEWSinitiativealongwiththeGeologicalSciences(GEO)Directorate;theNSF-wideINFEWSWorkingGroupisco-chairedbyCBETandGEO.ThisinitiativewasdevelopedatNSFinresponsetorecognizedthreatsposedbyclimatechangeandpopulationgrowth,whichcanhaveasignificantimpactontheadequatesupplyofwater,energyandfood.INFEWSsupportsprojectsthatincreaseourunderstandingofthefood-energy-watersupplysystemthroughmodelingapproaches.

31


Projectsinthisprogramaddressthedevelopmentoftechnologicalsolutionsthatproviderobustdecisionsupportcapabilities.AnimportantcomponentofNSFprogramsistheeducation,traininganddevelopmentofhumanresources.NSFisinterestedinfacilitatingopportunitiesforscientistsintheUSwhoarefundedbyNSFprojectstocollaboratewiththeircounterpartsinothercountries.PlanthealthmonitoringusingmultispectralimagingandvolatileanalysisforspaceandterrestrialapplicationsAaronI.Velez-Ramirez1,JokeBelza1,JoeriVercammen3,AlexVanDenBossche2,DominiqueVanDerStraeten1

1LaboratoryofFunctionalPlantBiology,FacultyofSciences.GhentUniversity,[email protected]

2DepartmentofElectricalEnergy,SystemsandAutomation,FacultyofEngineeringandArchitecture,GhentUniversity,Belgium

3Interscience,BelgiumPlantphenotypingtechnologyhasthepotentialtoadvancebasicscientificknowledgeandsaveresourcesincropproductionnotonlyonplanetEarth,butalsoontheMoonandMars.Interestingly,developingplantphenotypingtechnologyforsuchseeminglydistantspaceapplicationshasthepotentialtoimproveterrestrialtechnologyevenbeforeanyequipmentisshippedinarocket.Ifhumansaretoexploredeepspace,foodhastobeproducedonsiteusingminimalresources.Currently,however,ourbasicunderstandingofhowplantsandcropswillbehaveunderagravitationalpulldifferentfromtheterrestrialoneisstillverylimited.TheonlywaytogrowplantsundersimulatedLunarorMartiangravityisonboardtheInternationalSpaceStation(ISS)usingwhatisknownastheEuropeanModularCultivationSystem(EMCS).Howeveruseful,theEMCSnowneedsupdatesandimprovements,includingitsplantphenotypingcapabilities.WithintheTimeScaleproject,wearedevelopingaplanthealthmonitoringsystemconsistingofmultispectralimagingandvolatilemonitoringsystems.Theimagingsystemisabletoperformchlorophyllfluorescencekineticsandmonitorvisible,near-infrared(NIR)andlong-waveinfrared(LWIR)spectra.ThevolatileanalysissystemisbasedonSelectedIonFlowTubeMassSpectrometry(SIFT-MS),whichisabletodetectandquantifyinrealtimecomplexmixturesofbiogenicvolatileorganiccompounds(BVOCs).Theuseofthesesystemsshouldallowtheidentificationofnovelstressmarkersformonitoringcrops.Additionally,thelessonslearnedfromthedesign,constructionanduseofthisterrestrialsystemwillguidetheminiaturizationandsimplificationofthetechnologysoitwillfitintoaplantmonitoringsystemprototypetoupdatetheEMCS’plantphenotypingcapacityonboardtheISS.

32


High-throughputfield-basedphenotypinginbreedingwithUAVplatformsGuijunYang,ChunjiangZhao,JiangangLiu,HaiyangYu,XiaoqingZhao,BoXuNERCITA,China.Emailyanggj@nercita.org.cnAsfieldmeasurementofmassivegermplasmforcomplextraitsinbreedingischallenging,thereisastrongdemandforreal-time,rapidandnon-destructivephenotypingtoacceleratebreedingefficiency.Remotesensingusingunmannedaerialplatformscanbeappliedtorapidlyandcost‑effectivelyphenotypelargenumbersofplotsandfieldtrials.Inrecentyears,strategiesforhigh-throughputfield-basedphenotypinginbreedingwereinvestigatedbytheNationalEngineeringResearchCenterforInformationTechnologyinAgriculture(coordinatedbyNERCITA),whereproximalremotesensingisperformedbydeployingsensorsusingaerialplatforms.Strategiesincludethefollowing:(1)selectionandspecificationofUAVplatformsforapplyingphenotypinginbreeding;(2)rapidprocessingofmulti-sourceremotesensingdataforhigh-throughputphenotyping;(3)analysisofphenotypicinformationonsoybean,maizeandwheatinbreeding(over10,000plots)byproximalremotesensingatdifferentgrowthperiods;(4)determiningtheoptimalgrowthstage,indicesandalgorithmmodelforcropyieldprediction;(5)validationofthephenotypicinformationresolutionandyieldpredictionusingagriculturalUAVforbreedingplotstoascertainitsstabilityandaccuracy;and(6)genome-wideassociationstudyofmorphologicalindicatorsandmaizegenotypes,andidentificationofcandidategenes.CapabilitiesoftheFieldPhenotypingPlatformFIPdemonstratedbycharacterizationofthetimeseriesofcanopycoverinwheatasrelatedtoGxEinteractionsKangYu,NorbertKirchgessner,FrankLiebisch,AchimWalter,AndreasHundInstituteofAgriculturalSciences,ETHZurich,Switzerland.Email:kang.yu@usys.ethz.chAdvancesinplantphenotypingtechnologiesenableustomeasureplanttraitsandgenotype-by-environment(G×E)interactionsnon-destructivelyathightemporalresolution,whichthusallowsforcontinuouslyquantifyingtraitsanddissectingG×Einteractions.However,underuncontrolledenvironmentalconditions,itishardtoseparateeffectsofmultipleenvironmentalfactorssuchasmultiplebioticstresses.Inthiscase,significantbiasislikelyintroducedwheninterpretingthecause-effectrelationshipsbetweenphenotypic/complextraitsandenvironments.Here,weproposeamodelforcharacterizingthetrait“timeseriesofcanopycover”asaresponsetomultiplefactorsundernaturalenvironments,whichsuggestspromisingpotentialinpredictingG×Einteractionsforfutureclimatechangescenarios,takingadvantageoftheETHFieldPhenotypingPlatform(FIP),a2013-2016fieldexperimentcomprisingmorethan300wheatvarietiesattheETHLindau-Eschikonexperimentalstation,Switzerland.Anintegrated,multi-sensorplatform,FIPallowsautomatedmeasurementofmultipletraits,includingcanopycover(CC),temperature,heightandmultispectralproperties.Toevaluatethemodel,CCwasmeasuredevery3-4daysthroughouttwogrowingseasonsandatime-seriesofCC-basedgrowthratewascalculatedastheresponsetoseasonallyfluctuatingenvironments.Theresponsecouldbevisualizedasaplaneconstructedin3Dbytwoenvironmentalfactors,forinstance,thegrowingdegreedays(GDD)andvaporpressuredeficit(VPD),againstwhichtheCC-basedgrowthratewasplotted.Theresponsewasquantifiedasthreevariables:theslopesonGDDandVPDandtheintercept

33


onthegrowthrateaxes.Linearmixedmodelswereemployedforthebestlinearunbiasedpredictions(BLUPs)andresultsshowedthatheritabilitywas0.75,0.40and0.75intermsofthethreevariables,respectively.ResultsconfirmedthatGDDandVPDhavealargeinfluenceoncropgrowthratesinnaturalenvironments.Additionally,thismodelprovidesaplausiblewaytoweighttheeffectsofdifferentenvironmentalfactorsoncropgrowthandthusinterpretG×Einteractions.Preliminaryresultsalsosuggestthatthismulti-dimensionalmodelimprovespredictionofG×Eeffects,whichwillbefurthervalidatedinmultipleyearswiththeFIPaswellascollaborativelyatmultiplesites.Next-generationmaizefieldphenotypingapproachesusinginnovativesensingtechniquesforimprovedbreedingefficiencyMainassaraZaman-Allah1,JillCairns1,CosmosMagorokosho1,AmsalTarekegne1,MikeOlsen2,BoddupalliM.Prasanna21CIMMYTGlobalMaizeProgram,P.O.BoxMP163,Harare,Zimbabwe.Email:[email protected]

2CIMMYTGlobalMaizeProgram,P.O.Box1041,Nairobi15,KenyaGoodphenotypingisoneofthemostcriticalaspectsofasuccessfulbreedingprogram.Phenotypingmethods/platformsarethereforeexpectedtoprovidetheconceptsandtoolsrequiredtoincreaseefficiencyinbreedingforcropimprovement.Unfortunately,phenotypingmethods/toolsforabioticstresseshaverarelybeenimplementedbybreedingprograms.Duetothelimitationsofmanuallyperformedphenotypingmeasurements,recentadvancesinimagingandaerialtechnologiesareexpectedtoenablebetterintegrationofphenotypingapproachesintobreedingprogramsbyhelpingtoextractmorevaluefromeveryresearchplotandimprovetheprecisionandreliabilityofphenotypicdata.Wepresenttheuseofsensortechnologiesthatprovideopportunitiestoadvancetowardnext-generationphenotyping,whichismorecompatiblewithmaizebreeders’needsandwillsignificantlyminimizeselectioncostswhilemaximizingselectionefficiencyandacceleratingtheprocesstodeliverbettergeneticstofarmers.

34


PhenotypingleaftraitsinwheatgenotypesbasedonhyperspectraldataMarekZivcak1,MarianBrestic1,KatarinaOlsovska1,MarekKovar1,ViliamBarek1,PavolHauptvogel2,XinghongYang3

1SlovakUniversityofAgriculture,Nitra,Slovakia2NationalAgriculturalandFoodCentre,ResearchInstituteofPlantProduction,Piestany,Slovakia3CollegeofLifeSciences,ShandongAgriculturalUniversity,Taian,ChinaHyperspectralanalysishasbeenintroducedasanalternativetechnologytocharacterizethedifferentpropertiesofcropcanopies,includingapplicationsinfieldphenotypingofgeneticresources.Wheatgermplasmischaracterizedbyhavingbroadphenotypicaldiversity,includingleaftraits.Inthisrespect,theopenquestionishowreliablearehyperspectralindicesforestimatingleafpropertieswhenappliedtoabroadspectraofgenotypesdifferinginplantandleafmorphology,anatomyandchemicalcompositionofleaves.Toanswerthisquestion,hyperspectralfieldrecordsand,subsequently,leafanalyseswereconductedonmorethan100wheatgenotypesfromtheSlovakNationalGenebankcollection.Thetraitsoffullydevelopedflagleaves(chlorophyllandcarotenoidcontentperleafareaandperdrymassunit,chlorophyllatobratio,chlorophylltocarotenoidratio,andleafthicknessmeasuredasspecificleafweight,leafarea,SPADvalue,etc.)werecorrelatedwith132hyperspectralindicesdevelopedtoestimatedifferentpropertiesofabovegroundcropbiomass.Theselectedgenotypesprovidedrelativelyhighdiversityinallobservedtraits(thickvs.thinleaves,highvs.lowchlorophyllconcentration,verysmallvs.verylargeleaves),providinggoodbackgroundforcorrelationanalyses.Theresultsindicatedthatnumerousparametersdesignedforestimatingspecificleaftraits(e.g.,chlorophyllcontent,canopystructure)showedpoorcorrelationwithmeasureddata.Wealsoidentifiedtheparametersthathadrelativelygoodcorrelationacrosstheentirecollectionofwheatgenotypes,whichcanberegardedasmorereliableanduniversal,andusefulforphenotypingwheatgeneticresources.Thestudyrepresentsoneoftheinitialstepsoftheprogramaimedatphenotypingwheatgermplasm,andatdevelopingmethodologicalapproachestoassessthegenotypes,includingtraitsrelatedtoadaptability,plasticityandtolerancetoabioticstressfactors.ThestudywassupportedbynationalgrantsAPVV-15-0721andAPW-15-0562andbilateralprojectwithChinaSK-CN-2015-0005.

35

Posters:Addingvaluetophenotypicdata

Postersession:Addingvaluetophenotypicdata

Findinganeedleinahaystack–UsingZegamitovisualizephenotypicdatasetsBettinaBerger1,2,GeorgeSainsbury1,2,TrevorGarnett1,2,RogerNoble3

1ThePlantAccelerator,AustralianPlantPhenomicsFacility2SchoolofAgriculture,Food&Wine,UniversityofAdelaide,WaiteCampus,UrrbareSA5064,Australia3ZegamiLimited,Oxford,UKHigh-throughputphenotypingfacilities,suchastheAustralianPlantPhenomicsFacility,generatelargephenotypicdatasets,withthousandsofimages.Thesedatasetsareoftentoolargeandoverwhelmingforplantscientiststoworkwithandmakesenseof.Asaconsequence,dataanalysistakesalotlongerthantheactualexperimentandhasbecomeanewbottleneck.Toreconnectplantscientistswiththeirdata,ThePlantAcceleratorandZegamiLtd(UK)collaboratedtocustomizeZegamisoftwaretothespecificneedsofresearchersinplantphenomics.Zegamiisaneasy-to-useinteractivetoolthatallowsuserstobrowsetheirimagecollections,creategraphsandplotsontheflyandextractsubsetsofimages.Userscanusethefunctionalitiestodetectoutliers,findpatternswithinthedataandexplorenovelphenotypictraits.Importantly,plantscientistscancomeupwithnewresearchquestionstobefurtherexploredincollaborationwithstatisticians.AlldatasetsgeneratedatThePlantAcceleratorwillbeuploadedtoZegamiforprivateuseraccess,inthefirstinstance,andpublicaccess,oncedatasetshavebeenpublished.(https://zegami.plantphenomics.org.au/)Takingthenexthurdle:ManagementofphenotypicdatawithPIPPAStijnDhondt1,2,RaphaelAbbeloos1,2,NathalieWuyts1,2,DirkInzé1,21DepartmentofPlantSystemsBiology,VIB,Technologiepark927,9052Gent,Belgium

2DepartmentofPlantBiotechnologyandBioinformatics,GhentUniversity,Technologiepark927,9052Gent,Belgium,email:[email protected]

Whiledigitalphenotypingplaysacentralroleinmanyplantresearchprojects,phenotypicdataarebeingproducedathighspeedandinhighquantity.High-throughputphenotypingplatformscontinuouslygenerateplantimageswithseveralmodalities.Nowadays,thesamesystemcanacquireRGB,thermalinfrared,fluorescenceandhyperspectralimagesandstoreenvironmental,weighingandirrigationdata.Thenexthurdleistobeabletoproperlymanagethehighamountsofrawandderiveddata.AtVIB,wedevelopedPIPPAasacentraldatabaseandwebinterfacewithimageanddatavisualizationandanalysisfunctions.SeveralautomatedWIWAMphenotypingplatforms,rangingfromanXYtableforcontrolledirrigationandimagingofArabidopsistoaconveyorbeltsystemforfulllife-cycleanalysisofmaizeplants,havebeenintegratedintoPIPPA.Theinterfaceallowsscientiststosetupandanalyzetheirownexperiments,whilekeepingalldatatogetherinastructureddatabasethattakescareofdatamanagementandintegration,linkingimages,metadata,environmentaldata,andimageanalysisand

36


measurementresults.Asthesoftwarepackagewasdevelopedasawebinterface,thetoolisavailableoneverycomputerwithinthedepartment.Pre-processingofimages,suchascropping,canbeautomatedandimageanalysisisperformedbystartingataskontheserverorcomputerclusterforfastprocessing.Theanalysisframeworkisdesignedtosupporttheintegrationofexternalimageanalysisscripts.Furthermore,environmentalmeasurements,weighingandirrigationoutput,theexperimentaldesign,andimageanalysisresultscanallbegraphicallyvisualizedwithinPIPPA,bringingtheplantphenotypingresultstoyourfingertips.Currentandfuturedevelopmentsfocusontheinteroperabilityofimageprocessingtoolsandpublicaccessibilityofrawphenotypicdatatoenablecommunity-based'bigdata'analysisinitiatives.OPENSIMROOT:ComputationalfunctionalplantmodelingandsimulationChristianKuppe,JohannesA.Postma

InstituteofBio-andGeosciences,IBG-2:PlantSciences,ForschungszentrumJülich,Germany.Email:[email protected]

WeintroduceOPENSIMROOT,athree-dimensionalfunctionalstructuralplantmodelanduseittointegratedifferentroottraitsintoawholeplantrootarchitecture,andcrop“rootopy”.Wealsoshowthatwecanstudytheimportanceofatraitandintegratedphenotypesundermultipleresourceconstraintswithourrecentlyopen-sourcedsoftwareOPENSIMROOT.Thephenotypingrevolutioninplantsciencesprovideseverincreasingamountsofdatawhichneedtobeinterpretednotonlyinageneticcontextbutalsoinafunctionalcontext.Functionalunderstandingreflectsthestrategyforadaptingtodifferentenvironments.Characterizationofsoil-rootinteractioninafunctionalmodelisonesteptowardsunderstandinghowdifferenttraitsworktogethertoimproveplantgrowthundernaturaloragriculturalconditions.WithmodelingtoolslikeOPENSIMROOT,wehelpsupportthedecisioncycleofplantperformanceanalysiswheredrivingprocessesareidentified.Thatcanhelpdetermineonwhichuncertaininputparametersweshouldspendresourcestogainthebiggestreductioninuncertainty,byclosingtheloopofexperimentsandmodeling.Experimentalandsimulationstudiesshow,forexample,thatsteeperseminalandcrownrootsimprovenitratecaptureincoarsesoilandthatrootproliferationincreasesuptakeoflocallyplacedphosphorusfertilizer.Synergismbetweenlongroothairsandshallowrooting(andothereffects)canbeverifiedbyfunctionalmodeling.Thisisachallengingtaskandweneedtointegratephenotypicinformation,differentenvironmentalfactorsandtheplasticityoftheplanttothosefactors.Modelsmayaidinestablishingdoseresponsecurves,andstudyingtraitorGxEinteractions.

