智慧农业英语 课件 Unit2 Intelligent seed breeding

智慧农业英语EnglishforSmartAgriculture2UnitIntelligent

seedbreeding智慧农业英语EnglishforSmartAgricultureBytheendofthisunitonsmartplantprotection,studentswillbeableto:OutlinetheapplicationsofAIincropandlivestockbreedingandsummarizetheconcept;ExplainhowAIcanbeintegratedwithgeneticsandgenomicstoenhanceefficiencyandprecisionofbreeding;DemonstrateChinesescientists’breakthroughingenomicresearchoffoxtailmilletanditsglobalimpact;Createabi-foldpromotionalbrochureforanewcherrytomatovarietydeveloped

throughAl-poweredbreeding.Learning

objectives智慧农业英语EnglishforSmartAgricultureCONTENTSUnlocking

thetopicExploringworldagriculturePresentingChina'sinitiativesDecodingthefutureoffarmingPioneeringsolutionsforsustainabilityImaginedesigningaclimate-resilientcropinyears,notdecades.ThisisthepromiseofAI-poweredbreeding,arevolutiontransformingagricultureinthefaceofclimatechangeandagrowingpopulation.ByintegratingAIwithgenetics,scientistscannowpinpointidealgenesandpredictplantperformancewithunprecedentedspeed,creatingsmarter,moresustainablecrops.Inthisunit,youwillexplorehowthesecutting-edgetoolsturndataintosolutions.Attheendofthisunit,youwillstepintotheroleofabreederandcreateabrochureforanewAI-bredcherrytomatovariety,introducing

itsuniquebenefitstotheworld.SettingthesceneActivatingsubjectknowledgeScanthecodeandcompletetheagriculturalvocabularyexerciseonUcampus.Wordbankbreedern.

育种者genomicsn.

基因组学resistancen.

抵抗力barleyn.

大麦traitn.性状genetica.

基因的variationn.

（相似物体之间的）差异progeny

n.子代Watchthevideoclipandcompletethetablewithwhatyouhear.(Withsubtitles)(Withoutsubtitles)InstitutionTheQueenslandAllianceforAgricultureandFoodInnovationBreeders’workCollectingandturningdatapointsfrom

(1)_______________________________intoknowledgetofacilitatedecision-makingincropandlivestockbreedingprograms.phenomics,genomics,multiomics

ApplicationsofAIinbreedingWorkingoutwhattobreedfor.Cropsandlivestockneedtobemore(2)_____________inthefaceofglobalwarming.Capturinggeneticvariations.Imagesoflinesandanimalscanbecollectedonalargescaleandatalowercost,enablingtheidentificationof(3)______________tobreedfor.heat-tolerant

certaintraitsApplicationsofAIinbreedingIII.Predictinghowgoodavarietyis.AIisusedtocollectinformationofgeneticmarkers,metabolicmarkersor

(4)____________________.proteomicmarkersApplicationsofAIinbreedingIV.Optimizingbreedingstrategies.Insteadofviewingabreedingpopulationasindividuallineswithgeneticpotential,itisnowseenasagroupof(5)________________,chromosomesegments.AIhelpsidentifyspecificchromosomesegmentsanddeterminetheoptimal(6)_______________betweenindividuallines.chunksofDNAcrossingpathSettingthesceneActivatingsubjectknowledgeScanthecodeformorecomprehensionexercisesonUcampus.Participateinarole-playconversationbetweentwoprofessionalsfromdifferentculturalbackgrounds.TopicTheapplicationofAIinbreedingRolesStudentA:AprofessorleadinganintelligentagricultureresearchgroupataChineseuniversity,focusingondevelopingacropmulti-omicsdatabase.StudentB:AprofessorfromtheQueenslandAllianceforAgricultureandFoodInnovationattheUniversityofQueensland.StepsThinkaboutwhatyouwanttoaskorshareintheconversation.StudentAshouldprepareseveralquestionsrelatedtothegiventopic.StudentBshouldprepareanswerstoStudentA’squestionsbasedoninformationfromthevideoandyourownunderstandingofsmartagriculture.DiscussyourinitialideaswiththeAItutoronUcampus.ChooseyourroleandstarttheconversationwiththeAItutor.Makesuretoaskoranswerquestionsclearlyandprovidespecificdetails.Reviewthereal-timefeedbackfromtheAIspeakingtoolaftertheconversationandimproveyourperformanceaccordingly.Switchrolestoexplorethealternativeperspective.