37


Determinationofdifferentialpathogensensitivityofbarleycultivarsbymulti-reflectanceand-fluorescenceimagingincombinationwithdeepphysiologicalphenotypingDominikGrosskinsky,JesperSvensgaard,SvendChristensen,ThomasRoitschUniversityofCopenhagen,DepartmentofPlantandEnvironmentalSciences.Email:Roitsch@plen.ku.dkGreatadvanceshavebeenmadeincost-efficientandhigh-throughputanalysisofgeneticinformationandnon-invasivephenotyping,butlarge-scaleanalysisoftheunderlyingphysiologicalmechanismsislagging.Phenotypeisdeterminedbythesumofthecomplexinteractionsbetweenmetabolicpathwaysandintracellularregulatorynetworksreflectedinaninternal,physiologicalandbiochemicalphenotype.Thesevariousscalesofdynamicphysiologicalresponsesneedtobeconsidered,andgenotypingandnon-invasivephenotypingmustbelinkedtophysiologyatthecellularandtissuelevel.Thushigh-dimensionalphysiologicalphenotypingacrossscalesisneededthatintegratestheprecisecharacterizationoftheinternalphenotypeintohigh-throughputphenotypingofwholeplantsandcanopies.Thuscomplextraitscanbebrokendownintoindividualcomponentsofphysiologicaltraits.Sincethehigherresolutionofphysiologicalphenotypingbywetchemistryisinherentlylimitedinthroughput,high-throughputnon-invasivephenotypingneedstobevalidatedandverifiedacrossscalestobeusedasaproxyforunderlyingprocesses.Asacasestudy,weaddressedtheinfectionoffourbarleycultivarsbytwofungalpathogenswithdistinctlydifferentlifestyles,theobligatebiotrophBlumeriagraminisorandthenecrotrophDrechslerateres,usingamultidimensional,holisticphenotypingapproach.Non-invasivephenotypingbymultispectralandfluorescenceimaginginthenovelhigh-throughputphenotypingfacilityPhenoLabwascomplementedbymetabolicfingerprintingviadeterminationofactivitysignaturesofkeyenzymesofcarbohydrateandantioxidativemetabolism,phytohormoneprofilesanddeterminationofspecificdefensemarkergenes.Thedifferentialsensitivityofthetestedbarleygenotypestobothpathogenswasdetectedbysensor-basedimagingtechniquesandfurthervalidatedandverifiedbydistinctphysiologicalresponsesanddefensereactions.Thisstudyprovesthatinsuchaninterdisciplinaryandmulti-dimensionalphenomicsapproach,plantphysiology,non-invasivephenotypingandfunctionalgenomicswillcomplementeachother,ultimatelyenablingthein-silicoassessmentofresponsesindefinedenvironmentswithadvancedcropphysiologymodels.Thiswillallowthegenerationofrobustphysiologicalpredictorsforcomplextraitstobridgetheknowledgegapbetweengenotypesandphenotypesforapplicationsinbreeding,precisionfarmingandbasicresearch.

38


MaizePhenomap1andPhenomap2datasets:IntegrationwithgenomestofieldsZhikaiLiang,1SrinidhiBashyam,2BhushitAgarwal,2GengBai,3SrutiDasChaudhury,2OscarRodriguez,1YumouQiu,4YufengGe,3AshokSamal,2JamesC.Schnable11DepartmentofAgronomyandHorticulture,UniversityofNebraska-Lincoln,USA2DepartmentofComputerScienceandEngineering,UniversityofNebraska-Lincoln,USA3DepartmentofBiologicalSystemsEngineering,UniversityofNebraska-Lincoln,USA4DepartmentofStatistics,UniversityofNebraska-Lincoln,USAThroughoutmostofthehistoryofplantgenetics,whichphenotypesweremeasureddependedlargelyonwhatcouldbeeasilyscored.Forexample,8ofthe10mostwidelycitedmaizegenesproduceobservable,segregatingphenotypesatthekernelstage.Inprinciple,bothhyperspectralcamerasandcomputervisionalgorithmsmakeitpossibletoquantifyhundredsifnotthousandsofindividualplantphenotypes,aswellassecond-orderandderivedphenotypes(suchasratiosofdifferentmeasurements,ortherateofchangeofagivenmeasurementovertime).However,itisnotcurrentlyknownwhichphenotypes,ifany,measuredinacontrolledenvironmenthavevalueforpredictinghowmaizeandotherpanicoidcropplantswillperformunderfieldconditions.TwodatasetsweregeneratedusingtheautomatedgreenhousephenotypingfacilityattheUniversityofNebraska-Lincoln.Thefirst,MaizePhenoMap1,included31maizeinbredsimagedfromgerminationtolatevegetativedevelopment(39DAP).Thesesameinbredsweregrownandphenotypedunderconventionalagronomicconditionsat19locationsacrosstheUSandCanadain2014and24locationsin2015.Thesecond,MaizePhenoMap2,included140maizehybridsgeneratedusingrecentlyoff-patentinbredparentsgeneratedbymajorseedcompanies,makingtheselinesasclosetothematerialgrownbyNorthAmericanfarmersaslegallypossible.Plantswereagainimagedattheautomatedphenotypinggreenhouse,toaheightofapproximately2.5metersover63days,enablingtheobservationoftheonsetofreproductivedevelopment,aswellasunderfieldconditionsnearMead,NE.Thesedatasetsmakeitpossibletodevelopandtestnewcomputervision-basedalgorithmsforextractingnovelphenotypesfromRGBorhyperspectralimagedataonplants,andtorapidlyassesstheheritabilityofnewlydefinedphenotypes,sothattraitsnotundersignificantlevelsofgeneticcontrolcanbeefficientlytriaged.Futureeffortswillfocusoncomponent-basedphenotypingofreproductivestructures(earsandtassels)aswellasextendingthephenomapconceptfrommaizetosorghum.

39


Low-cost3Dimagingsystemsforhigh-throughputfieldphenotypingThangCao,KarimPanjvani,AnhDinh,KhanWahidUniversityofSaskatchewan,Canada,email:anh.dinh@usask.caGiventhehighdemandtosupportandacceleratebreedingfornoveltraits,thecropresearchcommunityfacestheneedtoaccuratelymeasureincreasinglylargenumbersofplantsandplantparameters.Avarietyofimagingmethodologiesisbeingusedtogatherdataforquantitativestudiesofcomplextraits.Thesetechniques(suchasvisibleimaging,imagingspectroscopy,thermalinfraredimagingandfluorescenceimaging)providequantitativemorphologicalmeasurements.Whenappliedtoalargenumberofplants,however,complete3Dimageacquisitionistimeconsumingforhigh-throughputphenotypingwithahugeamountofdata.Insomecontexts,itmaynotbenecessarytofullyrebuildentireplants.Low-costdepthimagingsystemscanbeusefultoproduceasmalleramountofdataperplant.Weproposeusingalow-costdepthcameracalledTime-of-Flight(ToF)tohavevideosandpicturesoftheplantin3D.Asoftwareprogramwaswrittentodisplaytheplantin3Dandestimatecertaintraits.WealsocomparedtheuseofaMicrosoftKinectV2camera,adigitalcameraandtheToFAgrosP100cameratoprovide3Dimagesofwheatandcanola.TheKinectFusionsystemreconstructsasingledensesurfacemodelwithsmoothsurfacesbyintegratingthedepthdatafrommultipleviewpoints.TheKinectV2emitshigh-frequencymodulatedlightandmeasuresthephaseshiftofthereturnsignaltoprovidea3Ddepthimage.However,itsambientlightrejectionisnotstrongenoughforoutdooruseduetothewidedivergenceofilluminationenergybothhorizontallyandvertically.Forthedigitalcamera,2Dimagesweretakenand3Dmodelsoftware(Agisoftphotoscan)wasusedtoconstructthe3Dimages.Althoughthisapproachcanbeusefulinagreenhouse,itisalsohighlyaffectedbytheilluminationlevel.Alow-costdepthcamerawithToFtechnology,Agros3DP100,wasusedinthecomparison.Thissmart3Dcamerasimultaneouslydeliversdepthinformationandgrayvalueimagedataforeachpixel.ByusingactiveIRillumination,thesensorcaptures3Dand2Dinformationataresolutionof160x120pixelswithupto160framespersecondindependentlyofambientlight.Basedonthesecharacteristics,itissuitableforuseinagreenhousesettingorinthefield.Usingthe3Dimagesfromthislow-costToFcamera,theheightofthewheatandcanolawasestimatedusingthedevelopedalgorithm;thenumberofcanolabrancheswasalsocorrectlycounted.Ofthethreecameras,theToFseemssuitableforhigh-throughputphenotypinginthefield.Thedrawbackofthiscameraisitsresolution,whichwasnotsufficienttocountthenumberofseedsincanolaorwheatinourexperiments.Acombinationof2Dand3DimagesisbeinginvestigatedtoimprovetheresolutionofthecurrentToFcamerainordertohaveabetterbiomassestimationandidentifyotherphenotypiccharacteristics.

40

Posters:Phenotypingforcropimprovement

Wednesday14thDecember(Morning)PHENOPTYPINGFORCROPIMPROVEMENT

Phenotypingforroot-basedgainsincropproductivityMichelleWatt,UlrichSchurrPlantSciences,InstituteofBio-andGeosciences,ForschungszentrumJuelich,Germany.Email:m.watt@fz-juelich.deTheamazingproductionincreasesofthe20thcenturywereachievedwithsoilandsoilinputmanagement(e.g.,phosphorusfertilizer,limeforsoilacidity,rhizobiawithlegumes,nitrogenfertilizer,rotationstocontrolrootdiseases,irrigation,andplowingmanagement).Meanwhile,newvarietieshavebeenselectedbasedonshootphenotypeslargelybyeyeinthefield(e.g.,canopyarchitecture,height,diseaseresistanceandabioticstresstolerancebasedonleaves,andfloweringtime),combinedwithgrainweightandqualitydestructivemeasures.Rootsarenotgenerallytargetedbymanagementorbreedingdespitetheirpotentialtoincreasewaterandfertilizeruptake,harvestindices,andadaptationstolandconservationandsoilamendments,anddespitethefactthatthe21stcenturypopulationwillseereductionsinland,waterandfertilizersavailableforfoodproduction.Wewillpresentnotableexamplesofwhererootphenotypesweredirectlyselectedduringpre-breedingactivitiesandtransferredtobreedingprogramsanddiscusshowphenotypingtechnologiesallowholisticselectionofrootandshoottraitsallowsproductstobedeliveredmorequicklyanddirectlytofarmers.Multi-modalityremotesensinganddataanalysisforhighthroughputphenotypingM.CrawfordPurdueUniversity.Email:[email protected],coupledwithchallengesofclimatechangerequiredevelopmentoftechnologiestosupportincreasedfoodproductionthroughouttheentiresupplychain–fromplantbreedingtodeliveryofagriculturalproducts.Developmentsinremotesensingfromspace-based,airborne,andproximalsensingplatforms,coupledwithadvancesincomputationalcapabilityanddataanalytics,areprovidingnewopportunitiesforcontributingsolutionstoaddressgrandchallengesrelatedtofood,energy,andwater.Space-borneplatformscarryingnewactiveandpassivesensorsaremovingfromcomplex,multi-purposemissionstolowercost,measurementspecificconstellationsofsmallsatellites.Advancesinmaterialsareleadingtominiaturizationandmassproductionofsensorsandsupportinginstrumentation,resultinginadvancedsensingfromaffordableautonomousvehicles.Newalgorithmstoexploitthemassive,multi-modalitydatasetsandprovideactionableinformationforagriculturalapplicationsfromphenotypingtocropmappingandmonitoringarebeingdeveloped.Anoverviewofrecentadvancesinhighresolutionmultiplemodalityremotesensing,aswellopportunitiesandchallengesfordatascienceinanalysisofmulti-temporal,multi-scaleremotely-senseddatafocusedonhigh-throughputphenotypingwillbepresented.

41

PHENOTYPINGFORCROPIMPROVEMENT

PhenotypingatBayerHyperCarefarmsElisaLiras,GretaDeBoth,StephanieThepot,RandallHess,WalidElfeki,RaphaelDumainBayer,CropSciencedivisionTheCropEfficiencyresearchprogramsofBayerCropSciencearedesignedtoenhancecropproductivitybypreservingandmaximizingyield,withtheprimaryfocusonwheat,butalsopursuingprojectsforsoybean,cornandcanola.Theseprogramsareusingvarioustechnologiestomodulateplantmetabolismorimproveplantnutrition.Throughunderstandingyieldformationinwheat,researchersworkcross-functionallytodeliverintegratedsolutionstofarmerswithyieldincreaseandabioticstresstoleranceasprimetargets.SeveralenablinginnovativetechnologiesaresupportingthedifferentR&Dprograms,andoneofthemistheHyperCareFarmconcept,focusingonin-fieldprecisionphenotypingactivities.TheHyperCareFarmsarefieldstationsthathavebeenupgradedintermsofprecisionphenotypingequipment.Themainpurposeistomeasure,invariousR&Dtrials,inaprecise,automatedandnon-invasivemanner,plantparameters(e.g.spectralreflectance,canopytemperature)asproxiesfordifferentphenotypicaltraits(e.g.biomassandwaterstress)andtoevaluatetheutilityofnewsensorsunderfieldoperatingconditions.SeveralHyperCareFarmshavebeenestablishedaroundtheworld.ExamplesofthehighprecisioncapabilitiesavailableintheHyperCareFarmsarethePhenoTracker,avehicleconceivedasamobilelabequippedwithhighresolutioncameras,scannersandreflectancesensorsandthePhenoTower,acamerasystem,measuringcanopytemperatureofthefieldplots.Viainterpretationofalltheseprecisionphenotypeddata,researcherscandrawconclusionsconcerningtheeffectsoftheappliedtechnologiesoncropdevelopmentandperformance.Reducinglodginginirrigatedwheat:FocusonstemandrootcharacteristicsandHTPmethodsM.FernandaDreccer1,TonyCondon1,M.GabrielaBorgognone2,GregRebetzke1,LynneMcIntyre1

1CSIROAgricultureandFood,Australia2DepartmentofAgricultureandFisheries,Toowoomba,QLD4350,AustraliaInAustralia,wheatbreedingeffortshavefocusedlargelyonimprovingcropyieldsfordrylandenvironments.Butwheatgermplasmwithhighyieldpotentialandreducedlodging,well-suitedtoirrigatedsystemsandhighrainfallenvironments/yearswherelodginglikelihoodcanbehigh,isalsoneeded.Thisstudywasdesignedtoevaluatetraitsandphenotypingmethodsforimprovedlodgingtolerance.Themainobjectiveswereto:(1)evaluatethevariationandrobustnessoftraitsproposedtoimprovelodgingtolerance,(2)establishtherelationshipsoftraitswithlodgingexpressionandwithyield,and(3)developahigh-throughputphenotypingmethodforrootanchoragecharacteristics.Upto50breadwheatgenotypes,identifiedashigh-yieldingandcontrastingforlodginginmulti-environmenttrials,weregrownunderirrigationatGatton,NEregionoftheAustralianwheatbelt,duringtwoconsecutiveyears.Characteristicsofstems(lowerinternodemaximumbreakingforceandlength)andofroots(crownrootplate-spreadanddepth)displayedhighandrepeatablevariation.Lineswithgreaterlodginghadweakerandthinnerstemsandnarrowerrootplates.Highyieldswereassociatedwithgreaterspecificstemweight,strongerstemsandwideranddeepercrownroots.Thecontributionofrootcharacteristicstoanchoragewasfurtherstudiedinthefield,viarootlengthevaluationinthetop

42


50cm,andinanexperimentundercontrolledconditions.Thelatterwasset-upinanefforttodevelopahigh-throughputphenotypingmethodfornodalrootstosupportgermplasmscreeningandmolecularmarkerdevelopment.Resultsarediscussedinrelationtofieldobservations.LeasyScanre-loaded:3DscanningplusseamlessmonitoringofcropcanopyandwateruseVincentVadez,JanaKholovaICRISAT–CropPhysiologyLaboratory,GreaterHyderabad,Patancheru502324,Telangana,India.Email:[email protected]

Inthispresentation,anupdateisgivenontheuseandupgradeoftheLeasyScanplatform,installedatICRISATin2014.LeasyScanisbasedonanovel3Dscanningtechniquetocaptureleafareadevelopmentcontinuously,andascanner-to-plantconcepttoincreaseimagingthroughput.Theinitialideaforitsdevelopmentwastoassesscanopytraitsaffectingwateruse(leafareaindex,rateofdevelopment),whichcanrevealcropfitnesstospecificwaterstressscenarios.Wedescribehowthetechnologyfunctions,howdataarevisualizedviaaweb-basedinterfaceandshowvalidationdataonthecloserelationshipsbetweenscannedandobservedleafareadataofindividualplantsofdifferentcrops(R2between0.86and0.94),orofscannedandobservedareaofplantscultivatedatdensitiesreflectingfieldconditions(R2between0.80and0.96).Examplesofthefirstapplicationsoftheplatformarepresented:(1)tocomparetheleafareadevelopmentpatternofpearlmilletbreedingmaterialtargetedtodifferentagro-ecologicalzones;(2)forthemappingofQTLsforvigor-relatedtraitsinchickpea,showntoco-mapwitha“droughttolerance”QTLreportedearlier;and(3)forthemappingofleafareadevelopmentinpearlmillet.Recently,thecapacitytomeasuretranspirationseamlesslyandatahighratewasaddedtotheplatformwithasetof1,488analyticalscales.Examplesofthemonitoringofplanttranspirationbytheanalyticalscalesarepresentedaspartoftheassessmentofthetranspirationresponsetohighvaporpressuredeficit(VPD)insorghumandpearlmillet.Thenewlarge-scalesetupusinglargetraysreflectingfieldconditionsispresented.Thisnewplatformhasthepotentialtophenotypeatahighrateandwithprecisionfortraitscontrollingplantwateruse,whichareofcriticalimportancefordroughtadaptationandprovidetheopportunitytoharnesstheirgeneticsforbreedingimprovedvarieties.

43


Extendingthephenotype:IntegrationoffieldandglasshousephenotypingwithcropmodelingScottC.Chapman1,GraemeL.Hammer2,AndriesPotgieter2,DavidJordan2,BangyouZheng1,TaoDuan1,RobertFurbank3,XavierSirault2,JoseJimenez-Berni21CSIRO,AUSTRALIA2TheUniversityofQueensland,Brisbane,Australia3ANU,Canberra,AustraliaCropmodelsapproximateplantprocessesinordertocomputephenotypesofinterest.Someofthesephenotypes(forexample,seasonalwateruse)aredifficulttoobservedirectlyovertheseason.However,proximalsensingofcropcanopyandrootcharacteristicsandtheirresponsetotheenvironmentstartstoprovideinformationto'correct'themodelandallowprediction,oratleastgenotyperanking,forthese'unobservablephenotypes'.Remotedatacapturefromfixed,groundandaerialsensorsprovidesinformationthatcanbeextendedbymodels.Differentlevelsofmodelsmaybedevelopedforshort/longtimeframesandforincorporatinggreaterorlessermechanisticdetail.Akeypointtoconsideristhatmodelsandtheinformingofthemcancomefrommultipletypesofexperiments,includingplantphenotypinginglasshouses,andstillbeusedtocontributeto‘disentangling’thephenotypesofeconomicinterest.PhenotypingandGWASforriceimprovement:Astrategyandpartialresultstowardsmulti-traitideotypeconstructionbygenomeeditingMichaelDingkuhn1,2,ArvindKumar2,TobiasKretzschmar2,ChristophePerin1,UttamKumar2,MariaCamilaRebolledo4,HeiLeung2,JulieMaeCristePasuquin2,PaulQuick2,AnindyaBandyopadhyay3,DelphineLuquet11Cirad,France2IRRI,Philippines3IRRI,India4CIAT,ColombiaTheGRiSPGlobalRicePhenotypingNetworkgeneratedextensivephenomicsdataonsubspeciesdiversitypanels,includingmorpho-physiological,phenologicalandchemicaltraitsconferringyieldpotential,lodgingresistanceandadaptationtoabioticstresses.Genome-wideassociationstudies(GWAS)identifiedmanylociandcandidategenesthatarepotentiallyrelevanttobreeding.Keyexampleswillbepresented.However,usingtheseresourcesinmolecularbreedingrequiresmanytime-consumingQTLvalidation,intogressionandselectionsteps.Wethereforeproposeashortcutstrategythatusesdirect,multi-traitengineeringofimprovedplantsthroughgermlinegenomeediting,suchasimprovedCRISPR/Cas9orsimilartechnologies.Wedevelopedoptimizedriceideotypeblueprintsbyin-silicovirtualbreedingbasedontheSAMARAcropmodel,whichsimulatestrait-traitcompensationsandtrait-environmentinteractions(phenotypicplasticity),andpresenttheresults.Asanextstep,guidedbyanew,higher-yielding,lodgingresistantideotype,majorcausativeDNApolymorphismsdiscoveredbyGWASwillbegenome-editedintoelitegeneticbackgrounds.Ideally,theresultingprototypeplantswill

44


requireonlyfewfurtherbreedingsteps.TheworkpresentedherewillcontinueinthesuccessorprogramoftheGlobalRiceSciencePartnership(GRiSP),calledRICE-CRP,from2017onwards.Anefforttophenotypethe3,000availablesequencedricegenomesisunderway.Successoftheproposedstrategywillhingepartlyonimprovedmethodologiestoedittargetedbase-pairorgenesubstitutionsintogenomes.Iftheapproachissuccessful,wepredictthatlibrariesofcausative,high-effectSNPswillbecomeamuchsought-afterresourceforcropimprovement.This,inturn,willaffectphenotypingstrategiesinthefuture.Scalablein-fieldphenotypingplatformsfordynamicmeasurementofperformance-relatedtraitsinbreadwheatJiZhou1,2,DanielReynolds1,ThomasLeCornu1,3,ClareLister2,SimonOrford2,StephenLaycock3,MattClark1,MikeBevan2,SimonGriffiths21TheEarlhamInstitute,Norwich,UK,email:[email protected],Norwich,UK,email:[email protected],Norwich,UKAutomatedin-fieldphenotypingcanprovidecontinuousandprecisemeasuresofadaptationandperformancetraitsthatarekeytotoday’sbreedingpipelinesandagriculturalpractices.Inourtalk,wewillintroduceourintegratedfieldphenotypingsystemsatJohnInnesCentreandEarlhamInstitute,includingunmannedaerialvehicles(UAV),a3Dscanningcropphenotypingplatform(Phenospex),cost-effectiveCropQuantworkstationsandothernovelhardware/softwaresolutionsthatfacilitatehigh-resolutionandhigh-frequencycropphenotyping.Inparticular,toempowertheassessmentofgenescontrollingyieldpotentialandenvironmentaladaptation,wewilltalkaboutourCropQuantsystem,acost-effectiveInternetofThings(IoT)inagricultureplatform(http://www.earlham.ac.uk/cropquant-next-generation-phenomics)designedtoenablenext-generationautomatedcropphenotyping.CropQuantincorporatesnetworkedsensors,single-boardcomputers,in-fieldwirelesscommunicationandopenhigh-throughputanalysisalgorithmstoprocessfieldexperimentationdata.Besidescroptraitanalysis,wehaveestablishedmachine-learningbasedmodelstoexploreandpredictthedynamicsbetweengenotype,phenotypeandenvironment(GxPxE).Aproof-of-principleexamplebasedonnear-isogeniclines(NILs)ofwheatsuchasPpd-1(lossoffunction),Ppd-D1a(photoperiodinsensitivity),Rht-D1b(semidwarfing),staygreeninducedmutants,Lr19(hypersensitiveresponsetothepathogen),andParagonwildtypewillbediscussedinourtalk.

45


Effectivedeliveryofphenomicsincommercialbreedingismoreaquestionofwhatandwhen,nothowG.J.Rebetzke1,J.Jimenez-Berni1,W.D.Bovill1,R.A.James1,D.M.Deery1,A.Rattey2,D.Mullans3,M.Quinn3

1CSIROAgricultureandFood,POBox1700,CanberraACT2601(email:[email protected])2DowAgroSciences,FrenchsForrestNSW20863Intergrain,19AmbitiousLink,BibraLakeWA6163Breedingforimprovedadaptationtowater-limitedenvironmentshasbeensuccessfulbutatratesofgeneticgainbelowthatoffavorableenvironments.Inlessfavorableenvironments,reducedgeneticvariancetogetherwithlargegenotype×environmentinteractionreducesrepeatabilityofgenotypeperformanceand,hence,confidenceinselection.Inpopulationscontainingappropriatetraitsforimprovedwaterproductivity,anopportunityexiststocomplementtraditionalselectionforyieldwithadvancedphenotypingtechnologiestoimproveresponsetoselectioninchallengingenvironments.Newhigh-throughputphenotypingtechnologiespromisemuchbenefittobreedersbutalsoposechallenges.Duetofixedbreedingcapacityconstraints,applicationofadditionalphenomicstoolsrequiresthedisplacementoftriedandtestedassessmentsrelatedtobreedinggoals.Assuch,breedershaverequiredstrongevidenceofthevalueofthesecomplementarytechnologiesindeliveringgeneticgainbeforedeployment.Theevidenceofthevalueofthesenewhigh-throughputtechnologiesisaccumulatingwiththebreedernowfacedwiththequestionofnot‘how’tophenotypebut‘when’.Yieldinlargeplotsisstillthebestpredictorofyield,andcanbereadilyachievedwithaplotheaderandabalance.Enrichingearlygenerationsinabreedingcycleforkeyallelesgeneticallycorrelatedwithperformancesignificantlyincreasesthelikelihoodofidentifyingsuperiorgenotypesinlater,moreexpensivestagesofmulti-environmenttesting.Itisatthisearlystageinthebreedingcycle,wheresmallplotsorspaced-rowsaretypical,thatimplementationofnewphenomicstoolsholdsmostpromise.Cost-reducinghyperspectralimagingandLiDAR(LightDetectionandRanging),coupledwithmoretraditionalRGBandNDVI,allowroutinedatacaptureonportable,self-propelledplatforms.Whencombinedwithtoolssuchasaerialinfraredthermo-imaging,biomass,leafarea,drymattercompositionandwater-use/stomatalconductancecanbereadilyestimated.Todrivegeneticgainsforwater-limitedenvironmentsperunitcost,weproposethatcarefulmultistageselection,beginningwithhigh-throughputphenotypinginearlygenerationsandculminatinginlaterassessmentinmanagedenvironments,bereadilyintegratedwithgenomicpredictionmodelingtotargetselectionunderspecific,high-frequencyenvironmenttypes.Together,repeatabilityandconfidenceshouldincreasetoproducecorrelatedincreasesingeneticgainatreducedcostorcycletimetobreedingprograms.Withbreeder-ledengagementthatincludesclosecollaborationwithphysiologists,geneticistsandmodelers,long-termgeneticprogresswillbeenhancedthroughassessmentandidentificationofnewtraitsandtraitcombinationsineliteanddiversegeneticresources,allowingtargeteddeploymentintoadaptedbreedinggenepools.

46


Plantdiseasephenomics:IdentificationofquantitativeresistanceincropplantsusingphenomicapproachesStephenRolfe1,SarahSommer1,FokionChatziavgerinos1,EstrellaLuna1,PierrePétriacq1,BruceGrieve2,DiegoCoronaLopez2,CharlesVeys2,JohnDoonan3,KevinWilliams3

1P3CentreforTranslationalPlantandSoilBiology,UniversityofSheffield,Sheffield,S102TN,UK2e-AgriSensorsCentre,UniversityofManchester,Manchester,M139PL,UK3InstituteofBiological,EnvironmentalandRuralSciences(IBERS),AberystwythUniversity,Penglais,Aberystwyth,Ceredigion,SY233DA,UK

Plantpestsanddiseasesaccountforupto40%oftotalagriculturallossesworldwide.Oneroutetoreducingtheselossesistousephenomicmethodstoidentifyquantitativeresistancetoinfectionincropvarieties.Quantitativeresponsesresultfromtheinteractionofmultiplegenesandthusprovideasustainableanddurableroutetocropprotection.Weareusingarangeofphenotypingapproachestoexaminetheimpactofbothfoliarandbelow-grounddiseasesonhostphysiologyanddiseasedevelopmentindiversitypanelsofcropplants.Physical(developmental),physiological(photosynthesis,wateruse)andbiochemical(pigmentationandmetabolicprofiling)methodshavebeenassessedfortheirabilitytoquantifyhostresponsestodisease.Inaddition,weareexploringnovel,low-costapproachessuchasmultispectralimagingandElectricalImpedanceTomography(EIT)toquantifydiseaseresponse.Asanexample,BrassicasfromadiversitypanelwereinoculatedwithPlasmodiophorabrassicae,thecausativeagentofclubrootdisease.Multiplephenotypicparametersweremeasuredtoidentifythosethatenableddiseaseprogressiontobequantifiedinanon-destructivemanner.Whilephotosyntheticmeasurementsobtainedusingquantitativeanalysisofchlorophyllfluorescencequenchingchangedonlyrelativelylateduringtheinfectionprocess,waterrelationshipswerealteredmuchearlier.Bothevapotranspiration(measuredusingthermalimagingofleaves)andwateruse(asdeterminedbyautomatedpotweighing)showedamarkedimpactofinfectiononthehostplant.Above-grounddevelopmentaldifferenceswerealsoobserved,butthesewerehighlydependentuponenvironmentalconditions.TheselectedparametersarecurrentlybeingusedtoexaminealargerpopulationofBrassicavarietiessothatthediseasephenotypescanbecorrelatedwithgeneticmarkers.PreliminarymeasurementsusingEIThavealsoshownpromiseasacost-effectivewaytovisualizebelow-groundresponsesdirectly.Strategiesforcropfield-basedhigh-throughputphenotyping(FB-HTP)inbreedingusingUAVplatformsJiangangLiu1,3*,GuijunYang1,2,3,HaiyangYu1,3,XiaoqingZhao1,BoXu1,31BeijingResearchCenterforInformationTechnologyinAgriculture,BeijingAcademyofAgricultureandForestrySciences,China,Emailliujg@nercita.org.cn2NationalEngineeringResearchCenterforInformationTechnologyinAgriculture(NERCITA),China3KeyLaboratoryofAgri-informatics,MinistryofAgriculture,ChinaRapidandnon-destructivemeasurementsforfield-basedphenotypingplayanimportantroleinacceleratingbreedingefficiency.Unmannedaerialvehicle(UAV)platformshavebeenusedforhigh-

47


throughputphenotypinginrecentyears,becausetheyrapidlyandcost-effectivelyprovidecropphysiologicaltraitstocropbreeders.Giventhatcomplextraitssuchasplantheight,biomass,lodgingresistanceandyieldarecontrolledbymultipleloci,large-scalemulti-environmentalphenotypicinformationanalysisisnecessaryforlocatingthegeneticlocithatarecloselyrelatedtothesetraits.Theobjectiveofthisstudywastoinvestigateamethodforfieldphenotypingofsoybeaninbreedingplotsbyproximalsensingduringdifferentgrowthperiodsanddeterminethebestmodelforyieldpredictionofmassivegermplasm.Fieldexperimentson100soybeanbreedingmaterialswereconductedin2014and2015.Anunmannedaerialplatformequippedwithadigitalcamera,amultispectralcamera,ahyper-spectrometerandathermalimagerwasusedforfield-basedhigh-throughputphenotypingofsoybeaninbreedingplots.Tenvegetationindicescombiningalgorithmsincludingpartialleast-squaresregression(PLSR),principalcomponentanalysis,multipleregression,exponentialregression,anartificialneuralnetwork,asupportvectormachineandadigitalelevationmodelwereadoptedforestimatingfourfield-basedsoybeanphenotypesinbreedingplots:height,leafareaindex(LAI),biomassandcanopytemperature.Plantheightofsoybeaninbreedingwasestimatedaccurately,withacorrelationcoefficientbetweenobservedandestimatedvaluesof0.92.ThePLSRapproachperformedbestforpredictingsoybeanyieldandestimatingLAIandbiomasswithcorrelationcoefficientsof0.85,0.91and0.84,respectively.ThecorrelationcoefficientbetweencanopytemperaturedetectedbytheUAV-basedInfraredThermalImagerandobservedbyhand-heldInfraredradiometerwas0.91,whichshowedgoodstabilityandconsistency.TheUAV-basedfieldphenotypingplatformisarapidandaffordablemethodforperformingcropphenotypicinformationanalysisinbreeding.AUAVplatformequippedwithmulti-sensorswasabletoidentifythedifferencesinphenotypicestimatesamongsoybeancultivars.Combiningproximalsensingdataandcropphysiologicaltraitscanimprovetheaccuracyofyieldpredictioninsoybeanbreeding.TheUAV-basedproximalsensingplatformprovidednovelinsightsinacceleratingthebreedingefficiency.

Wednesday14thDecember(Afternoon)PHENOPTYPINGFORCROPIMPROVEMENTcont’d

Remotesensingforcropimprovement:FromresearchtoindustrySebastienPraud1,FredericBaret2,AlexisComar3and,DavidGouache41BIOGEMMA,FRANCEFrance.Email:[email protected],FRANCE3HI-PHEN,FRANCE4ARVALIS,FRANCEPlantbreedingiscostlyandtime-consuming,andanytechnologythatcanhelppredicttheperformanceofeachgenotypewhilereducingthecostandamountoftestinggreatlyimprovestheoverallrateofgeneticgain.Inresponsetothisproblem,wedevelopedaprototyperemote-sensing-basedsystemtocharacterizetheresponseofwheatvarietiestonitrogen.Itisunderpinnedbytheknowledgeofhownitrogenaffectswheatcanopycharacteristics,suchasleafareaandangle,andchlorophyllcontent.We

48


installedRGB(red,green,blue)camerasandmultispectralreflectancespectrometersonatractor,whichenabledustoscreenthedynamicresponseof220wheatvarietiestodifferentnitrogentreatments.Usingthesedata,weshowedthatsuchsystemscanprovideresultsrelevantforidentifyinggenesandtheirfunctions,andforscreeninginplantbreeding.Weidentifiedaspectsofthewheatgenomethataffectcanopyreflectance,chlorophyllcontent,biomassandyield.Weshowedthatthesemeasurementsaresufficientlyrepeatableandwelllinkedtothetoleranceofsomevarietiestonitrogendeficiency.Hence,wewereabletousereflectancetraitsatspecificstagesofthewheatcroptoidentifysuperiorwheatvarietiesfornitrogendeficittolerance.Wewerethenabletoscaleupthesystemfromtrialsofaround100plotsto1000,whichhelpedustospecifynewsystemsdevelopedwithinthescopeofPhenome,aplantphenotypingnetworkbasedinFrance.Weimplementedtheseprototypesonanautonomousgroundvehicle.Morerecently,anairbornesystemwasimplementedandisnowavailabletoplantbreedersandgeneticists.Itisusedtoassessawiderangeofrelevanttraitslinkedtodroughtstresstoleranceinwheatandmaize.Testingtheefficacyoflarge-scalefieldphenotypingingenomicselectiontoacceleratewheatbreedingEricOber1,RobertJackson1,R.ChrisGaynor2,AlisonBentley1,PhilHowell1,JohnHickey2,IanMackay1

1NationalInstituteofAgriculturalBotany(NIAB),HuntingdonRoad,CambridgeCB30LE,UK2TheRoslinInstitute,UniversityofEdinburgh,EasterBush,Midlothian,EH259RGTheprocessofbreedingnewplantvarietiescanbeacceleratedandmadelesscostlybyapplyingnewmethodsofgenomicselection(GS).GSuseshigh-densitymolecularmarkersandrelativelyinexpensivegenotyping,incombinationwithphenotypicandpedigreedata,toincreasetheselectionintensityanddecreasethecycletimebetweeninitialcrossesandfinishedvarieties.Increasedprecisioncomesfrombothexploitinggeneticrelationshipsbetweentraitsandreducingtheeffectofenvironmentalinfluences.However,abottleneckthatoccurswhenapplyingGSisthecostofphenotypingtherequisitetrainingpopulation,whichforcommercialbreedingprogramsmustbelarge(severalthousandindividuals).Robustpredictionalgorithmsdevelopedfromthetrainingpopulationcanbeappliedtoreferencepopulationswithoutneedingtorepeatthephenotypingprocess.Whileyieldisthefundamentalphenotypicvariableofinterest,wetesttheideathatadditionalphysiologicalandmorphologicaltraitscanincreasethepredictivepowerofalgorithms,akintoincreasingthenumberofgeneticmarkers.Thescaleandcostofphenotypingcanbereducedbyusingremotesensingmethods,largelybasedonspectralreflectancefromcropsurfaces,whichhavebeenusedinagricultureresearchfordecades.Inacollaborativeprojectwithfourcommercialbreedingcompanies,wearetestingtheseprinciplesusingaUAVplatformtosupplementground-basedmeasurementstoderivephenotypicvaluesinalargepopulationof3000elitewheatlinesplantedintwofieldlocationsinCambridgeshirein2016.TypicalvegetationindicesbasedonreflectanceinthevisibleandnearinfraredandhighresolutionRBGimageswillbeusedtoestimatecanopygrowthandsenescence,cropheight,etc.HyperspectralandLiDARdata(e.g.,toestimatestemsolublecarbohydrateconcentrationsandbiomass),willsupplementdataderivedfrommultispectralcameras.Furtherprecisioncanbeobtainedbytakingintoaccountenvironmentalcovariatesthatvaryspatiallyacrossthetrialarea,suchasdifferencesinsoiltextureand

49


moisturecontent,obtainedusingmethodssuchaselectromagneticinduction.Fieldtrialsonthesamematerialswillbeconductedagainin2017.Phenotypingforabioticstresstoleranceincrops:IndianinitiativesJagadishRane1,PrashantKumar,MamruthaMadhusudhan,MaheshKumar,SaiPrasad

1NationalInstituteforAbioticStressManagement,Baramati,Pune,India.Email:[email protected],Karnal,India3IndianInstituteofAgriculturalResearch,RegionalStation,Indore,IndiaTolerancetoabioticstressincropsiscrucialforIndianagricultureasthefoodsecurityofmillionswillcontinuetodependonnewcropcultivarsthatyieldmoregrainandfodderinbothfavorableandunfavorableagro-ecosystems.Water-deficitagriculturalareasarepredictedtoexpandduetoimminentcompetitionbetweendifferentsectorsandtheadverseeffectsofclimatechange.Atpresent,68%ofIndianagricultureisrainfedandhighlypronetodrought.Hightemperatureslimittheproductivityofwintercropslikewheat.Thepredictedriseintemperaturecouldhavemoreimpactoncropproductivityintheabsenceofsufficientsoilmoisture.Increaseddemandforfoodcouldextendcropcultivationtodegradedsoilswithedaphicstressessuchassalinity,waterloggingandnutrientimbalance.Wheatgenotypesselectedbasedonyieldalonemaynotbesufficienttoaddressthesechallenges,asevidentfromthemarginalgeneticgainsincropproductivityintherecentpast.Hencetraitscontributingtoresilienceofcropstoabioticstresseswillbeessentialforfuturefoodproduction.Rapidlyevolvingmolecularapproachesarewideningthescopeforgeneticimprovementandcanbeeffectiveiftheassociationbetweenthegenestobeintrogressedanddroughttolerancetraitsisestablishedwithgreaterprecision.TheseconcernsandemergingopportunitiesforinvestigatingandphenotypingtraitsrecentlypromptedtheIndianCouncilofAgriculturalResearchtoestablishhigh-throughputphenomicsplatformsforcereals,pulsesandothercrops.Effortshavebeenmadetoemployphenomicstoolsforassessingresponsesofdifferentcropstoabioticstressessuchasdrought.Thisincludesassessingtheutilityoftechniquessuchaschlorophyllfluorescenceandthermalimagingsystemsfordifferentiatingstresstolerantandintolerantcropgenotypes;methodsforidentifyingmungbeangenotypesthatproducemorebiomasswithlesswaterrelativetolocallyadaptedcultivars;andphenotypingphotosyntheticefficiencyinspikes.Effortstoassociatestressresiliencetraitsandgenesareexpectedtoaccelerateifthesephenomicstoolsareadoptedalongwithafield-lab-fieldapproachatdifferentexperimentalsites.Thisreviewwillalsobrieflydescribeexistingandemergingopportunitiesforresearchcollaborationtoenhancecropresiliencetoclimateextremesthroughinternationalnetworks.