ScriptsI’mProfessorLeeHickey,themeleaderofpredictiveagriculturehereattheQueenslandAllianceforAgricultureandFoodInnovationwithintheUniversityofQueensland(UQ).Breedersaroundtheworldworkingonplantsandanimalsarecollectingbillionsandbillionsofdatapoints,everythingfromphenomics,genomics,multiomics.

It’sallaboutturningthesebillionsofdatapointsintoknowledgetosupportbetterdecision-makinginplantandanimalbreedingprogramstoacceleratethedevelopmentofhigher-performing,moresustainableandprofitablecropsandanimalsforthefuture.ThroughseveralGRDCinvestments,wehaveanexcitingpackageofresearchthatwe’reundertaking.We’reintegratingartificialintelligenceandspeedbreedingtorapidlystackmultiplediseaseresistanceinbothwheatandbarley.There’swideapplicationofthistechnology.We’reworkingonwheatandbarleyforthesetools.Butreally,they’reuniversalintermsofapplicationsacrossallplantsandanimalsbecausethegoalofthebreederisthesame.It’sallaboutbringingtogethertraitsasquicklyaspossibleinanimprovedpackageforafarmertogrow.Forthelast10years,I’veinvestedalotoftimetodevelopprotocolsforspeedbreedingandcommunicatethebenefitsofspeedbreeding,andworkwithbreedingprogramsaroundtheworldtoestablishthetechnology.Ithinkthisisjustthebeginning.Integratingspeedbreedingwithgenomicsandinparticular,AI,wherewetapintothesebigdatasets,isthenewfrontierforplantandanimalbreeding.I’mProfessorBenHayesfromtheQueenslandAllianceforAgricultureandFoodInnovationhereatUQ.So,weseefourapplicationsforAIincropandlivestockbreeding.ThefirstapplicationofAIisworkingoutwhattoactuallybreedforinourcropsandlivestock.It’sbeenallaboutincreasingproductivityandyields.Butinthefuture,weneedourvarietiesorouranimalstobemoreheat-tolerant,forexample,inthefaceofclimatechange.Wealsoneedtopayincreasingattentiontoconsumers’preferencesforthefoodthatwe’reproducing.TheseconduseofAIisalreadywellunderwayandthat’susingimagesthatcanbecapturedonaverylargescaleandverycheaplytocapturegeneticvariationbetweenlinesoranimalsincertaintraitswewanttobreedfor.ThethirdareawhereAIcanbeused,whichwe’reactivelyusingaswell,istotakegeneticmarkersormetabolomicmarkers,proteomicmarkersandusethatinformationtopredicthowgoodavarietyisgoingtobeforbreeding.Thefourthapplicationistheonewe’rereallyexcitedabouthereatUQ,

andthat’sreallyshiftingthewaywethinkaboutbreedingfromlookingatthegeneticpotentialofindividuallinesforbreeding,breedinglotsofprogenyfromthose,toasystemwherewethinkaboutabreedingpopulationasagroupofchunksofDNA,chromosomesegmentsthatarereallygoodforatrait.ButwhatyoucanuseistheAIbothtofindthatchromosomesegmentandthentoworkoutthecrossingpathbetweentheindividualsthatgetsyoutotheultimatestackofchromosomesegments.Andthat’swherespeedbreedingcomesintoplay.Speedbreeding,youcanturnovermultiplegenerationsveryquickly.Soyoucanusethoseormakethosecrossesthatyouneedtomaketogettheultimategenotype,whereyouhaveallthegoodchromosomesegmentscomingtogetherveryquickly.AI-poweredcropbreedingforaclimate-resilientfuture1Untilrecently,thefieldofplantbreedinglookedalotlikeitdidincenturies