50


ImprovingtheprecisionofphenotypicdatausingUAV-basedimageryFranciscoPinto,MatthewReynolds

CIMMYT,GlobalWheatProgram,Mexico.Email:fr.pinto@cgiar.orgPhysiologicalbreedingcomplementsconventionalbreedingapproachesbydesigningstrategiccrossesthataimtofostercomplexandcomplementarytraitsinplantsinawiderangeofenvironments.Effectivescreeningforthesetraitsingeneticresourcesandfurtherscreeningoftheprogenyarekeyelementsofthisapproach.Remotesensingtoolsprovideanexcellentopportunityforhigh-throughputphenotypingunderfieldconditions,enhancingthevalueofphysiologicalbreeding.Inthiscontext,unmannedaerialvehicles(UAVs)havebeenimplementedbytheWheatPhysiologygroupatCIMMYTresultinginasuitableandeffectiveplatformtoremotelymeasuretraits,suchasNDVIandcanopytemperature,inthousandsofwheatlines.Aerialimageryenablesfastcollectionofspatiallyresolveddata,potentiallyimprovingtheprecisionoftraitmeasurementandtheeliminationofconfoundingfactorssuchasenvironmentalfluctuationsornon-vegetationelementswithintheplot.Byselectingpixelswithineachplotusingstatisticaloutlieranalysis,weimprovedthepredictionofbiomassandyieldforvariousgenotypescomparedtoground-basedmeasurements.Thissuggeststhathigher-resolutionimagescouldresultinmoreprecisemeasurementsofremotely-sensedtraits.However,thisalsoimpliesmorecompleximagesshowingahigherdegreeofheterogeneitywhichneedstobeunderstood.Inthispresentation,wedescribeourlatestadvancesintheanalysisofUAV-basedimagingforscreeninggeneticresourceswithinthecontextofphysiologicalbreeding.Phenotypingforbreedingandphysiologicalpre-breedingMatthewReynolds,GemmaMolero,FranciscoPinto,CarolinaRivera,FranciscoPinera,SivakumarSukumaran,MartaLopes,andCarolinaSaintPierreGlobalWheatProgram,CIMMYTPhenotypinghasbeenthecornerstoneofplantbreedingbut,untiltheadventofremotesensing(RS)tools,selectionwaslargelyrestrictedtoheritabletraits—phenology,height,kernelsize,diseaseresistance,andnon-negotiablecomplextraitsincludingend-usequalityandyielditself.RSallowstheevaluationofintegrativetraits—bydefinitiongeneticallycomplex—onascalethatsomewhatovercomesthedisadvantagesoftheirrelativelylowheritability.Inparticularcanopytemperatureisagoodpredictorofyieldandrootfunctionunderabioticstress,andanumberofspectralindices,includingNDVIandwaterindex,alsoshowpredictivepower.Recentresearchhasshownthattheimprovedprecisionofaerialphenotypingplatformsleadstoincreasedheritabilityandthereforegreaterpredictivepower.Additionaltraitsthatlendthemselvestoaerialhigh-throughputscreeningarepigmentsandotherindicesassociatedwithphotosynthesisandphoto-inhibitionandwater-relatedindicesassociatedwithtissuehydrationstatus.Innovationsinthepipelineincludeuseofimageanalysistoestimateagronomictraitsandtheapplicationofvegetativeindicestocorrectforspatialvariationinlargetrials.Byvirtueofbeinghigh-throughput,manyoftheRSindicesmentionedlendthemselvestolarge-scalegeneticresourcescreeningaswellasgeneticanalysis.

51


Integrativetraitshavelesspowertopredictperformanceunderhighyieldenvironments.There,physiologicalbreedingfocusesonpotentialparents’phenotypestodesigncomplementarycrosses,thoughonasmallerscale.Agoodexampleisspikephotosynthesis(SPS),whichisdifficulttomeasureandhasthusbeenlargelyignored,despitethefactthatspikesinterceptuptohalfofincidentradiationduringgrain-filling.ThreeinnovationshavebeenappliedtobettercharacterizeSPSincludinga360degreeLEDilluminationchamberforgasexchangemeasurement,long-durationspikeshadingtreatmentsanduseofstablecarbonisotopesanalysisofgrain;bothofthelatterhelptoestimatetheintegratedcontributionofSPStoyield.DOCUMENTINGADVANCESandIDEASINPLANTPHENOTYPINGHarmonizingeffortsamongphenotypinginitiatives:BottlenecksandopportunitiesJoséL.ArausUniversityofBarcelona,SectionofPlantPhysiology.Email:jaraus@ub.eduPhenotypingisthemainbottlenecktotheapplicationofnewmoleculartechniquesandbreedingprogressingeneral,raisingtheinterestoftheglobalscientificcommunityincollaborativeinitiatives,suchastheIPPNandtheWheatInitiative,whichreflectthevariedperceptions,priorities,experiencesorsenseofopportunityoftheirpromotors.Insomecases,emphasisisonthedevelopmentoffirst-ratefacilitiestophenotypeforspecialtraits—forexample,rootarchitectureandfunctionality—undercontrolledconditionsorfeaturingpermanentlydeployed“fieldplatforms”.Althoughthesefacilitiesmayhelptoadvancetheresearchfrontierwithinthediscipline,theconsumers—thebreedingcommunity—arestillsceptical.ArecentsurveybytheWheatInitiative’sExpertWorkingGrouponPhenotypingidentifieddatamanagementandannotationasthebiggestimmediateissuesforthe“phenotypingcommunity”toaddress,whereasaccessingreliablephenotypicdata,togetherwithfeedinghigh-throughputphenotypingresultsintobreedingprograms,willbeamongthebiggestissuesinonedecade.Inthatcontext,harmonizingeffortsacrossinternationalinitiatives,togetherwithotherrelevantactors(CGIARcenters,seedcompaniesandnationalagriculturalprograms),withaviewtobreeders’realneeds,maypavethewayforthefutureofcropphenotyping.

52


Postersession:Phenotypingforcropimprovement

OptimizingwheatrootarchitecturebyexploitingdiversegermplasmJonathanA.Atkinson12,Cai-yunYang2,StellaEdwards2,SurbhiMehra2,JulieKing2,IanKing2,MalcolmBennett1,DarrenM.Wells11SchoolofBiosciences,theUniversityofNottingham,UK.Email:[email protected]/BBSRCWheatResearchCentre,theUniversityofNottingham,UKRootarchitectureiskeyforefficientnutrientuptakeandthushasadirecteffectonyield.However,wheatbreedingandselectionprogramsoftendonotdirectlyconsiderrootarchitectureduetothepracticaldifficultiesofmeasuringit.Preliminaryanalysespointtoareservoirofgeneticvariationinkeyroottraitsamongwheatlandracesandwildrelativesthatcanbeintroducedintomoderncultivarstoincreasethegeneticvariationinmodernbreedingprogramsforkeyagronomictraits.ApopulationofAmbylopyrummuticumdoublehaploidintrogressionlinesproducedbytheNottingham/BBSRCWheatResearchCentrehasbeenphenotypedusinghigh-throughput,low-costpipelinesforbothrootandaerialtraits.Thephenotypingmethodologyandpreliminarydatafromtheselineswillbepresented.WaterstressfieldphenotypingandPHENOMOBILE-LVmonitoringofwheatB.deSolan1,F.Baret2,S.Thomas1,O.Moulin1,G.Meloux1,S.Liu2,S.Madec2,M.Weiss2,K.Beauchene1,A.Comar3,A.Fournier1,D.Gouache1

1ARVALISInstitutduvégétal,France2INRA,UMREMMAH,Avignon,France3HIPHEN,Avignon,FranceThePHENOMOBILE-LVisafullyautomatedrobotdesignedforhigh-precision,high-throughputfieldphenotyping.ItisequippedwithseveralsensorsincludingRGBcameras,spectroradiometersworkinginthevisibleandnearinfrared,andLIDARs.Allthesemeasurementsareperformedfromnadirandinclineddirectionstogathercomplementaryinformationoncanopystructure.Thesensorsruninactivemodeusingsynchronizedflashestoproducemeasurementsthatarefullyindependentfromnaturalilluminationconditions.Theyareautomaticallyfollowingtheacquisitionscenarios.In2016,alargefieldexperimentcomparing220wheatgenotypesunderbothwater-stressedandirrigatedconditionswasconducted.Usingasetofsensors,severalbiophysicalvariableswereestimated:cropheight,greenfraction,thefractionofinterceptedradiation(fIPAR),greenareaindex(GAI)andchlorophyllcontent.Comparedwithdestructivemeasurementstakenonasubsetofmicro-plots,thiswasinagreementwitharelativeprecisionofaround10%forGAIandchlorophyllcontent.Ourmaininterestinthesenon-destructivemeasurementswastoanalyzetheirdynamics.Eachmicro-plotwasphenotypedon10differentdatesduringthegrowingseason.Thetimeandintensityofthestressexperiencedbyeachgenotypewerethereforequantifiedbycomparingthechangesincanopystructureandleafcharacteristicsbetweenbothmodalities.Thepossibleimpactofsoilheterogeneitywasaccountedforin

53


datainterpretationbyusingamapofavailablesoilwatercontent,previouslyderivedbycombiningasoilelectricalresistivitymapandlocalsoilanalyses.AfterdescribingthemainfunctionsofthePHENOMOBILE-LV,themethodsandperformanceofthealgorithmsdevelopedtoestimatethebiophysicalvariablesarepresented,followedbytheircorrelationswiththetargetvariables(yieldandgrainproteincontent)andtheirheritability.Finally,themostpertinenttraitsderivedfromPHENOMOBILE-LVnon-destructivemonitoringareidentifiedbasedonIndirectSelectionEfficiencyfortolerancetowaterstress,whichcombinesheritabilityandcorrelationwithproductivityandquality.High-throughputphenotypingofearlyvigorinwheat:RapidmeasuresofleafareaasproxyofearlyplantgrowthparametersandyieldAviyaFadida-Myers1,2,AvivTsubary1,2,AiaSulkoviak1,AharonBellalou1,KamalNashef1,YehoshuaSaranga2,DavidJ.Bonfil1,ZviPeleg2,RoiBen-David1

1TheAgricultureResearchOrganization(ARO)-VolcaniCenter,BetDagan,Israel.Email:[email protected]

2TheHebrewUniversityofJerusalem,Rehovot,IsraelInsemi-aridMediterranean-likeenvironments,improvingearlyvigorofwheatvarietiesisanimportantbreedingtarget.Genotypeswithearlyvigorwillestablishbetterandcompletegroundcoverfaster,whichreduceswaterlossviaevaporation.Phenotypicparameterssuchascoleoptilelength,leafareaandrelativeemergenceareassociatedwithearlyvigorandcanbemeasuredonsingle-plants,andafieldSeptometercanbeusedtomonitorleafareaindex(LAI)inlargerexperimentalplotswithcommercialfieldstands.However,thosemethods,aswellasbiomasssampling,aretime-consumingandhavetechnicalconstraintsthatlimitthenumberofplantsthatcanbeefficientlyscreenedinawheatbreedingprogram.EasyLeafAreaisfree,open-sourcesoftwarethatrapidlymeasuresleafareaindigitalimages(ordinaryphotographsorscannerimages).ThissoftwareusestheRGBvalueofeachpixeltoidentifyleafandscaleregionsineachimageandtocalculatepercentageofleafgroundcover.Ourhypothesisisthat%covercouldserveasarapidlow-costmethodtomeasureearlyvigorandbiomassaccumulationinwheat.Inthecurrentstudy,weaimtoassessthecorrelationbetween%coverto:(i)phenotypicparametersofearlyvigor,(ii)biomassaccumulationandagronomictraits.Phenotypingof%cover,earlyvigorandbiomassparameterswererecordedinfiveindependentfieldexperimentsatthreedifferentscales:experiment-(1-2)Rehovot2014-16:near-isogeniclines(NILs)ina1-msinglerow;experiment-(3)Bet-Dagan2015-16:landracesin0.5m2;andexperiment-(4-5)Gilat2014-16:moderncultivarsinmicroplots(40m2).Inallexperiments,highsignificantcorrelationswereobservedbetween%cover,earlyvigorparametersandbiomassaccumulation.Inexperiment-1,%cover21daysafteremergencewaspositivelycorrelatedwithinitialgrainsize(r=0.85,p<.0001)and3rdleafarea(r=0.57,p=0.02).Itwasalsopositivelycorrelatedwithbiomass(r=0.67,p=0.004)andgrainyield(r=0.69,p=0.003).Inexperiment-2,%cover28daysand42daysafteremergencewaspositivelycorrelatedwith3rdleafarea(r=0.72,p<.008)andfastemergencerate.Inaddition,%coveronbothdateswasalsopositivelycorrelatedwithflagleafareaandgrainyield(r=0.6,p<.0038).Inexperiment-4,%coverwaspositivelycorrelatedwithLAImeasurementsboth50and70daysafteremergence(r=0.6-0.65,p<.0001).PCAanalysisofexperiment-4(explaining47.5%oftheexperimentalvariance)showedhighassociationbetween%coverat30and50daysandbiomassaccumulation(onadegree-daybasis),heightandfinalbiomass(mechanicalharvest130daysafteremergence).Theseresultsshowthepotentialof%cover

54


monitoringasagoodphenotypicindicatorofbothearlyvigorandbiomassaccumulationatthevegetativestageand,insomecases,finaldrymatterandgrainyield.Furtherlargeplotexperimentsareneededtoassesswhether,ortowhatextent,%covercouldserveasapractical,high-throughputalternativetoLAI.AnefficienttooltodescribeolivevarietiesthroughstrictlymathematicallydefinedmorphologicalparametersKonstantinosN.Blazakis1,LucianaBaldoni2,AbdelmajidMoukhli3,MarinaBufacchi4,PanagiotisKalaitzis1

1DepartmentofHorticulturalGenetics&Biotechnology,MediterraneanAgronomicInstituteofChania(MAICh),73100Chania-Crete,Greece,email:[email protected]

2ItalianNationalResearchCouncil,InstituteofBiosciencesandBio-Resources(CNR-IBBR),ViaMadonnaAlta,06128Perugia,Italy

3INRAMarrakech,URAméliorationdesPlantes,Marrakech,Morocco4ItalianNationalResearchCouncil,InstituteforAgricultureandForestSystemsintheMediterranean(CNR-ISAFOM),ViaMadonnaAlta,06128Perugia,Italy

Morphologicalanalysesofolivefruits,leavesandstonesmayrepresentefficienttoolsforcharacterizinganddistinguishingvarietiesandforestablishingphenotypicrelationshipsamongthem.Inthisstudy,wepresentafurthersteptowardsthedevelopmentofintegratedautomatedmethodologiesfordescribingfruit,leafandendocarpmorphologies.Ourapproachdiffersfromthetrendsandtoolscurrentlyusedtodescribethemorphologyofvariousorgans,sincethereisnoneedforpriortime-consumingmanualtechniquesorprerequisitesregardingthecoloroftheimagebackgroundorthepositionofthedescribedorgan.Herewepresentbiologicallymeaningfulmorphologicaltraitsinordertodescribefruit,leaforendocarpmorphologies.Thedevelopedparametershavebeendefinedstrictlymathematically,makingourmethodologymorerobustandefficient.Weexaminedquantitativeandqualitativemorphologicaltraitssuchassize,shape,symmetry,surfaceroughnessandpresenceofadditionalstructures(nipple,petiole,etc.).Somemorphologicalparametersappeartobeslightlyinfluencedbytheenvironmentandtheproposedmethodologycanbeausefulandrapidtoolforidentifyinganddistinguishingolivecultivars.Wehopethatourmethodologyisusefulandwillleadtomorphologicalanalysesofcropspeciessuchastomatoes,pears,grapesand,especially,olives.

55


High-throughputlaboratoryphenotypingoflettucetoassessdroughttoleranceMarianBrestic1,MarekKovar1,KlaudiaBruckova1,MarekZivcak1,KatarinaOlsovska1,XinghongYang2

1SlovakUniversityofAgriculture,Nitra,Slovakia2CollegeofLifeSciences,ShandongAgriculturalUniversity,Taian,ChinaLettuce(LactucasativaL.)isoneofthemostimportantsaladvegetablesintheworld.Itischaracterizedbyvariablemorphologicalformsandinterestingnutritionalcontentofhealth-promotingsubstances.Manydifferentformsoflettuceposeachallengewhenselectingsuitablegenotypesforcultivation.Theclassicdestructiveanalysisofplantmaterialisslow,time-consumingandoftenexpensive.Currently,modernplantphenotypingapproachesbasedonopticalsignalsfromplantsprovideopportunitiesforfastphenotypingandselectionofappropriatebiologicalmaterial.Theadaptiveresponsesof12differentlettucegenotypestoprogressivedroughtwerestudiedinapotexperimentunderenvironmentallycontrolledconditionsusingthePlantScreenTMphenotypingplatformintheAgroBioTechResearchCenteroftheSlovakUniversityofAgricultureinNitra,Slovakia.Genotypeprofilingwascomplementedbymeasuringwatercontentandbiochemicalcompounds(chlorophylls,carotenoids,anthocyanins,polyphenolsandprolinecontent).Ahigh-throughputphenotypingmethodbasedonautomateddigitalimageanalysisoffluorescence,RGBandhyperspectralsignalswasusedduringtheentirevegetativeperiodtoaccuratelymeasurethegrowthandphysiologicalandnutritionalresponsesoflettucegenotypestowatershortage.Non-invasiveautomaticRGBandVNIRhyperspectralimagingofplantsalloweddividinggenotypesbasedonleafcoloringintothreegroups(lightgreen,darkgreenandredleaves).Morphometricalanalysisshowedwatershortagereducedplantgrowthmeasuredasplanarplantareawithsignificantgenotypicdifferences.Onaphysiologicallevel,plantwaterstresswasaccompaniedbyapronouncedreductioninleafwatercontentandprolineaccumulation.MaximalandactualphotochemicalefficiencyofPSIIwashigherinredlettucegenotypes.Thisobservationwasconfirmedbytheevaluationlevelofthephotochemicalreflectanceindex.Scanningofopticalsignalsfromplantshasthepotentialtodiscriminatelettucecultivarswithdifferentcolorsandphysiologicaltraitsandcanbesuccessfullyusedforbasicautomaticgenotypeselection.ThisstudywassupportedbynationalgrantsAPVV-15-0721andAPW-15-0562andbilateralprojectwithChinaSK-CN-2015-0005.Animage-basedautomatedpipelineformaizeearandsilkdetectioninahigh-throughputphenotypingplatformNicolasBrichet,LlorençCabrera-Bosquet,OlivierTurc,ClaudeWelcker,FrançoisTardieuUMRLEPSE,INRA,MontpellierSupAgro,34060,Montpellier,FranceWaterdeficitstronglyimpactssilkgrowthandsilkemergenceinmaize(ZeamaysL.),whichinturndeterminethefinalnumberofovariesthatdevelopgrains.However,phenotypingsilkgrowthandsilkexpansionisdifficultatthroughputneededforgeneticanalyses.Wedevelopedanimage-basedautomatedpipelineformaizeearandsilkdetectioninahigh-throughputphenotypingplatform.Thefirststepconsistsofselectingthebestwholeplantside-viewimagescontainingmaximuminformationforeachplantandday,asthatcontainingmostleavesandwholestem,basedontop-viewimages.Inasecondstep,thebestsideimagesaresegmentedandskeletonized,andpotentialearpositionsare

56


determinedbasedonchangesinstemwidths.Thexyzearpositionidentifiedinthiswayservestopilotthemovementofamobilecameraabletotakeadetailedpictureat30cmfromtheear,withthefinalaimofdeterminingsilkemergenceandsilkgrowthduration.ThesemethodsweretestedusingthePhenoArchplantphenotypingplatform(www6.montpellier.inra.fr/lepse/M3P)onapanelof300maizehybrids.Thefirstresultsshowedthatin>80%ofcases,earsweresuccessfullydetectedbeforesilkingandthedurationofsilkexpansionsignificantlycorrelatedwithvisualscores.Theimagepipelinepresentedhereopensthewayforlarge-scalegeneticanalysesofcontrolofreproductivegrowthtochangesinenvironmentalconditionsinreproductivestructures.IdentificationofresistancetoramulosiscausedbyColletotrichumgossypiivar.cephalosporioidesinadvancedcottonbreedinglinesandmonitoringoframulosisdiseasebyRGB-imageanalysisOscarBurbano-Figueroa,MilenaMoreno-Moran,KeyraSalazarPertuz,LorenaOsorioAlmanza,KarenMontesMercado,ElianaReveloGomez,MariadelValleRodriguezThePlantInteractionsLaboratory,C.I.Turipaná,ReddeCultivosTransitoriosyAgroindustriales,CorporaciónColombianadeInvestigaciónAgropecuaria(CORPOICA),Km13víaMontería–Cereté,Cereté,Córdoba,Colombia.Email:[email protected]

CottongrowingregionsinSouthAmericaareaffectedbyramulosis,adiseasecausedbyColletotrichumgossypiivar.cephalosporioides(Cgc).Themostsevereepidemicscauseconsiderableyieldreductionlinkedtomeristemnecrosis,oversprouting,branchingandstunting.TheSinuValley,thebiggestcottonproducerinColombia,isheavilyaffectedbythisdisease.RainfallwasidentifiedasthemaindriveroframulosisdevelopmentintheSinuValleyincropsplantedatthebeginningoftherainyseason.Fifty-fiveadvancedbreedinglines(ABLs)wereassessedforramulosisfieldresistance.NineABLsexhibitedhighlevelsofpartialresistance(<10%ofplantsexhibitedsprouting).Tooptimizetheaccuracyofdiseaseassessmentandbreedingforramulosisresistance,weevaluatedtheuseofRed-Green-Blue(RGB)imagesforautomatedassessmentoframulosissymptoms.IndicesobtainedfromRGBdigitalimageshavebeenproposedasaffordablehigh-throughputphenotypingtoolsforestimatingplantdiseaseresistance,cropgrowthandyield.RGBimagesobtainedfrominoculatedplotswereanalyzedbyBreedpixsoftwareforextractingindices.TheaccuracyofRBGindicesforassessingramulosisincidenceandcropgrowthwascomparedwithvisualassessmentofplantdiseaseseverity,leafareaindexandplantbiomass.RGB-basedindicesareaccuratepredictorsofcottongrowth,yieldandramulosisincidenceandcost-effectivetoolsforcottonphenotypingbasedonautomationofRGB-imageassessmentandaffordableRGBcameras.

57


PredictingyellowrustandSeptoriatriticiblotchinwheatbyhyperspectralphenotypingandmachinelearningRitaArmoniené1,AlexanderKoc1,SalvatoreCaruso1,TinaHenriksson2,AakashChawade11SwedishUniversityofAgriculturalSciences(SLU),Alnarp,Sweden.Email:[email protected]ännenLantbruk,Svalöv,SwedenVariousfoliardiseasesaffectbreadwheat(TriticumaestivumL.),thussignificantlyreducingitsgrainyields.YellowrustcausedbyPucciniastriiformisf.sptritici(Pst)isoneofthemostdevastatingfungaldiseasesofwheatinmajorpartsoftheworld.InSweden,yellowrusthasbecomeincreasingcommoninsouthernandcentralpartsofthecountryandhasbecomeamajorconcern.Septoriatriticiblotch(STB)causedbyZymoseptoriatriticiisalsoadamagingdiseaseofwheatcausingyieldlosseswhenleftwithoutcontrol.Hence,earlydetectionoftheoutbreakofthesediseasesisneededtoreducetheextentofthedamage.Hyperspectralphenotypingwithproximalsensorsorsensorsmountedonunmannedaerialvehicles(UAVs)canrecordthecanopyreflectancespectraoverawiderangeofvisibleandnear-infraredspectrum.Thehyperspectralreflectancedatacangenerateenormousamountofinformationonthephysiologicalandbiochemicaltraitsoftheplants.Anydeviationsinthesetraitsduringthegrowthcyclesuggestabioticorbioticstressfactorsaffectingtheplant.Thus,hyperspectralphenotypingcanbeextremelyusefulinpredictingfoliardiseaseoutbreakduringvariousstagesoftheplantgrowth.TheaimofthisprojectistoevaluatethepossibilityofpredictingyellowrustandSTBincidenceinwheatbyhyperspectralphenotypingandmachinelearning.Thus,hyperspectralphenotyping(350-1150nm)wasperformedwithahandheldhyperspectralsensorApogeePS-100(ApogeeInstruments,Inc.,USA)atthemilkdevelopmentstage(Zadoks71-77)oftwobi-parentaldoubledhaploidwinterwheatpopulationsinfieldsofsouthernSweden.Thetwopopulationswerescoredforyellowrustatthebooting(Zadoks40-49)anddoughdevelopmentstages(Zadoks80-87)andSTBatthebootingstage.Over80previouslyknownindiceswereestimatedfromtheobtainedhyperspectraldatausingacustomRpackage.Thereafter,randomforest(RF)predictionmodelswerebuiltfromthefirstbi-parentalpopulationasthetrainingpopulationandseparatelyforyellowrustandSTB.Thesecondbi-parentalpopulationwasusedasanindependenttestsettoevaluatetheobtainedpredictionmodel.TheyellowrustRFmodelexplained66%and71%ofthephenotypicvarianceinthetrainingandthetestpopulationrespectively.Thecoefficientforprediction(R2)was0.74inthetestpopulation.Ongoingworkwillincludesecondyearoffieldtrialsandfurtherrefinementofthemodels.Basedonthesepreliminaryresults,hyperspectralphenotypingcombinedwithmachinelearningapproachesseemstobeaverypromisingapproachforpredictingfoliardiseasesincidencelevelsinwheat.

58


ExploringthepotentialofspectralreflectancefordetectionoforganandcanopypropertiesinwheatM.FernandaDreccer1,JoseJimenez-Berni1,StephenGensemer2,EthanGoan2

1CSIROAgricultureandFood2CSIROManufacturingOurworkaimstoobtainhigh-throughputinformationonkeybiochemicalcomponentsofcroporgans,withoutcostlyandtime-consumingdestructivesamplingofsingleorgansorcropcanopies,withhyperspectralimaging.Inthispaper,weexplorethepotentialofthistoolfordeterminingspike,grainandstempropertiesduringgrain-filling.Totakemeasurementsattheorganlevel,asystemconsistingofartificialillumination,hyperspectralcamerasandamovingflatbedwasdesignedtoscanspikes,grainsandstems.Hyperspectraldataandwetchemistryarecombinedtodevelopandevaluatedifferentpredictivealgorithmsforthedetectionanddiscriminationofconcentrationofcarbohydrates,proteinandwatercontent.Asimilareffortisbeingcarriedoutinfieldexperiments,combiningreflectancewithLiDARinformation.Improvingnitrogenuseefficiency:Canbetterphenomicsmakeadifference?TrevorGarnett1,NicholasSitlington-Hansen1,DarrenPlett2,MalcolmHawkesford3,BettinaBerger1

1ThePlantAccelerator,AustralianPlantPhenomicsFacility.Email:trevor.garnett@adelaide.edu.au2TheAustralianCentreforPlantFunctionalGenomics,UniversityofAdelaide,Australia3RothamstedResearch,Harpenden,UKNitrogen(N)fertilizationisfundamentalforachievingcropyieldsbuttheinefficientuseofnitrogenfertilizerleadstomajoreconomicandenvironmentalcosts.Overthelast20years,therehasbeenmajorpublicandprivateinvestmentinimprovingplants’nitrogenuseefficiency(NUE),butwithlimitedsuccess.ThereasonsbehindtheinabilitytoimproveNUEarebecomingclearerandtechnologicaladvancesarepavingthewayforaddressingtheseissuesandmakingrealprogress.AmajorreasonforthelackofprogressinimprovingNUEisthatwhatdeterminesNUEinacroplikewheatdiffersgreatlydependingonenvironmentandmanagement,bothofwhichvarygreatly.EvenwithinAustralia,improvingwheatNUEmeansverydifferentthingsforwinterrainfallcropsinthedeepsandysoilsofWesternAustraliacomparedwithwheatcropsinthedeepclaysofSouthernQueenslandwhichrelyonstoredsummerrainfall,andaroundtheworldtherearemanydifferentenvironmentandmanagementscenarios.Globalefforts,suchastheWheatInitiative,arecataloguingdifferentwheatNUEscenariostolookforcommontargets,thusenablingbettercoordinationofglobalactivities.Theimpactofenvironmentandmanagementmeansthatgoodphenomics,bothincontrolledenvironmentsandinthefield,isessentialtomakerealprogressinNUE.Advancedfield-basedphenotypingplatformsthatallowdetailedmeasurementofbiomassandnitrogenstatusthroughouttheseasoncanfacilitatetheunravellingofgeneticxenvironmentxmanagementinteractions.Improvedcontrolledenvironment

59


phenotypingsystemsandnon-destructivemeasurementofgrowthandtissueNofferthepotentialtofurtherdissectthegeneticbasisofNUE.ControlledenvironmentphenotypinghastoberelevanttofieldperformanceandthisbecanhelpedbybettermatchingofcontrolledenvironmentconditionstotheknownenvironmentandmanagementscenariosthatmosteffectNUE.IdentifyingwheatroottraitsandregulatorygenesthatcontrolnitrogenuptakeefficiencyMarcusGriffiths1,JonathanAtkinson1,MalcolmJ.Bennett1,SachaMooney2,DarrenM.Wells11CentreforPlantIntegrativeBiology,SchoolofBiosciences,UniversityofNottingham,UK2DivisionofAgricultural&EnvironmentalSciences,SchoolofBiosciences,UniversityofNottingham,UK,email:[email protected]

Wheat(Triticumspp.)isaparticularlyimportantcropforfoodsecurity,providing20%ofworldwidecalorieintake.Wheatyieldsarenotmeetinganincreasingglobaldemandof2.4%perannum.Improvementofresourcecaptureinwheatcouldhelpmeetthisyielddemand.Nitrogenisanessentialmacronutrientforplantgrowthanddevelopment;however,only50-60%oftheNinfertilizersistakenupbytheplants.Domesticationofmodernvarietiesofwheatmayhavecausedpotentiallybeneficialagronomictraits,particularlyintherootsystem,tobelost.Optimizationofrootsystemarchitecture(RSA)couldthusprofoundlyimprovenitrogenuptakeefficiency(NUpE),whichcouldinturnincreaseyieldpotential.Ancestralwheatgermplasmanddoubledhaploid(DH)mappinglinescanbeusedtoidentifyandbreeddesirabletraitsbackintocommercialwheatvarietiestoincreaseyieldpotential.Usinganewrootphenotypingsystem,X-rayMicroComputedTomography(µCT),athree-dimensionalrepresentationofwheatrootscannowbeimagedinsoil.SeedlingrootQTLhavebeenpreviouslyidentifiedusingaSavannahXRialto(SXR)doubledhaploidmappingpopulationunderrepleteNhydroponicconditions.ThismappingpopulationhasnowbeenscreenedunderlowNconditions,andNtreatmentspecificandnonspecificQTLidentified.AsubsetofSXRlineswasusedfor3DµCTanalysisbasedon2DRSAandfieldNUpEparameterstoidentifypromisingroottraitsinseedlingsandmatureplants.Canhigh-throughputphenotypinghelppredictsoybeanyieldincontrastingenvironments?RaceHiggins,KyleParmley,AsheeshK.SinghDepartmentofAgronomy,IowaStateUniversity,Ames,USA.Email:rhiggin2@iastate.eduImprovingseedyieldisthemostimportantobjectiveinsoybean[GlycinemaxL.(Merr.)]breedingprograms.Theobjectiveofthisstudyistofindthephysio-geneticparametersthatdrivesoybeanyieldthroughacriticalassessmentofphysiologicaltraitsatmultiplecropgrowthstages.Thisstudyincluded32genotypesfromthesoybeannestedassociationmapping(SoyNAM)panelconsistingofmaturitygroupsIIandIIIwithindeterminateandsemi-determinategrowthhabits,andancestriesrangingfromPlantIntroductionaccessionstomodernelitecultivars.Threecontrastingplantingdensities(50K,140K,230Kperacre)weretreatedasfixedfactorstostudythephysio-geneticsofyieldunderdifferentcropmanagementconditions.ExperimentsweregrowninreplicatedplotsinfiveenvironmentsinIowa,USA.

60


Physiologicalparameterscollectedincludedleafareaindex(LAI),interceptedphotosyntheticallyactiveradiation(iPAR),canopytemperature,SPADchlorophyllcontent,andmultiplehyperspectralremotesensingvegetationindicesthroughspectroradiometerviagroundtruthing.Physiologicaltraitswerecollectedovermultiplegrowthstagesthroughoutthegrowingseasontodetermineplantresponse.Seedyieldwascollectedperyieldplot.Yieldpredictionequationswerebuiltusingstep-wiseregressionequations,andseveralcommonindiceswereidentifiedasbestpredictorsofyieldindifferentenvironmentsand/ordensitytreatmentsatdifferentcropgrowthstages.On-goingresearchisattemptingtodeterminethegeneticfactorsrelatedtospectralindicesusingaSoyNAMmappingpopulationsub-setthroughphenotypingbasedonspectroradiometerandaerialmulti-spectralcameras.High-throughputfieldphenotypingofwheatplantheightandgrowthrateinfieldplottrialsusingUAVremotesensingFennerH.Holman

1*,AndrewB.Riche2,AdamMichalski

2,MarchCastle

2,MartinJ.Wooster

1,3,Malcolm

J.Hawkesford2*

1DepartmentofGeography,King’sCollegeLondon,London,WC2R2LS,UK.Email:[email protected]

2RothamstedResearch,Harpenden,Hertfordshire,AL52JQ,UK.Email:[email protected]

3NationalCentreforEarthObservation(NCEO),Leicester,LE17RH,UKThereisagrowingneedtoincreaseglobalcropyields,whileminimizinguseofresourcessuchasland,fertilizersandwater.Agriculturalresearchersuseground-basedobservationstoidentify,selectanddevelopcropswithfavorablegenotypesandphenotypes;however,theinabilitytocollectrapid,highqualityandhighvolumephenotypicdatainopenfieldsisrestrictingthis.Thisstudydevelopsandassessesamethodforderivingcropheightandgrowthraterapidlyandrepeatedlyusingmulti-temporal,veryhighspatialresolution(1cm/pixel),3DdigitalsurfacemodelsofcropfieldtrialsproducedfromimagerycollectedbyUnmannedAerialVehicle(UAV)flightsviaStructurefromMotion(SfM)photogrammetry.WecomparedUAVSfMmodeledcropheightstothosederivedfromterrestriallaserscanner(TLS)andtothestandardfieldmeasurementofcropheightconductedusinga2-mrule.ThebestUAV-derivedsurfacemodelandtheTLSbothachievedaRootMeanSquaredError(RMSE)of0.03mcomparedtotherule.TheoptimizedUAVmethodwasthenappliedtothegrowingseasonofawinterwheatfieldphenotypingexperimentcontaining25differentvarietiesgrownin27m2plotsandsubjecttofourdifferentnitrogenfertilizertreatments.AccuracyassessmentsatdifferentstagesofcropgrowthproducedconsistentlylowRMSEvalues(0.07,0.00and0.03mforMay,JuneandJulyrespectively),enablingcropgrowthratetobederivedfromthemulti-temporalsurfacemodels.Wefoundgrowthratesrangedfrom-13mm/dayto17mm/day.Ourresultsclearlyshowtheimpactofvariablenitrogenfertilizerratesoncropgrowth.Thedigitalsurfacemodelsproducednovelspatialmappingofcropheightvariationbothatthefieldscaleandwithinindividualplots.ThisstudyprovesUAV-basedSfMhasthepotentialtobecomethenewstandardforhigh-throughputphenotypingofin-fieldcropheight.