past.Abreedermightexamine,forexample,whichtomatoplantswere

mostresistanttodroughtandthencrossthemostpromisingplantstoproducethe

mostdrought-resistantoffspring.Thisprocesswouldberepeated,plantgeneration

aftergeneration,

until,overthecourseofroughlysevenyears,thebreederarrived

atwhatseemedtheoptimalvariety.2Now,withtheglobalpopulationexpectedtoswelltonearly10billionby2050,

andclimatechangeshiftinggrowingconditions,cropbreedersandgeneticists

don’tthinktheyhavethatkindoftime.Tospeeduptheprocess,theyareturning

toAI.Usingcomputersciencetechniques,breederscanrapidlyassesswhich

plantsgrowthefastestinaparticularclimate,whichgeneshelpplantsthrivethere,

andwhichplants,whencrossed,producethebestcombinationof

genesforagiven

location,optingfortraitsthatboostyieldandstaveofftheeffectsofachanging

climate.Withcomputingpowerrapidly

advancing,thetechniquesarenowpoisedto

acceleratebreedingonabroaderscale.3AIisnot,however,acure-allsolution.Cropbreedersstillgrapplewithtrade-offs

suchashigheryieldversusmarketableappearance.AndeventhemostsophisticatedAIcannotguaranteethesuccessof

anewvariety.ButasAIbecomesintegratedintoagriculture,somecropresearchersenvisageanagriculturalrevolutionwithcomputerscienceatthehelm.4Duringthegreenrevolutionofthe1960s,researchersdevelopednewchemical

pesticidesandfertilizersalongwithhigh-yieldingcropvarietiesthatdramatically

increasedagriculturaloutput.Buttherelianceonchemicalscamewiththeheavy

costofenvironmentaldegradation.“Ifwe’regoingtodothissustainably,genetics

isgoingtocarrythebulkoftheload,”saysStevenTanksley.5Asthegenomicsrevolutionunfoldedintheearly2000s,plantbreedersfound

themselvesinundatedwithgenomicdatathattraditionalstatisticaltechniques

couldn’tgrapplewith.Plantbreeding“wasn’tgearedtowarddealingwithlarge

amountsofdataandmakingprecisedecisions,”saysTanksley.6Fromthe1990s,TanksleybeganchairingacommitteeatCornellthataimedto

incorporatedata-drivenresearchintothelifesciences.Foreachplantlineincluded

inapoolofpossiblecrosses,TanksleyinputDNAsequencedata,phenotypic

dataontraitslikedrought

tolerance,diseaseresistance,andyield,aswellas

environmentaldatafortheregionwheretheplantlinewasoriginallydeveloped.

Thealgorithmprojectedwhichgeneswereassociatedwithwhichtraitsunder

whichenvironmentalconditionsandthendeterminedtheoptimalcombinationof

genesforaspecificbreedinggoal,suchasdroughttoleranceinaparticulargrowing

region,whileaccountingforgenesthatboostedyield.7UsefulfarmingAIrequiresgooddata,andplentyofit.Tocollectsufficientinputs,

someresearcherstaketotheskies.CropresearcherAchimWalterandhisteamare

developingtechniquestocaptureaerialcropimages.Everyotherdayforseveral

years,theyhavedeployedimage-capturingsensorsoverawheatfieldcontaining

hundredsofgeneticlines.Theyflytheirsensorsondronesoroncablessuspended

abovethecrops,orincorporatethemintohand-helddevicesthataresearchercan

usefromanelevatedplatform.8Meanwhile,they’redevelopingimagingsoftwarethatquantifiesgrowthratebased

onthecapturedimages.Usingthesedata,theybuildmodelsthatpredicthow

quicklydifferentgeneticlinesgrowunderdifferentweatherconditions.Ifthey

find,forexample,thatasubsetofwheatlinesgrewwelldespiteadryspell,then

theycanzeroinonthegenesthoselineshaveincommonandincorporatethem

intonewdrought-resistantvarieties.9ResearchgeneticistEdwardBucklerattheU.S.DepartmentofAgricultureand

histeamareusingmachinelearningtoidentifyclimateadaptationsacross1,000

differentspeciesinalargegroupingofgrassesspreadacrosstheglobe.Thegrasses

includefoodandbioenergycropssuchasmaize,sorghum,andsugarcane.Buckler

saysthatwhenpeoplerankwhatthemost

photosyntheticallyefficientandwater-efficientspeciesare,thisis

thegroupthatcomesoutatthetop.10Despitetheseadvances,thetransitiontoAIcanbeunnerving.Thereareinherent

trade-offsthatnoalgorithmcanovercome.Consumersmayprefertomatoeswitha

leafycrownthatstaysgreenlonger.Butthepriceabreederpaysforthatgreencalyx

(aplant’sleafycrown)isabout1%oftheyield,accordingtoTanksley.11Imagerecognitiontechnologycomeswithitsownhostof

challenges,saysWalter.

“Tooptimizealgorithmstoanextent

thatmakesitpossibletodetectacertaintrait,

youhavetotrainthe

algorithmthousandsoftimes.”Inpractice,thatmeans

snapping

thousandsofcropimagesinarangeoflightconditions.Thenthere’stheprocessof

checkingpredictionsagainstmanually-obtaineddata,or“ground-truthing.”Thisis

necessarysothatWalterandotherscanknowwhetherthemodelsworkornot.

Keentoknowwhetherthe

modelaccuratelycapturesthenumberofkernelsonan

earofcorn?You’dhavetocountthekernelsyourself.12Despitethesehurdles,Walterbelievesthatcomputersciencehasbroughtusto

thebrinkofanewagriculturalrevolution.Onceimagerecognitiontechnology

becomesmoredevelopedforcrops,deployingitmayberelatively

inexpensive.

Breederscouldoperatetheirowndronesandobtainmorepreciseratingsoftraits

likethetimetofloweringorfruityield.13Thegenomicdata

–

sovitaltoAI-ledbreedingprograms

–

isalsobecomingmore

accessible.“We’rereallyatthispointwheregenomicsischeapenoughthatyoucan

applythesetechnologiestohundredsofspecies,maybethousands,”saysBuckler.Now,withtheglobalpopulationexpectedtoswelltonearly10billionby2050,andclimatechangeshiftinggrowingconditions,cropbreedersandgeneticistsdon’tthinktheyhavethatkindoftime.[Meaning]:Theglobalpopulationisexpectedtoincreasetoalmost10billionby2050,andtheclimatechangeischangingthegrowingconditionsofcrops.Facedwiththeseurgentsituations,cropbreedersandgeneticistsfeeltime-pressured:itisimpossibleforthemtofollowtheseven-yearperiodoftraditionalplantbreedingprocess.“Thatkindoftime”refersto“roughlysevenyears”inthelastsentenceofparagraph1.[Words&Phrases]:swell:v.increaseinnumber增加

e.g.Thetotalpricetaghasswelledtomorethan$35billion.shift:

v.changeasituation改变e.g.Sheshiftedherpositionslightlysoshecouldseethestagebetter.Duringthe“greenrevolution”ofthe1960s,researchersdevelopednewchemicalpesticidesandfertilizersalongwithhigh-yieldingcropvarietiesthatdramaticallyincreasedagriculturaloutput.[Notes]:“Greenrevolution”referstoagreatincreaseintheproductionoffoodgrains,especiallywheatandrice,drivenbytheintroductionofhigh-yieldcropvarietiestodevelopingcountriesduringthemid-20thcentury.ItsearlydramaticsuccesseswereinMexicoandIndiabeforegraduallyspreadingtoothercountries.Thenewvarietiesrevolutionizedagricultureandhelpedreducepovertyandhungerinmanydevelopingcountries.However,theheavyuseofchemicalfertilizersandpesticidesraisedconcernsaboutaffordabilityandenvironmentaldamage.NormanBorlaug(1914–2009),anAmericanscientist,iscreditedwithpropellingthegreenrevolution.Asthegenomicsrevolutionunfoldedintheearly2000s,plantbreedersfoundthemselvesinundatedwithgenomicdatathattraditionalstatisticaltechniquescouldn’tgrapplewith.[Knowledgefocus]:Genomicsisthestudyofthestructure,function,andevolutionofentiregenomes.madepossiblebyDNAsequencingtechnology.Itfacilitatesthestudyofgenefunctionatabroadlevel,revealinghowsetsofgenesinteracttoaffectbiologicalproperties.Whilethetermgenomewasnotbroughtintouseuntil1920,theexistenceofgenomeshasbeenknownsincethelate19thcentury,whenchromosomeswerefirstobservedasstainedbodiesvisibleunderthemicroscope.Theinitialdiscoveryofchromosomeswasthenfollowedinthe20thcenturybythemappingofgenesonchromosomesbasedonthefrequencyofexchangeofpartsofchromosomesbyaprocesscalledchromosomalcrossingover,aneventthatoccursasapartofthenormalprocessofrecombinationandtheproductionofsexcells(gametes)duringmeiosis.Inatypicalexperimenttodetermineagenomicsequence,genomicDNAfirstisextractedfromasampleofcellsofanorganismandthenisbrokenintomanyrandomfragments.ThesefragmentsareclonedinaDNAvector(carrier)thatiscapableofcarryinglargeDNAinserts.BecausethetotalamountofDNAthatisrequiredforsequencingandadditionalexperimentalanalysisisseveraltimesthetotalamountofDNAinanorganism’sgenome,eachoftheclonedfragmentsisamplifiedindividuallybyreplicationinsidealivingbacterialcell,whichreproducesrapidlyandingreatquantitytogeneratemanybacterialclones.TheclonedDNAisthenextractedfromthebacterialclonesandisfedintothesequencingmachine.Theresultingsequencedataarestoredinacomputer.Whenalargeenoughnumberofsequencesfrommanydifferentclonesisobtained,thecomputertiesthemtogetherusingsequenceoverlaps.Theresultisthegenomicsequence,whichisthendepositedinapubliclyaccessibledatabase.Acompletegenomicsequenceinitselfisoflimiteduse;thedatamustbeprocessedtofindthegenesand,ifpossible,theirassociatedregulatorysequences.Theneedforthesedetailedanalyseshasgivenrisetothefieldofbioinformatics,inwhichcomputerprogramsscanDNAsequenceslookingforgenes,usingalgorithmsbasedontheknownfeaturesofgenes.Oncecandidategenesareidentified,theymustbeannotatedtoascribepotentialfunctions.Suchannotationisgenerallybasedonknownfunctionsofsimilargenesequencesinotherorganisms,atypeofanalysismadepossiblebyevolutionaryconservationofgenesequenceandfunctionacrossorganismsasaresultoftheircommonancestry.Thegrassesincludefoodandbioenergycropssuchasmaize,sorghum,andsugarcane.[Knowledgefocus]:

Bioenergycropsrefertoplantsspecificallygrownforthepurposeofproducingbiomassthatcanbeconvertedintoenergy.Thesecropsareclassifiedintodifferenttypes,includingenergygrasses,oilseeds,andlignocellulosiccrops.Energygrasses,suchasswitchgrassandmiscanthus,areknownfortheirhighbiomassyieldandabilitytogrowinmarginallands.Oilseeds,suchassoybeansandcanola,produceoilthatcanbeconvertedintobiodiesel.Lignocellulosiccrops,suchascornstoverandwheatstraw,containhighamountsofcelluloseandcanbeusedforbiofuelproduction.Throughouthistory,humanshaveutilizedvariousformsofbioenergy,suchasburningwoodforwarmthandcooking.However,itwasnotuntiltheindustrialrevolutionthatbioenergycropsgainedsignificantattention.Theincreasingdemandforenergy,coupledwithconcernsaboutfossilfueldepletionandenvironmentaldegradation,ledtothedevelopmentofbioenergycropcultivation.Keentoknowwhetherthemodelaccuratelycapturesthenumberofkernelsonanearofcorn?You’dhavetocountthekernelsyourself.[Notes]:

Thefirstsentenceisarhetoricalquestion,andthesecondsentenceisanimperative.Thiscombinationcanbeviewedfromtwodifferentperspectives.Inoneway,itcanbeanimitationofWalter’sspeechatthebeginningthisparagraph:“Tooptimizealgorithmstoanextentthatmakesitpossibletodetectacertaintrait,youhavetotrainthealgorithmthousandsoftimes.”InWalter’sspeech,“you”mayrefereitherspecificallytoWalterandhisteam,orgenerallytoanyresearchteamthatisdevelopingimagerecognitiontechnology.Somay“you”inthelastsentenceofthisparagraph.Inanother,thiscombinationcanbeviewedasadevicetoengagethereaders,whoareassumedtobecuriousandwhosesenseofanticipationwillbearoused.人工智能赋能作物育种，助力构建气候韧性未来1

直到不久前，植物育种领域看起来还和过去几个世纪差不多。例如，育种者可能会观察哪些番茄植株最耐旱，然后将最有前景的植株进行杂交，以培育出最耐旱的后代。这个过程会一代又一代地重复进行，直到大约七年后，育种者最终获得看似最优的品种。2如今，随着全球人口预计到2050年将增长到近100亿，同时气候变化正在改变作物生长条件，作物育种家和遗传学家认为，他们已经没有那么多时间了。为了加快这一过程，他们开始转向人工智能。借助计算机科学技术，育种者可以快速评估：哪些植物在特定气候下生长最快，哪些基因有助于植物在那里茁壮成长，以及哪些植物相互杂交后，能够为某一特定地点产生最佳的基因组合。他们会选择那些能够提高产量并抵御气候变化影响的性状。随着计算能力的迅速提升，这些技术如今有望在更大范围内加速育种进程。3然而，人工智能并不是万能的解决方案。作物育种者仍然需要应对各种权衡取舍，例如更高产量与更具市场吸引力的外观之间的矛盾。即使是最先进的人工智能，也无法保证新品种一定成功。但随着人工智能逐渐融入农业，一些作物研究人员设想，一场由计算机科学引领的农业革命即将到来。

4在20世纪60年代的“绿色革命”期间，研究人员开发了新的化学农药和化肥，以及高产品种，这些都大幅提高了农业产量。但对化学品的依赖也带来了沉重代价，即环境退化。StevenTanksley说：“如果我们要以可持续的方式做到这一点，遗传学将承担大部分重任。”

5随着21世纪初基因组学革命的展开，植物育种者发现自己被海量基因组数据所淹没，而传统统计技术难以处理这些数据。Tanksley说，植物育种“并不是为处理大量数据和作出精准决策而设计的”。