61


PhenotypingtraitstoimprovedroughttoleranceinwheatYin-GangHu1,2,LiangChen11StateKeyLabofCropStressBiologyforAridAreasandCollegeofAgronomy,NorthwestA&FUniversity,Yangling,Shaanxi,712100,P.R.China

2InstituteofWaterSavingAgricultureinAridRegionsofChina,Yangling,Shaanxi,712100,China.Email:[email protected]

Sincewheatisgrownmainlyinrainfedandlowirrigationregions,droughtisoneofthemostimportantstressesworldwide.Asdroughtmayoccurateverygrowthstagetovariousdegrees,differenttraitsareinvolvedindroughttoleranceatdifferentstages.Droughttoleranceisthusacomplexquantitativetrait.Toimprovedroughttolerance,weusedvarioustraits,suchascoleoptilelengthforseedlingemergence,earlyvigorforquickseedlingpopulationestablishment,highertranspirationefficiency(CID)forleafstructureandcanopytemperaturefortherootsystem.WealsoevaluatedtheeffectsofGA-responsivedwarfinggenes,Rht4,Rht12,Rht13andRht18ondroughtrelatedandagronomictraitsandfoundthatthosegenescouldimprovecoleoptilelength,earlyseedlingvigorandtherootsystemingeneral,whilereducingplantheighttosomeextent,comparedwiththeGA-insensitivedwarfinggenesRhtB1bandRhtD1b;however,theireffectsvariedgreatlywiththedifferentgeneticbackgrounds.HyperspectraldiseasesignaturesfordetectingcharcoalrotinsoybeanSarahJones,AsheeshK.Singh,SoumikSarkar,BaskarGanapathysubramanian,DarenMueller,ArtiSinghIowaStateUniversity,USA.Email:[email protected][Glycinemax(L.)Merr]yieldisunderconstantthreatbybioticfactors,especiallydiseasesincludingcharcoalrotcausedbyMacrophominaphaseolina,whichisanimportantfungalpathogenthataffectssoybeansinwarm,dryenvironments,andismovingnorthwardintotheprimarysoybeangrowingregionsoftheUnitedStates.Asthisdiseasecancauseupto30%yieldloss,breedingprogramsstrivetoproducenewsoybeanvarietiesthatincludediseaseresistancetraitstocombatyieldlosses.However,currentphenotypingmethodsusedtojudgediseaseseverityoftenincludetime-andlabor-intensivevisualratingsthataresubjecttohumanerrorandbias.Hyperspectralimaging(HSI)technologyoffersanalternativesolutiontothephenotypingbottleneckasithastheabilitytocollectpreciseandaccuratephenotypesandcandetectminordifferencesindiseaseexpression.Inaddition,HSIcapturesspectralreflectancefromregionsoftheelectromagneticspectrumbeyondhumanvisionandthereforehasthepotentialfordetectingsymptomsnotyetvisibletothehumaneye.ThepurposeofthisresearchistodeterminethespectralreflectancesignaturesofcharcoalrotusingHSIforfutureimplementationinhigh-throughputphenotypingplatforms.Thestudyutilizedhyperspectralimagingtoexaminethespectralreflectancepatternsoftworesistantandtwosusceptiblesoybeangenotypesthatweremock-inoculatedandinoculatedwithcharcoalrotusingacut-steminoculationprotocolinagrowthchamber.PreliminaryresultsindicatethatHSIsuccessfullydifferentiatedsymptomsonplantpartsthatwerenotdistinguishablethroughvisualassessment.

62


IdentificationofwaterusestrategiesatearlygrowthstagesindurumwheatusingmodernshootimagephenotypingNiklasKörber1,AlirezaNakhforoosh2,ThomasBodewein1,KerstinA.Nagel,FabioFiorani1,GernotBodner21InstituteforBio-andGeosciences,ForschungszentrumJülich,Germany2DepartmentofCropSciences,UniversityofNaturalResourcesandLifeSciences,Vienna,AustriaModernimagingtechnologyprovidesnewapproachestoplantphenotypingfortraitsrelevanttocropyieldandresourceefficiency.ThescreenhousephenotypingfacilityattheJülichResearchCenterwasusedtoinvestigatewaterusestrategiesatearlygrowthstagesindurumwheatgeneticresourcescombinedwithphysiologicalmeasurements.12durumlandracesfromdifferentpedo-climaticoriginwerecomparedtothreemoderncheckcultivarsinagreenhousepotexperimentunderwell-watered(75%plantavailablewater)anddrought(25%plantavailablewater)conditionstoidentifydifferentwater-usestrategiesatearlyvegetativestages.Duringearlygrowth,genotypeswithlargeleafareahadhighdry-matteraccumulationunderbothwell-wateredanddroughtconditionscomparedtogenotypeswithcompactstature(Nakhforooshetal.2016).However,highstomataconductancewasthebasistoachievehighdrymatterperunitleafarea,indicatinghighassimilationcapacityasakeyforproductivityinmoderncultivars.Inindependentexperimentsweanalyzedrootarchitecturechangesinthesamecontrastingpaneltorevealbelow-groundresponses.Weconcludethattheidentifiedwaterusestrategiesbasedonearlygrowthshootandrootphenotypingcombinedwithdetailedgasexchangeanalysisprovideaframeworkforidentifyingwater-usestrategiesanddevelopingtargetedselectionofdistinctpre-breedingmaterialputativelyadaptedtodifferentscenariosofwater-limitedenvironments.TacklingamajorepidemicofmaizelethalnecrosisineasternAfricaL.M.Suresh1,YosephBeyene1,ManjeGowda1,JumboMacDonaldBright1,MichaelOlsen1,BiswanathDas1,DanMakumbi1,B.M.Prasanna11InternationalMaizeandWheatImprovementCenter(CIMMYT),ICRAFCampus,UNAvenue,Gigiri,POBox1041-00621,Nairobi,Kenya.Email:[email protected](MLN)in2011inKenya,followedbyconfirmedreportsofMLNinD.R.Congo,Ethiopia,Rwanda,TanzaniaandUganda,iscausingseriousconcerntostakeholdersinSSA.IneasternAfrica,MLNiscausedmainlybysynergisticinteractionbetweentwoviruses,maizechloroticmottlevirus(MCMV)andsugarcanemosaicvirus(SCMV).InKenya,fieldobservationssuggestedthatthediseasewasaffectingalmostallcommercialmaizevarieties,causingestimatedyieldlossesof30-100%dependingonthestageofdiseaseonsetandseverity.RespondingrapidlytotheMLNepidemic,CIMMYT,inpartnershipwiththeKenyaAgriculturalandLivestockResearchOrganization(KALRO),establishedacentralizedMLNScreeningFacilityatNaivashainSeptember2013.WehaveoptimizedtheprotocolsforartificialinoculationofmaizegermplasmagainstMLN,aswellasfortheindividualviruses.ThefacilityalsooffersMLNphenotypingservicetonationalagriculturalresearchinstitutions(freeofcharge)andseedcompanies(onacostrecoverybasis).SincetheestablishmentoftheMLNScreeningFacilityin2013,morethan60,000germplasmentries(morethan100,000rows)

63


havebeenscreenedforMLN.Germplasmentriesincludedinbreds,open-pollinatedvarieties,pre-commercialhybrids,commercialhybrids,mappingpopulationsandlandraces.Fivefirst-generationMLN-tolerant(CIMMYT-derived)hybridshavebeenidentifiedandreleasedinKenya(H12MLandH13ML),Tanzania(HB607)andUganda(UH5354andUH5358),andarebeingscaledupbyseedcompanypartnersforcommercializationin2016-17.Wearenowtesting15second-generationMLN-tolerant/resistanthybridsinNationalPerformanceTrialsinKenya,TanzaniaandUganda.SeveralmorepromisingMLNresistanthybridsareinthepipeline.TheMLNScreeningFacilityalsoservesasaplatformtounderstandthegeneticbasisofMLNresistance,aswellastoundertakeresearchonseedtransmissionofMLN-causingviruses.TheMaizeGenomestoFields(G2F)initiative:DatamanagementandavailabilityNaserAlKhalifah1,DarwinA.Campbell1,ReneeWalton1,CintaRomay2,Cheng-TingYeh1,RamonaWalls3,GenomestoFieldsCollaborators4,PatrickS.Schnable1,DavidErtl5,NataliadeLeon5,JodeEdwards1,6,CarolynJ.Lawrence-Dill11IowaStateUniversity2CornellUniversity3UniversityofArizonaandCyVerse4http://www.genomes2fields.org/5IowaCornPromotionBoard6UniversityofWisconsinThemulti-institutionalGenotypebyEnvironment(GXE)subprojectofthemaizeGenomestoFields(G2F)Initiativeaimstoassesstheimpactsofgenotypeandenvironmentaleffectsontheperformanceofalargecollectionofmaizehybrids.Towardthataim,wehavecollectedandanalyzedgenotypic,phenotypic,andenvironmentaldatafrommorethan30NorthAmericanfieldlocationsacross3years(atotalof86environments).Thesedatacomprise14corephenotypictraitsandweathermeasurementscombinedwithgenotypicdata,andforasubsetoflocations,imagedata(atscalesfromindividualplanttoindividualfield).Toassistinthemanagementofthesediversedatatypes,wehavedevelopedanddeployedarobustyetflexibledatamanagementandanalysispipelinethatmeetstheproject’sneedsbutisalsoextensibletothebroadplantbreedingcommunity.Inthisposter,wepresentprogressmadeoverthepastyearworkingwithpartnersatCyVerseanddescribemethodsforG2Fdataaccessandanalysis.Formoreinformation,visithttp://www.genomes2fields.org/.

64


NSFresearchtraineeship–P3,predictiveplantphenomicsJulieDickerson,ThedoreHeindel,CarolynJ.Lawrence-Dill,PatrickSchnableIowaStateUniversityNewmethodstoincreasecropproductivityarerequiredtomeetanticipateddemandsforfood,feed,fiberandfuel.Usingmodernsensorsanddataanalysistechniques,itisnowfeasibletodevelopmethodstopredictplantgrowthandproductivitybasedoninformationabouttheirgenomeandenvironment.However,doingsorequiresexpertiseinplantsciencesaswellascomputationalsciencesandengineering.ThroughP3,webringtogetherstudentswithdiversebackgrounds,includingplantsciences,statistics,andengineering,andprovidethemwithdata-enabledscienceandengineeringtraining.Thecollaborativespiritrequiredforstudentstothriveinthisuniqueintellectualenvironmentwillbestrengthenedthroughtheestablishmentofacommunityofpracticetosupportcollectivelearning.Thistraineeshipanticipatespreparing48doctoralstudents,including28NRTfundeddoctoralstudents,withtheunderstandingandtoolstodesignandconstructcropswithdesiredtraitsthatcanthriveinachangingenvironment.AnalysisofwaterlimitationeffectsonthephenomeandionomeofArabidopsisatthePlantImagingConsortiumLuciaMAcosta-Gamboa1,SuxingLiu1,ErinLangley1,ZacharyCampbell1,NormaCastro-Guerrero2,DavidMendoza-Cozatl2,ArgeliaLorence1,31ArkansasBiosciencesInstitute,ArkansasStateUniversity,JonesboroAR,[email protected]

2ChristopherS.BondLifeSciencesCenter,UniversityofMissouri,Columbia,MO,USA3DepartmentofChemistryandPhysics,ArkansasStateUniversity,JonesboroAR,USAUnderstandingplantresponsesandadaptationstolimitedwateravailabilityiskeytomaintainingorimprovingcropyield;thisisevenmorecriticalconsideringthedifferentprojectionsofclimatechange.ThisisanNSF-fundedcollaborativeeffortbetweentheMendozaandLorencegroupsaspartoftheactivitiesofthePlantImagingConsortium(http://plantimaging.cast.uark.edu/).Inthiswork,wecombinedtwohigh-throughput-omicplatforms(phenomicsandionomics)tobegindissectingtime-dependenteffectsofwaterlimitationinArabidopsisleavesand,ultimately,seedyield.Asproofofconcept,weacquiredhigh-resolutionimageswithvisible,fluorescenceandnear-infraredcamerasandusedcommercialandopen-sourcealgorithmstoextracttheinformationcontainedinthoseimages.Atadefinedpoint,sampleswerealsotakenforelementalprofiling.Ourresultsshowthatgrowth,biomassandphotosyntheticefficiencyweremostaffectedunderseverewaterlimitationregimes,andthesedifferenceswereexacerbatedatlaterdevelopmentalstages.Theelementalcompositionandseedyield,however,changedacrossthedifferentwaterregimestestedandthesechangesincludedunder-andover-accumulationofelementscomparedtowell-wateredplants.Ourresultsdemonstratethatthiscombinationofphenotypingtechniquescanbesuccessfullyusedtoidentifyspecificbottlenecksduringplantdevelopmentthatcouldcompromisebiomass,yieldandthenutritionalqualityofplants.

65


High-throughputphenotypicexplorationofgeneticvariationfromwildrelativesforbreedinghighbiomassandyieldinwheatLornaMcAusland1,LauraBriers1,StellaEdwards2,Cai-yungYang2,JulieKing2,IanKing2,ErikMurchie1

1SchoolofBiosciences,UniversityofNottingham,SuttonBoningtonCampus,Leicestershire,LE125RD.Email:[email protected]

2BBSRCWheatResearchCentre,UniversityofNottingham,SuttonBoningtonCampus,Leicestershire,LE125RD

Withtheglobalpopulationsettoreachninebillionby2050,thereisanurgentneedtoincreasefoodproductionbyatleast60%.Wheatproductionhasplateauedinmanyareasoftheworldduetoalackofnovelgeneticvariationforagronomicallyimportanttraits,compoundedbytheeffectsofclimatechange.Distantrelativesandlandracesofferavaluablesourceofphenotypictraitslostintheevolutionofmodernvarietiesastheresultofseveregeneticbottlenecks.Recentadvancesintheabilitytodetectandcharacterizegeneticmaterialfromcrossesbetweendistantrelativesandelitelines(introgressions)providepowerfultoolstorapidlyintroducegreatergenome-widevariation.Rapidphenotypingoftheseintrogressedlinesisavitalstepinidentifyingvariationinrelevanttraitsthatcanbeusedforbreedingandpre-breedingintoelitevarietiesforimprovedbiomassandyield.Thereisnowrecognitionthatimprovedgrainyieldsofmajorcropsrequireenhancedtotaldryweightproductionwhichmustarisemostlyfromanimprovementofradiation-useefficiency(RUE).RaisingRUErequiresahigherleafandcanopyphotosynthesisrateandthisremainsanimportanttargettounderpinfutureyieldprogress.WorkingwiththecurrentWheatImprovementStrategicProgramme(WISP),theobjectiveofthiscollaborationistodeveloparapidphenotypingscreentoidentifyimprovedphotosynthetictraitsinnewaccessionsofdistantrelativesofwheat(e.g.,Ae.muticaandT.urartuaccessions)andintrogressedpopulationsfromWISP,withtheaimofbackcrossingthesetraitsintomodernwheatvarietiesforimprovedbiomassandyieldinthefield.High-throughputphenotypingofcanopydevelopmentinsoybeanFabianaFreitasMoreira1,AnthonyA.Hearst2,KeithCherkauer2,KatyMRainey11DepartmentofAgronomy,PurdueUniversity,USA.Email:[email protected]&BiologicalEngineering,PurdueUniversity,USA.TheSoybeanBreedingprogramatPurdueUniversitypreviouslyshowedthathigh-throughputUAS-basedphenotypingofcanopydevelopmentcanpredicttheyieldpotentialofsoybeanlines,andearly-seasoncanopydevelopmenthasastronggeneticassociationtoyield.Severalaspectsofcanopydevelopmentwereevaluatedandaveragecanopycoverage(ACC)hadthehighestcorrelationwithyield.Theoverallgoalofourprojectistoselecthighyieldpotentialsoybeanlinesusingyieldperformancedatasupplementedbydrone-basedcanopydevelopmentdata,todemonstratewhethernewsourcesofdataandanalyticalapproachescanimproveselectionefficiency.AspecificobjectiveistoevaluatetheresponseofACCafterselection.Weeklymeasurementsofcanopyandyieldwereacquiredfor4,640progenyrowsand13checksand673linesand6checksinthe2015preliminaryyieldtrial(PYT).ThecanopytraitwasmeasuredbypercentageofcanopycoverageusingUASwithRGBcameras.Breeding

66


valueswereestimatedandlineswereselectedusingthreeselectioncriteria:yield,ACCandyieldgivenACC.Theselectedlinesweregrownin2016andcanopymeasurementswereperformedasdescribedabove,butwithoneadditionalflightaweek.Fortheprogenyrowderivedlines,theresponsetoselectionofACCwas21.6%andthecorrelatedresponseofACCwithselectionbasedonyieldandyieldgivenACCwere21.6%and20.4%,respectively.ForPYTderivedlinestheresponsetoselectionofACCwas16.8%andthecorrelatedresponseofACCwithselectionbasedonyieldandyieldgivenACCwere17.7%and17.7%,respectively.MechanismofheattoleranceevaluationinwheatthroughphenotypicparametersRenuMunjalandS.S.DhandaDepartmentofGeneticsandPlantBreeding,CCSHaryanaAgriculturalUniversity,HisarHaryana,India.Email:[email protected](TriticumaestivumL.,)differingintheirperformanceunderheatstresswasevaluatedundernormalandlatesownconditions.Dataondaystoheading(DH),yield,daystomaturity(DM),thetetrazoliumtriphenylechloridetest,leafmembranestability(%)andgrainyieldwererecorded.Heatstresshadasignificantimpactondaystoheading,asthemeanheadingdatewasabout11daysearlierunderheatstress(90.48±0.83days)thanundernormal(102.86±0.74days)conditions.GenotypesWH283andHD2329escapedheatstressbecauseoftheirearlyheadinginbothenvironments.ButgenotypesPBW343(87),WH730(87)andUP2425(85)escapedheatstressbyacceleratingtheirlifecycle.HeatstresshadcomparativelylittleinfluenceondaystoheadingingenotypesKanchan,PBW373,NIAW34,Pastor,GW173andKauz,whichalsoexcelledingrainyieldand/orheattoleranceparametersunderheatstress.Thismaybeattributedtotheirabilityeithertoavoidheatstressbyabsorbingmorewaterfromthesoilforcoolingthroughtranspirationorbyotherheat-protectivemechanisms.SignificantHeatSusceptibilityIndex(HSI)valuesandHeatResponseIndex(HRI)valuesforgenotypesWH730,WH533,Nesser,Raj3765andKauzsuggestedthattheheatstressperformanceofthesevarietiesmaynotonlyresultfromheattolerance,butalsofromheatescapeandhighyieldpotential.Similarly,highgrainyieldsofSeriandHUW234underheatstressmaybeattributedonlytoHRI,whilehighgrainyieldofPBW373mayonlybeduetohighyieldpotential,becausethisvarietydidnotmanifestanyescapemechanism.IdentificationofroottraitsthatcanserveassuitableselectiontargetstoenhancewinterwheatproductionintheSouthernGreatPlainsoftheUSAAnaPaez-Garcia1,JoshAnderson2,FrankMaulana2,XuefengMa2,andElisonB.Blancaflor11TheSamuelRobertsNobleFoundation.PlantBiologyDivisionand2ForageImprovementDivisionAgricultureintheSouthernGreatPlainsoftheUSAconsistspredominantlyofbeefcattleproductionsystems,supportedbyavarietyofforagesspeciesand,duringfalltoearlyspring,limitedgrazingsupplementedbyhay.Themajorgoalofplantbreedersistodevelopforagecultivarsthataretoleranttodifferentbiotic/abioticstressesthatallowincreasedgrazingtime.Weinitiatedastudytodetermineroot