6从20世纪90年代开始，Tanksley开始主持康奈尔大学的一个委员会，该委员会旨在将数据驱动型研究引入生命科学。对于纳入潜在杂交组合池中的每一个植物品系，Tanksley输入了DNA序列数据、关于抗旱性、抗病性和产量等性状的表型数据，以及该植物品系最初培育地区的环境数据。该算法预测在何种环境条件下哪些基因与哪些性状相关，然后在考虑提高产量基因的同时，确定针对特定育种目标的最佳基因组合，例如某一特定种植区域的抗旱性。7有用的农业人工智能需要优质数据，而且需要大量数据。为了收集足够的输入数据，一些研究人员把目光投向了天空。作物研究人员AchimWalter和他的团队正在开发捕捉作物空中影像的技术。几年来，他们每隔一天就在一片包含数百个遗传品系的小麦田上方部署图像采集传感器。他们将传感器安装在无人机上，或安装在悬挂于作物上方的缆线上，也会将其集成到手持设备中，供研究人员在较高的平台上使用。8与此同时，他们正在开发一种成像软件，用来根据采集到的图像量化作物生长速度。利用这些数据，他们建立模型，预测不同遗传品系在不同天气条件下的生长速度。例如，如果他们发现某一小麦品系子集尽管经历了干旱期仍然生长良好，那么他们就可以锁定这些品系共有的基因，并将这些基因引入新的抗旱品种中。9美国农业部的研究遗传学家EdwardBuckler和他的团队正在利用机器学习，识别全球范围内一个大型禾本科植物类群中1000种不同物种的气候适应性。这些禾本科植物包括玉米、高粱和甘蔗等粮食及生物能源作物。Buckler表示，当人们对光合效率最高和水分利用效率最高的物种进行排名时，这一类群总是名列前茅。10尽管取得了这些进展，向人工智能转型仍可能令人不安。有些内在的权衡取舍是任何算法都无法克服的。消费者可能更喜欢带有叶状冠、且叶冠能保持更长时间绿色的番茄。但据Tanksley称，育种者为这种绿色萼片，也就是植物的叶状冠，所付出的代价大约是1%的产量损失。11Walter表示，图像识别技术本身也面临一系列挑战。“为了将算法优化到能够检测某一特定性状的程度，你必须对算法进行成千上万次训练。”在实践中，这意味着要在各种光照条件下拍摄成千上万张作物图像。随后还要把预测结果与人工获得的数据进行核对，这一过程称为“地面实况验证”。这是必要的，因为Walter和其他研究人员需要知道模型到底是否有效。想知道模型是否准确捕捉到一穗玉米上的籽粒数量吗？那你就得自己去数一数这些籽粒。12尽管存在这些障碍，Walter仍然认为，计算机科学已经把我们带到了新一轮农业革命的边缘。一旦图像识别技术在作物领域变得更加成熟，部署这项技术的成本可能会相对较低。育种者可以操作自己的无人机，并对开花时间或果实产量等性状获得更精确的评估。13对人工智能引领的育种项目至关重要的基因组数据，也正变得更加容易获得。Buckler说：“我们确实已经到了这样一个阶段：基因组学的成本已经足够低，使你可以将这些技术应用到数百个物种，甚至可能是数千个物种上。”GlobalunderstandingReadthepassageandrearrangethestatements.MWithsufficientbiologicaldataandenvironmentaldata,theresearcherusedanalgorithmtopredictgeneticperformance.Itusedtotakeaboutsevenyearsforbreederstoproduceaseeminglybestvarietyofthemostdrought-resistanttomatoplant.Itmayincursomecosttomeetcustomers’preferenceforaspecificappearanceoftomatoes.B–F–D–A–H–E–J–C–I-GGeneticsisclaimedtohaveamajorroletoplayinachievingsustainableagriculture.Withdatageneratedbyimagingsoftware,researcherscanmodelthegrowthofdifferentlinesofcropsanddetectthedesiredtrait,andintegratethecorrespondinggenesintonewvarieties.GlobalpopulationgrowthandclimatechangehavejointlypropelledbreederstoresorttoAIinbreeding.B–F–D–A–H–E–J–C–I-GWithimagerecognitiontechnologybecomingreadilyaffordableandgenomicdataconvenientlyaccessible,breederswillbeabletodotheirjobmoreeasily.Toacquiresufficientdata,researchersattachsensorstodrones,cables,orhand-helddevicesusedfromanelevatedplatform.Algorithmshavetobetrainedrepeatedlybeforetheycanbeusedtoaccomplishaspecifictask.Researchershaveusedmachinelearningtoidentifyclimateadaptationsofvariousspeciesofmaize,sorghum,sugarcaneandothergrassesdistributedglobally.B–F–D–A–H–E–J–C–I-GScanthecodeandcompletemorelanguageexercisesonUcampus.Detailed