67


traitsthatcontributetoenhancedforageproductivityunderthelow-inputagriculturalsystemsoftheSouthernGreatPlains.Oneapproachhasbeentocomparerootarchitecturaltraitsbetweencommonlyusedwinterwheatcultivars(e.g.Duster)andthosethatperformpoorly(e.g.Cheyenne).RootstudiesinthelaboratoryandgreenhouseshowedthatDusterhadlowertotalrootbiomassduetoslowerseminalandprimaryrootgrowthrates.DusteralsohadmoreshallowseminalandcrownrootanglesthanCheyenne,whoserootsgrewmoresteeply.Ourresultsindicatethatwheatcultivarswithreducedrootbiomass,shallowseminalandcrownrootanglesandslowseminalrootgrowthmeritconsiderationasselectiontargets.ThelowerrootbiomassobservedinDusterisconsistentwithprevioushypothesesthatplantsthatreducethemetaboliccostofsoilexplorationimprovetheirabilitytoacquirelimitedsoilresources.Theshallowseminalandcrownrootanglealsocouldenableplantstouseavailablesurfacewaterandimmobilephosphatepoolsmoreefficientlyduringearlyseedlingestablishment.Wearecurrentlyverifyingourresultsusingarangeofrootphenotypingsystemsinthelab,greenhouseandfield,andhaveexpandedourstudiesto200hardwinterwheatcultivarsfromtheTriticeaeCoordinatedAgriculturalProject(TCAP)plus47linesfromotherbreedingprograms.Ourpreliminaryresultsshowlargevariabilityinseedlingrootgrowthratesandanglesamongthewheatcultivars.Usingcultivarswithextremerootphenotypes,ourfuturestudieswillaimtocorrelateseedlingrootgrowthrateandanglewithfieldperformancetodetermineiftheseroottraitscanserveassurrogateselectiontargetsforsmallgrainsbreedingeffortsintheSouthernGreatPlains.High-throughputscreeningtoolsforidentificationoftraitscontributingtosalinitytoleranceKláraPanzarová1,MariamAwlia2,ArianaNigro3,JiříFajkus1,ZuzanaBenedikty1,MarkTester2,MartinTrtílek1

1PSI(PhotonSystemsInstruments),Drásov,CzechRepublic2CenterforDesertAgriculture,KAUST,SaudiArabia3InstituteofPlantBiology,UniversityofZürich,SwitzerlandSoilsalinityisakeystressaffectingtheworld’sagriculturallandsandplantssufferrapidgrowthreductionincludingslowerleafemergencewhentheirrootsareexposedtosaltstressinearlygrowthphases.Ourworkprovidesquantitativeinsightsintotheearlyphaseofsalinityresponseandarobustprotocolforhigh-throughput,image-basedanalysisofphenotypictraitsassociatedwiththeearlyphaseofsalinityresponse.Wetestedanon-invasiveprotocolbasedontechnologyfromPhotonSystemsInstruments(PSI,CzechRepublic)andautomatedintegrativeanalysisofphotosyntheticperformance,growthandcolorindexattheonsetandearlyphaseofsalinitystressresponseinsoil-grownArabidopsisthalianaecotypes.SalinitystressrapidlyandsignificantlyaffectedphotosystemIIefficiencyandimpactedthegrowthandgreeningindexofArabidopsisplants.ThePlantScreenTMplatformprovidesapowerfultooltodetectandselectmorphological,physiologicalandbiochemicalparameterstoidentifycomponentsthatunderlieearlyplantresponsestodiverseenvironmentalconditions.

68


CustomizingsoybeancultivardevelopmentthroughaerialandgroundphenotypingKyleParmley1,VikasChawla,AkintayoAdedotun,RaceHiggins,TalukdarJubery2,SayedVahidMirnezami2,NigelLee2,BaskarGanapathysubramanian2,*,SoumikSarkar2,*,AsheeshK.Singh1,*

1DepartmentofAgronomy,IowaStateUniversity,Ames,USA2DepartmentofMechanicalEngineering,IowaStateUniversity,Ames,USA*Correspondingauthor:KyleParmley([email protected])Asagronomicpracticescontinuetoevolve,physio-geneticparameters(i.e.,lightinterception,energyconversion,andbiomasspartitioning)mustbecustomizedintandemtoimprovetherateofyieldgain.Theobjectiveofthisresearchwastoidentifythe“drivers”ofyieldinmultipleenvironmentsandgenotypesusingaerialandground-basedhigh-throughputphenotypingforphysiologicaltraits(LAI,iPAR,canopytemperatureandhyperspectralindices).Thirty-twodiversesoybeangenotypeswereplantedintworow-to-rowspacingtreatmentsof15”and30”.Genotypesvariedinorigin(plantintroductions,diverseandelitegenotypes),growthhabit(indeterminateandsemi-determinate)andmaturitygroups(IIandIII).Threetypesofoptimalyieldresponseswereobserved:fitfor15”,fitfor30”andfitfor15”and30”rowspacing;thesedifferencescouldbeattributedtothephysiologicaltraits.Unliketraditional‘BlackBox’statisticalapproaches,wedeployedaProbabilisticGraphicalModelingmethodologytolinkexplanatoryvariableswithyield.Suchanapproachnaturallycapturesconditionalandcausaldependenciesacrossspatialandtemporalscales,whilealsoprovidingthecriticalabilitytoseamlesslyincorporatedomainknowledgeintothemodel.Weexploredhowtheinferenceprocesscanbeusedforpredictingandidentifyingdriversofyieldinamoremeaningfulmannersothatitcanbeappliedinselectionregimesofcultivardevelopmentprograms.Theseapproachesenableasystematicandrigorousmethodologytocustomizecultivardevelopment.Phenotypingandgeneticanalysisoflodging-relatedtraitsFranciscoJ.Piñera-Chavez1,2*,PeterM.Berry3,MichaelJ.Foulkes1,GemmaMolero2,SivakumarSukumaran2,MatthewP.Reynolds2

1DivisionofPlantandCropSciences,TheUniversityofNottingham,SuttonBoningtonCampus,Loughborough,LeicestershireLE125RD,UK

2InternationalMaizeandWheatImprovementCenter(CIMMYTInt.),Apdo.Postal6-641,06600MexicoDF,Mexico

3ADASHighMowthorpe,Malton,NorthYorkshireYO178BP,UKLodgingisanimportantproblemaffectinggrainqualityandgrainyieldinwheat.Theriskoflodginginwheatwillincreasewiththecurrentstrategyofseveralmultinationalinitiativesaimedatimprovingwheatproductivitybyraisingyieldpotential.Inthiscontext,increasingresistanceofplantsupportstructuressuchasstems(stembase)androots(plantanchorage)willbecrucial.Standardmethodstomeasurebiophysicalpropertiesofthestembaseandplantanchorage(stemandanchoragestrength)aretoolaborioustobeincorporatedinbreedingprograms.Theimplementationofrapidmethodologiesthatenableplantbreederstoselectforgreaterlodgingresistanceinwheatwillbeamajorbreakthroughthatwillhelpguaranteefuturegrainyieldincreases.Tosetascaleofworkthathastobedoneto

69


evaluatelodgingresistance,itisnecessarytomentionthat,intotal,standardmethodshavetomeasure14variableson10-15plantsperplot.Inaddition,eachplanthastobeextractedfromthefieldandwashedinthelaboratorytoremovesoilfromroots(crownroots),whichmeansthat100-120minutesareneededtoassessasingleplot.Optimizingstandardmethodsbyreducingthenumberofvariablesandplantsmeasuredperplothasreducedtheassessmenttimeto20-30minutesperplot.Thiscanbefurtherreducedifrootvariablesaremeasureddirectlyinthefieldwithoutremovingthesoil.However,themostpromissorystrategytobreedforlodgingresistanceisperhapstodevelopreliablemolecularmarkersthatcanbeusedinmarker-assistedselection(MAS).Inthisregard,progresshasbeenmadebyidentifyingQTLsassociatedwithstemandanchoragestrengthinUKreferencewheatpopulationAvalonxCadenza.PhenotypicscreeningofthispopulationusingoptimizedstandardprotocolsindicatedwidegeneticvariationandsignificantG×Einteractionsforlodging-relatedtraits.However,stemstrengthtraitscanbedescribedashighlyheritable,whilerootorrootanchoragestrengthtraitsarelessheritable.GeneticanalysishasidentifiedtwoQTLsforstemstrengthonchromosome3B(explaining44%ofphenotypicvariation)andtwoQTLsforanchoragestrengthonchromosome5B(explaining32%ofphenotypicvariation).Accordingtotheliteratureandthesefindings,itseemsthatregionsofchromosomes3Band5Bhavemajoreffectsonstemandanchoragestrength,respectively.However,furthervalidationand/orfinemappingwillbenecessarytodevelopmolecularmarkers.Phenotypingshowsassociationofhighseedyieldwithstemwatersolublecarbohydratesandchlorophyllcontentduringanthesisorgrain-fillinginwheatcultivarsunderheatstressHafeezurRehman1,AbsaarTARIQ1,MakhdoomHussain3,MatthewP.Reynolds21DepartmentofAgronomy,UniversityofAgriculture,Faisalabad,38040,Pakistan.Email:[email protected]

3WheatResearchInstitute,AyubAgricultureResearchInstitute,Punjab-Pakistan2CIMMYT,Int.,Apdo.Postal6-641,06600México,DF,MexicoNewwheatplanttypesadaptedtoheatordroughtrequirecoolercanopiesforremobilizingassimilatesefficientlyandtranslatingaccumulatedabove-groundbiomassintoachievableyield.Phenotypingpromisestohelpdissectgenotype-by-environmentinteractionofgeneticresourcesforthesetraits.Thepresentstudyevaluatedsevendifferentwheatcultivarsfortillering,numberofgrainsperspike,thousand-grainweight,spikedensity,biomassandgrainyield,harvestindexandphysiologicalmaturity.Phenotypingforcanopytemperature,stemwatersolublecarbohydratesandstay-greentraitswasperformedatanthesisorgrain-fillingtodeterminetheirpotentialassociationwithyieldtraits.CultivarsSehar-2006,Faisalabad-2008,AARI-2011,Inqilab-91,Pasban-90,Lasani-2008andShafaq-2006withrestrictedmaturityandheattolerancewerecomparedwithatemperatesowncrop.Heatstressreducedthenumberoftillersperplant,thousand-grainweightanddaystomaturityinthetestcultivarsbuttheyexpressedhighseedyield,harvestindex,spikeindexandwatersolublecarbohydratesatanthesisascomparedtothetemperatesowncrop.However,nodifferencesinbiomassyield,numberofgrainsperspikeandchlorophyllcontentduringgrain-fillingwerefoundinbothenvironmenttypes.Underheatstress,cultivarsSehar-2006,Inqilab-91andShafaq-2006hadthehighestincreaseinseedyield(20%),harvestindex(31%)andspikeindex(133%)comparedtothetemperatesowncrop.Stemwatersolublecarbohydratesatanthesisandflagleafchlorophyllcontentatgrain-filling

70


increasedby24%and20%,respectively.Thesecultivarsalsomaturedearlier(15-17days)andhadlowercanopytemperatureduringgrain-fillingunderhighheatstressthanthetemperatesowncrop.Nonetheless,thehighseedyieldobservedincvs.Sehar-2006,Inqilab-91andShafaq-2006wasassociatedwithhighchlorophyllcontent(R2=0.41)duringgrain-fillingandstemwatersolublecarbohydrates(R2=0.54)atanthesis.Likely,associationofwatersolublecarbohydratesatanthesiswaspositive(R2=0.30)withchlorophyllcontentatgrain-filling.Insummary,wheatcultivars(Sehar-2006,Inqilab-91andShafaq-2006)withearliermaturity,highseedyield,stemwatersolublecarbohydratesandstaygreentraitscanbepromisingonestobetestedforheatadaptationandstabilityacrossdifferentheatenvironmentsandutilizationinphysiologicalbreedingforfutureclimatechange.Phenotypingtoolsandphysiologicalbreeding:OptimizingbiomassdistributionwithintheplanttoincreaseharvestindexinwheatcultivarsCarolinaRivera-Amado1,2,GemmaMolero1,EliseoTrujilloNegrellos2,JohnFoulkes2,MatthewReynolds1

1CIMMYT,GlobalWheatProgram,México,email:[email protected],UniversityofNottingham,LE125RD,UKConversionofinterceptedradiationintobiomassisakeymajorphysiologicalprocessdeterminingproductivityinyieldpotentialenvironments.Currentstrategiesforyieldgeneticimprovementhaveastrongbasisonimprovingphotosyntheticcapacityandefficiencyforincreasedbiomassproduction.Whileallocationofcarbontothedevelopingwheatspikedeterminesgrainsinkstrength,concurrentgrowthofotherplantorganscompetesforcarbon.Therefore,itiscrucialtoidentify‘useful’biomasstraitsthatenablebreederstomaximizeassimilatepartitioningtothegrainsfromexistentandfurthergainsinbiomass.Geneticvariationforstructuralandnon-structuraldrymatter(DM)partitioningtothestem,sheathandspikesevendaysafterfloweringwasobservedin26CIMMYTspringwheatcultivarsandadvancedlinesunderhighradiation,irrigatedconditionsinnorthwesternMexicoduring2011-12and2012-13.Furthermore,DMpartitioningwasassessedinthesteminternodesandamongthenon-grainspikeDM(rachis,glume,palea,lemma,awn)atharvest.ResultsshowedthatlowerstructuralDMpartitionedtothestemwasassociatedwithgreaterspikepartitioningindex(SPI;spikeDM/abovegroundDMatanthesis+7days)atsevendaysafteranthesis,potentiallyincreasinggrainnumberperunitareaatharvest.ResultsalsoshowedatrendtowardsapositiveassociationbetweenSPIandharvestindex(R2=0.16,P<0.05).Moreover,strongnegativeassociationsbetweenSPIandspikeDMperunitareaandsteminternode2(peduncle-1)and3(peduncle-2)DMpartitioningwerefound(P<0.05),buttherewerenoassociationswithotherinternodes.Resultsindicatedassociationsbetweenspikemorphologicalcharacteristicsatharvestandfruitingefficiency(FE;grainspergofspikeDMatflowering).Accordingtoourresults,avalueofHI>0.6couldbeachievedinCIMMYTspringwheatbycombiningthebiggestexpressionfor‘useful’biomasstraits.

71


PhenotypingtoolstodissectmorphologicaldiversityinsweetpotatoAmparoRosero1,LeiterGranda2,José-LuisPérez1,DeisyRosero3,WilliamBurgos-Paz1,RembertoMartínez1,IvánPastrana11ColombianAgriculturalResearchInstituteCORPOICA-Turipana,Colombia.Email:[email protected]

2DepartmentofCropScience,BreedingandPlantMedicine,MendelUniversityinBrno,CzechRepublic3FacultyofAgriculturalScience,NationalUniversityofColombia-Palmira,ColombiaThegeneticdiversityofsweetpotato(Ipomoeabatatas(L.)Lam)anditswildrelativeshasbeencollectedandconservedingermoplasmcollectionsworldwideandexploredbyseveraltools.Characterizationofcropandwild-relateddiversitythroughmorphologicalandmoleculartoolsproducesusefulinformationforidentifyinggenotypeswithdesirabletraitsforuseinbreeding.However,characterizationusingmorphologicaldescriptionpresentslowlevelsofpolymorphismaffectingtheestimationofdiversity.Newphenomicapproacheswereusedtoincreaseefficiencywhendeterminingpolymorphismsnotdetectedbymorphologicaldescriptors.SeventyaccessionsofsweetpotatocollectedonthenortherncoastofColombiawerecharacterizedusing49parametersfromsweetpotatodescriptors(Huamán1991)anddataobtainedbyRGBimagingandcolorimetryanalysis.Fielddescription,RGBimaging,colorimetryandtheircombineddatabaseswereanalyzedusingGower’sgeneralsimilaritycoefficientforclusteringinR.EstimationofgenotypesimilaritywassignificantlyimprovedwhenquantitativedataobtainedbyRGBimagingandcolorimetryanalysiswereincluded.Variationsintraitssuchasfleshandperidermcolorinroots,leafveincolorandleafshape,whichwerenotdetectedbyfielddescriptors,wereefficientlydiscriminatedbymeasuringpixelvaluesfromimages,estimatedshapedescriptors(circularity,solidity,area)andcolorimetrydata.Asexpected,highcorrelationswerefoundforfieldparameters(numberoflobes,lobetype,centrallobeshape)andimagedata(circularityandsolidity).However,visualcolorparametersshowedlowcorrelationwithpixelvaluesandcolorspaceparameters,confirmingtheirlimitationswhenusedsolelyformorphologicalcharacterization.CombiningRGBimagingandcolorimetrybenefitsthequalityofmorphologicalcharacterizationduetotheuseofquantitativeinsteadofsubjectivequalitativemeasurements,resultinginacost-effectiveprocessabletoidentifypolymorphismsandtargettraitsfordiversityestimationandbreeding.

72


NDVIvaluesandgrainmineralcontentpredictthepotentialofsyntheticspringwheatandwildrelativesA.I.Abugaliyeva1,A.I.Morgounov2,K.Kozhakhmetov1,T.V.Savin1,A.S.Massimgaziyeva1,A.Rsymbetov1,3

1KazakhResearchInstituteofAgricultureandPlantGrowing,Almaty,Kazakhstan2CIMMYT,Ankara,Turkey3KazakhNationalAgrarianUniversity,Almaty,KazakhstanThedynamicsofbiomassaccumulationasreflectedbyNormalizedDifferenceVegetationIndex(NDVI)valuesdemonstratethereactionofgenotypestostressconditions(hightemperature,moisturestress,etc.).OurdatashowthatwheatwildrelativesdonotsharplyreduceNDVIunderstressandarecharacterizedbylesssharpcurveduringvegetativegrowth.ModernspringwheatvarietiesshowmuchhighervariabilityofNDVIvaluesduringvegetativegrowth.Syntheticspringwheatgenotypesreactmorespecificallytoabioticstressesthanvarietiesandwildrelatives.ThecriterionforselectionusingphysiologicaltraitswouldbeahighNDVIcurvewithslowreductionafterheading.NDVIvaluesrangedfrom0.19to0.77forvarieties,wildrelativesandsynthetics.HighNDVIvaluescorrelatedwithgrainyieldexceeding5t/hainthebestsyntheticlines.Evaluationofgrainmineralcontentrevealedgenerallyhighervaluesinwildrelatives,especiallyAe.ovataandAe.triuncialcomparedtomodernvarieties.ThemaximumvalueswererecordedforT.kiharae(N,Mg,Mn,Fe,Zn),T.militinae(N,P,S);T.compactum(K,Zn)andT.petropavlovskye(Mn,Fe,Zn).Amongsynthetics,thefollowinglineshadthehighestmineralcontent:Kazakhstanskaya10xT.dicoccum(K,P,Ca,Mg);Kazakhstanskaya10хT.timopheevi(N,S,Fe,Zn,Mg,Mn);andKazakhstanskayarannespelayaхT.timopheevi(Fe,S).ScontentinsyntheticsdoesnotexceedScontentinvarietiesbutlineZhetisuxT.militinaehadthehighestvalueincludingforCa,Fe,ZnandMn.Top-crosstestsindicatedthesetraitsweretransferredtothreebreedinglines:KazakhstanskayarannespelayaхTr.timopheevi;6625xT.timopheeviand6583хT.timopheevi.Globalnetworkforprecisionfield-basedwheatphenotypingCarolinaSaintPierre1,AmorYahyaoui1,PawanSingh1,MatthewReynolds1,MichaelBaum2,HansBraun11CIMMYT,GlobalWheatProgram,Mexico.Email:[email protected],MoroccoTheCGIARResearchProgramonWHEATenvisionsanetworkofprecisionfield-basedwheatphenotypingplatformsdevelopedwithco-investingnationalagriculturalresearchsystems.Themaingoalofthisnetworkistogeneratehigh-qualitydatatoassistplantbreedersindevelopingresistant,highyieldingwheatvarietieswithabroadgeneticbase,bymaximizingthepotentialofnewgenotypingtechnologies.Toinitiatethisnetwork,fiveagreementsweresignedwithnationalpartnerstosetupfacilitiesandprotocolsforevaluatingheatstressinSudan,multiplediseasesinUruguay,SeptoriatriticiblotchindurumwheatinTunisia,wheatblastinBoliviaandyellowrustinTurkey.AdditionalplatformsareplannedtophenotypeforSeptoriatriticiblotchinbreadwheat(Ethiopia),wheatrusts(Ethiopia),Helminthosporiumleafblight(Bangladesh/Nepal),Fusariumheadblight(China),heatanddrought

73


toleranceinspringwheat(Morocco,India,Pakistan,Bangladesh,Nepal),heatanddroughttoleranceinfacultative/winterwheat(Turkey)andyieldpotential(China,India,Pakistan,Egypt,Zimbabwe).AllplatformswilloperateinclosecollaborationwithcurrentlyfunctioningplatformsinKenyaandMexico.Interdisciplinaryteamswillpromotetheuseofprecisephenotypingapproaches,standardizedprotocolsandnoveltoolstoacceleratesuperiorgermplasmdevelopmentanddissemination.RootcorticalsenescenceinfluencesmetaboliccostsandradialwaterandnutrienttransportinbarleyHannahM.Schneider1,TobiasWojciechowski1,JohannesA.Postma1,DagmarvanDusschoten1,JonathanP.Lynch2

1ForschungszentrumJülich,IBG-2,Jülich,Germany.Email:[email protected],UniversityPark,PA,USARootcorticalsenescence(RCS)isatypeofprogrammedcelldeathincorticalcellsofmanyPoaceaespecies.ThefunctionalimplicationsofRCSformationarepoorlyunderstood,butstudiessuggestthatRCSformationconfersbothbenefitsandcosts.Theobjectivesofthisresearchweretotestthehypothesesthat:(1)thereisgeneticvariationforRCS;(2)RCSreducesthemetaboliccostofroottissue;(3)RCSdecreasesradialwaterandnutrienttransport.UsingaPitmanchamber,radialwaterandnutrienttransportweremeasuredonexcisedrootsofbarleyusingstableandradioactiveisotopes.LandraceshadgreaterRCSformationthanmoderngenotypes.Nitrogen-andphosphorus-deficientconditionsincreasedtherateofRCSdevelopmentinalllines.RCSdecreasesthemetaboliccostofroottissue:RCSreducedrootnitrogencontentby66%,phosphoruscontentby63%,andrespirationby87%comparedtorootsegmentswithnoRCSofthesamelength.OlderrootsegmentswithcompleteRCShad90%lessradialwatertransport,92%lessradialnitratetransport,and84%lessradialphosphorustransportcomparedtoyoungerrootsegmentswithnoRCS.RCSwasassociatedwith30%greateraliphaticsuberincontentintheendodermis.RCSmaybeausefuladaptationtodroughtbyreducingthemetaboliccostsofsoilexploration.AsRCSprogresses,fewermetabolicresourcesneedtobeinvestedincorticalmaintenance,whichwouldpermitgreaterresourceallocationtothegrowthofshoots,otherroots,andreproduction.ReducedhydraulicconductivityinducedbyRCSmayalsobeadvantageousunderdroughtconditionsbecauseitpreventsdesiccationoftheroottipandsurroundingsoil.TheseproposedmeritsofRCSunderedaphicstressneedfurtherinvestigationunderfieldconditions.

74


Nutrient-relatedtraitsforimprovedgrowthandgrainqualityinIndianwheatJaswantSingh1,LolitaWilson1,ScottD.Young1,SindhuSareen2,BhudevaS.Tyagi2,IanP.King1,MartinR.Broadley1

1SchoolofBiosciences,UniversityofNottingham,SuttonBonington,Loughborough,LE125RD,UK.Email:[email protected]

2IndianInstituteofWheat&BarleyResearch,POBox158,Karnal-132001,IndiaWheatgrainmicronutrientcontent(e.g.,ironandzinc)needstobeofsufficientqualityforhumanhealth,especiallyindevelopingcountries.Itisnotcleariffurtheryieldandqualityimprovementscanbeachievedduetothenarrowgeneticpotentialofmodernwheatcultivars,andtolimitedlandavailability(e.g.,manysoilsaremarginalduetosalinity,pH,andmineralnutrientimbalances).Thisstudyquantifiedyield,yieldcomponentandmineralqualitytraitsindiversewheatgenotypes,includingIndianvarieties,wildrelativesandlinescontainingwildwheatintrogressions.FieldexperimentswereconductedatsixdiversesitesinIndiaonhostilesoilswithcontrastingmineralstresses(pH4.5-9.5)overtwoyears(2013/14and2014/15).Traitsmeasuredincludedyieldandyieldcomponents,andgrainmineralcompositiondeterminedusinginductivelycoupledplasma-massspectrometry(ICP-MS).Meangrainyieldrangedfrom1.0to5.5tha-1onhostileandnon-hostilesoils,respectively.ThemeangrainFeconcentrationrangedfrom25-39mgkg-1andgrainZnconcentrationrangedfrom25-37mgkg-1acrossthesixsites.Site(E)hadthelargesteffectonyieldandgrainmineralcompositiontraits;however,thereweresignificantgenotype(G)andgenotype×environment(G×E)interactions,whichimpliesthereisscopeforgenericselectionwithinandbetweenmajoragro-ecologicalzones,ascurrentlypracticed,andalsoforsite-specificselectionifthephenotypingpipelinesaresufficientlycost-effective.Large-scalephenotypingfornext-generationwheatvarietalimprovementSukhwinderSingh1,PrashantVikram1,CarolinaSaintPierre1,JuanAndersBurgueño1,HuihuiLi1,CarolinaSansaloni1, Deepmala Sehgal1, Sergio Cortez2, G. Estrada Campuzano3, N. Espinosa4, Pedro Figueroa4,Guillermo Fuentes4, C.G.Martínez3, Ernesto Solís Moya4, H.E. Villaseñor4, Victor Zamora5, Ivan Ortiz-Monasterio1, Carlos Guzmán1, Cesar Petroli1, Gilberto Salinas1, Thomas Payne1, Kate Dreher1, RaviPrakashSingh1,VelluGovindan1,MathewReynolds1,PawanSingh1,JoseCrossa1andKevinPixley11CIMMYT,Mexico;GeneticResourceProgram.Email:[email protected]écnicadeFranciscoI.Madero,México3UniversidadAutónomadelEstadodeMéxico,México4InstitutoNacionaldeInvestigacionesForestales,AgrícolasyPecuarias,INIFAP,México5UniversidadAutónomaAgrariaAntonioNarro,MéxicoOn-farmandgenebankwheatdiversityrepresentanimportantresourcetospeedadvancesinyieldpotential,addressclimatechangeeffectsthroughbreeding,meetexpectedfooddemandincreasesandbroadenbreedinggermplasmpool.ThroughtheCIMMYT-led“SeedsofDiscovery”(SeeD)project,wehavewehaveconductedphenotypingandgenotypiccharacterizationofthecenter’smorethan140,000wheatgenebankaccessions:70,000forheatand/ordrought,20,000forgrainquality,10,000fordisease

75


resistance,2,000forPUEand900syntheticwheatsforyieldpotential.Usefultraitdonorsidentifiedarebeingincorporatedintopre-breedingprograms,followingatwo-tierstrategy:(1)incorporatinggeneralgeneticdiversityand(2)mobilizingtheidentifiedtraitdonoraccessions.Morethan1,000exoticwheatlineshavebeenmobilizedinthebreedingpipeline.Advancedpre-breedingsetsarebeingevaluatedinMexicoandtargetenvironmentsinIndia,Iran,andPakistan.Wheatlandracecoresubsetshavebeenformulatedusinglarge-scalephenotype-genotypedataandleveragedwithnationalresearchprogramsinAfrica,SouthAsiaandtheUSA.UsingshovelomicstoidentifyrootingtraitsforimprovedwateruptakeunderdroughtinawinterwheatdoubledhaploidpopulationShaunaghSlack1,LarryM.York1,2,MalcolmBennett1,JohnFoulkes11CentreforPlantIntegrativeBiology,SchoolofBiosciences,UniversityofNottingham,SuttonBonington,UnitedKingdom.Email:[email protected]

2DivisionofPlantSciences,UniversityofMissouri,Columbia,MOWatercaptureinwheatunderdroughtdependsonrootsystemarchitecture(RSA)traitsthatfacilitaterootforaginginsoil.RootphenotypingisbeingusedtostudygeneticvariationforRSAandlinkthisvariationtofunctionalutility.Herewedescribetherootcrownphenotypingofawheatmappingpopulationusingahigh-throughputshovelomics-basedapproach.FieldexperimentswerecarriedoutoverthreeyearsonadroughtproneloamysandsoilattheUniversityofNottinghamfarmon94linesoftheSavannahxRialtodoubled-haploidwinterwheatpopulationunderirrigatedandrainfedconditions.Rootcrownswereexcavatedfromthefieldandwashedfreeofsoil.Thewholerootcrownwasimaged,thensplitintothemainshootandtillerrootsystemswhichwereimagedseparately.Customsoftwarewasusedtomeasurepropertiessuchascrownrootnumber,angle,diameterandrootsystemwidthforallrootsystems.Soilcoringof16DHlinesvalidatedthatrootcrownphenotypespartiallydeterminerootlengthdensityatdepth.SignificantgeneticvariationwasfoundforRSAunderbothirrigatedandrainfedconditionsandQTLwereidentified.ShootandrootphenotypingofspringwheatunderwaterloggedconditionsinfieldandrhizotronexperimentsT.Sundgren1,A.K.Uhlen1,T.Wojciechowski2,W.Waalen3,M.Lillemo11NorwegianUniversityofLifeSciences,Norway.Email:[email protected]ülich,Germany3NorwegianInstituteofBioeconomyResearch,NorwayImprovingthewaterloggingtoleranceofwheatvarietiescouldalleviateyieldconstraintscausedbyexcessiverainandpoorsoildrainage.Reliablescreeningmethodsandaccuratephenotypingprotocolsarefundamentalforachievinggeneticimprovement.In2013-2014,177wheatgenotypeswerephenotypedforwaterloggingtoleranceinNorwegianhillplotfieldtrialswithcontrolledwaterlogging

76


treatments.Tolerancepropertiesweredeterminedusingtraditionalphenotypingmethodssuchastraitscoringandcountscombinedwithprincipalcomponentanalysis.In2015and2016,12and16genotypeswithcontrastingtolerance,respectively,wereselectedforlargerscaleexperiments.Estimatesofphenotypictraitsfrom2015wereanalyzedinsingle,multipleandprincipalcomponentregressionanalyseswithyieldastheresponsevariable.Bestmodelfitwasobtainedbythepercentageofchlorosis(R2=0.87).Imageanalysis(RGBandNDVI)fortolerancecharacterizationiscurrentlyunderinvestigation.Resultsofthefieldexperimentsindicateddifferentadaptationstrategies.Forfurtherinvestigation,arandomizedsplit-plotrhizotronexperimentincluding6contrastinggenotypeswasconducted.Imagesofrootsandshootsweretakeneveryseconddaytostudythestressresponseofwaterloggedandcontrolplantspriorto,duringandaftera7-daywaterloggingtreatment.Resultsshowedthattolerantgenotypeshadsignificantlyhigherseminalrootelongationratepriortothetreatment.Nodalrootsofthesegenotypesalsoelongatedatasignificantlyhigherrateduringthetreatment.Allgenotypesresumedseminalrootgrowthpost-treatment.Thepresenceofaerenchymainrootsamplesiscurrentlybeingexamined.EffortstoidentifyQTLassociatedwithwaterloggingtoleranceunderfieldconditionsareongoing.Predictingsorghumbiomassusingaerialandground-basedphenotypesAddieThompson1,KarthikeyanRamamurthy2,ZhouZhang3,FangningHe3,MelbaMCrawford3,AymanHabib3,CliffordWeil1,MitchellR.Tuinstra11Agronomy,PurdueUniversity,WestLafayette,IN2IBMResearch,YorktownHeights,NY3Engineering,PurdueUniversity,WestLafayette,INInacropsuchasbioenergyorforagesorghum,above-groundplantbiomassisconsideredthemeasureofyield.However,determiningbiomassbywaitinguntiltheendoftheyeartodestructivelyharvestplotswithabiomassharvesterdoesnotallowselectioninabreedingprogrambeforefloweringandcrossing,therebydelayingbreedingcycles.Early-seasondestructivesamplingisoneoptionthatallowsselectionbeforethesorghumflowers;however,removingasubsetofplantsdecreasestheaccuracyofthebiomassestimateaswellasthenumberofplantsavailableforbreeding(orincreasesthetotalexperimentsizeneededtoaccountfortheselosses),andeliminatestheoptionoflaterphenotypingontheremovedmaterial.Lessdirectmeasurementsofplantbiomasscanbecalculatednon-destructively,suchastheapproximationofstemvolumederivedfromplantheightwithtopandbottomstemdiameters.Thisapproachcanprovidefairlyaccurateprediction,demonstratedhereacrossmultipletimepointsinthegrowingseason.Thesemeasurementscanbetime-consumingandlabor-intensivetotakebyhand,butadvancesinremotesensingtechnologiesallowalargenumberofvarietiestobescreenedfromaerialplatforms.Wecompareandcombineresultsforestimatingend-of-seasonbiomassusingremotelysensedplantheight(derivedfromRGBpointclouds)andhyperspectralfeatures(includingExtendedMulti-AttributeProfiles,orEMAPs),pairedwithextensiveground-basedphenotypingtoprovideabenchmarkforourresults.Overall,thisstudyhighlightstheusefulnessofacombinationofplatformsforpredictingend-of-seasonbiomassinforageorbioenergycrops.

77


Alterationsinrootproteomeofsalt-sensitiveandsalt-tolerantbarleylinesundersaltstressconditionsStanisławWeidner1,AgnieszkaMostekA.1,AndreasBörner2,AnnaBadowiec1

1FacultyofBiologyandBiotechnology,UniversityofWarmiaandMazury,Olsztyn,Poland.Email:[email protected]

2LeibnizInstituteofPlantGeneticsandCropPlantResearch,GermanySalinityisoneofthemostimportantabioticstressesthatcausesignificantreductionsofcropyields.Togainabetterunderstandingofsalinitytolerancemechanismsinbarley(Hordeumvulgare),weinvestigatedthechangesinrootproteomeofsalt-sensitive(DH14)andsalt-tolerant(DH187)linesinresponsetosaltstress.Theseedsofbothbarleylinesweregerminatedinwaterorin100mMNaClforsixdays.Therootproteinswereseparatedbytwo-dimensionalgelelectrophoresis.Toidentifyproteinsregulatedinresponsetosaltstress,MALDI-TOF/TOFmassspectrometrywasapplied.Itshowedthatsensitiveandtolerantbarleylinesresponddifferentlytosaltstress.Themostsignificantdifferencesconcernedproteinsinvolvedinsignaltransduction(annexin,translationally-controlledtumorproteinhomolog,lipoxygenases),detoxificationandproteinfoldingprocesses(osmotin,vacuolarATP-ase,proteindisulfideisomerase)whichwereup-regulatedonlyinthetolerantlineundersaltstress.TheresultssuggestthattheenhancedsalinitytoleranceoflineDH187resultsmainlyfromincreasedactivityofsignaltransductionmechanisms,whicheventuallyleadstotheaccumulationofstressprotectiveproteins.

Apdo. Postal 6-641 CDMX, Mexico 06600Email: [email protected]

www.cimmyt.org

Apdo. Postal 6-641 CDMX, Mexico 06600 Email: … · Kang Yu, Achim Walter, Andreas Hund 7 ......

Documents

Transcript of Apdo. Postal 6-641 CDMX, Mexico 06600 Email: … · Kang Yu, Achim Walter, Andreas Hund 7 ......