understandingAIalgorithmscanprojectanidealcombinationofgenesanddrivecore

breedingdecisions:whichtraitstoprioritize,whichvarietiestocross,

whichphenotypestoselect.Unlikehumanbreeders,algorithmsoperateon

“learned”patternsfromdata.Whatifalgorithmicbiasoccurs?AIbreedinghasgreatlybenefittedmajorseedcompaniesworldwide.What

approachescansmallerlocalseedcompaniesandtraditionalfarmerstaketo

alsobenefitfromit?IfAImodelsaretrainedtooptimizefortraitsthatmaximizeprofit,thetraitscriticaltomarginalizedcommunitieswillbeoverlookedorabandoned.Forexample,anAImodelforwheatbreedingprioritizesyieldoverzinccontent,eventhoughzincdeficiencyisamajorpublichealthissueinSouthAsia,wherethewheatisconsumed.Similarly,livestockAImodelsmayselectforrapidweightgain,ignoringtraitslikediseaseresistancethatsmallholderfarmersinAfricaneedtoreducelosses.ReferenceanswersLargeseedcompaniesprimarilyfocusongrainskeytoanimalfeedsuchascorn,wheat,andsoy.Togainashareofthemarket,smallerlocalseedcompaniesmaycooperatewithtraditionalfarmersandfocusondevelopingvegetablecropsthatpeopleactuallyeat.AIbreedingcanhelpproduceclimate-resilientvegetablessuchasdrought-toleranttomatoes,heat-resistantlettuceorflood-adaptedcarrots.

ScanthecodeandcompletemorelanguageexercisesonUcampus,includingvocabularyandtranslation.Chinesescientistsdeciphergenomesecretsoffoxtailmillet1Chinesescientistshavemadeanewbreakthroughinthegraph-basedgenomeresearchoffoxtailmillet,whichwillfacilitaterapidbreedingofthecropandhelpensureglobalfoodsecurity.Thenewstudy,conductedbyaninternationalteamledbyscientistsfromtheInstituteofCropSciencesoftheChineseAcademyofAgriculturalSciences(CAAS),waspublishedonlineintheacademicjournalNatureGenetics.2

FoxtailmilletisoneoftheoldestdomesticatedcropsintheworldandastaplecropintheformationofAsianagriculturalcivilization.ItheldadominantpositionintheChineseagriculturalsystembeforetheintroductionofhigh-inputagriculturalpracticeslikeirrigationandchemicalfertilizers.Thecroppossessesseveralfavorabletraits,includinghighphotosyntheticrates,efficientwaterutilization,andadaptabilitytodiverseenvironments–includingaridregions.Foxtailmilletshowsimmensepotentialforensuringfoodsecurityamidclimatechange.Theresearchersestablishedthefoxtailmilletpan-genomebyassembling110representativegenomesfromaglobalcollectionof1,844foxtailmilletspecies.Large-scalegeneticstudieswereperformedfor68traitsacross22environmentsin13geographicallocations.Theresearchersalsoidentifiedpotentialgenesandmarkerpanelsforhowfoxtailmillethasevolvedandimprovedatdifferentgeographicsites.Forexample,theresearchersfoundthatthegeneSiGW3regulatesgrainyieldinfoxtailmillet.3Thisstudyprovidesabetterunderstandingofthedomesticationandimprovementprocessoffoxtailmillet,the