塑造农业的深度技术革命

ShapingtheDeep-TechRevolutioninAgriculture

INSIGHTREPORTNOVEMBER2025

Images:GettyImages

Contents

Foreword3

Executivesummary4

Introduction5

1Acknowledgingtheloomingcrisisinglobalagriculture6

2Future-proofingagriculture:Theroleofdeeptech8

2.1Understandingdeeptechinagriculture9

3Adetailedlookatpromisingagrideep-techdomains10

3.1GenerativeAI10

3.2Computervision12

3.3Edgeinternetofthings(edgeIoT)14

3.4Satellite-enabledremotesensing16

3.5Robotics(includingdrones)18

3.6CRISPR20

3.7Nanotechnology22

4Techconvergencesandbreakthroughusecasesforagriculture24

4.1Breakthroughusecases26

5Optimizingagrideep-techecosystems34

Conclusion37

Annexe:Selectingtechnologydomainsfordetailed38

examination–methodology

Contributors40

Endnotes41

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ShapingtheDeep-TechRevolutioninAgriculture2

ShapingtheDeep-TechRevolutioninAgriculture3

November2025

ShapingtheDeep-TechRevolutioninAgriculture

Foreword

JeremyJurgens

ManagingDirector,

CentrefortheFourth

IndustrialRevolutionandCentreforCybersecurity,WorldEconomicForum

Globally,agriculturestandsatadefiningmoment

inhistory.Astheworldfacestheintersecting

pressuresofclimatechange,resourcedegradation,demographicshiftsandgeopoliticalinstability,theabilitytosustainablyfeedagrowingpopulationisunderincreasingstrain.Conventionalapproaches,thoughvital,arenolongersufficienttomeetthe

scaleandurgencyofthesechallenges.Instead,

thereisnowarequirementtotakeaboldleap

forward–byharnessingthetransformativepotentialofdeeptechinagriculture.

Deeptech,spanningdomainssuchasartificial

intelligence(AI),robotics,biotechnology,

nanotechnologyandsatellite-enabledsystems,

offersseveralopportunitiestoreshapefoodand

farmingsystems.Thesetechnologiesnotonly

leadtoincrementalimprovementsbutcantriggerfundamentalshiftsinhowcropsaregrown,

monitored,protectedanddistributed.From

developingclimate-resilientseedvarietiesto

deployingautonomousfarmmachineryandbuildingpredictiveclimatemodels,deeptechholdsthe

promiseofcreatingagriculturalsystemsthataremoreproductive,resilientandsustainable.

Seedingandcommercializingdeep-tech

innovations,however,requiresdeliberateecosystembuildingthatencompassessupportivepolicies,

RanveerChandra

ChiefTechnologyOfficer,Agri-Food,Microsoft

patientcapital,robustdigitalinfrastructureandskilledhumancapital.Collaborationacross

governments,researchinstitutions,start-ups,corporationsandfinancierswillbeessentialfortranslatingearlybreakthroughideasintotransformativesolutionsatscale.

Inabidtodemystifythepotentialofdeeptechinagriculture,theWorldEconomicForum’sArtificialIntelligenceforAgricultureInnovation(AI4AI)

initiativewithsupportfrompartnersgloballyhas

developedthisinsightreport.Ithighlightsthe

mostpressingchallengesfacingagricultureand

illustratespromisingdeep-techdomainsthatcanmitigatetherisksassociatedwiththesechallenges.Itfurtherexploreshowtheconvergenceofmultipledeep-techdomainscanunlockbreakthroughusecasesrangingfromswarmroboticsandagenticAIsystemstomacrocropplanningandsupplychainrisk-assessmenttools.

Thereportalsooutlinesactionablepathwaysto

acceleratetheadoptionofdeeptechinagriculture.Consequently,itisaninvitationtopolicy-makers,

corporations,innovators,academia,researchandfunderstorecognizethepivotalroleofdeeptechinfuture-proofingglobalagricultureandtoact

cohesivelytosupportforward-lookingtechnologies.

ShapingtheDeep-TechRevolutioninAgriculture4

Executivesummary

Deeptechhasthepotentialtofuture-proofagriculturalsystems,butcollaborationiscriticaltodeliverthematscale.

Theagriculturalsectorgloballyfacesconvergingpressures:ashrinkingworkforce,intensifying

climateextremes,naturalresourcedegradation,risingfooddemandandgeopoliticalinstability.

Thesechallengesthreatenfoodsecurityandrurallivelihoods,demandingtransformativeaction.Novelscience-backedtechnologiesoftenreferredtoasdeeptechcoulddrivemuchofthisactioninthe

comingdecades.

Thisreportexploresdeeptech’spotentialin

agricultureandidentifiessevenpromisingdeep-techdomainsaspivotalfortacklingcurrentandfuture

agriculturalchallenges.Theseare:

GenerativeAI(GenAI):Offersuse-casesrangingfromtailoredfarmeradvisoryand

pestmanagementtoagenticAIsystemsandclimaterisksimulations.GenAI’sapplicability

inagricultureisdrivenbyrecentadvancesin

largelanguagemodels(LLMs)andtheincreasing

availabilityofagriculturaldata.However,despite

theseadvancesandthegrowingadoptionofGenAI,thelackofhigh-qualitydatafortraininghyperlocalmodelsremainsabarriertousability.

Computervision:Providesuse

casessuchasrapidpestanddiseaseidentificationorplantstressdetection.

Thegrowthofcomputervisionusecaseshas

beenfuelledbydecreasingcameracostsand

advancesindeep-learningmodels.However,unlikeinindustrialunits,on-fieldvariability(forinstance,variationsinon-fieldlightingandplantappearancebetweengrowthstages)restrictsitsapplicability

foragriculture.

Edgeinternetofthings(IoT):Enablesreal-time,on-farmdataprocessing

andautonomousdecision-makingforagriculture.Thisminimizeslatencyandbandwidthdependency,especiallyinareaswithpoorinternetconnectivity.EdgeIoTcanimprovedecisions

relatedtoirrigation,fertilizationanddisease

management,whileautomatingfarmprocesses.

Thedomaincurrentlyfaceschallenges,withhighcapitalcostsforfarmersandlimitedinteroperabilityamongedgesystems.

Satellite-enabledremotesensing:Allowscontinuousandlarge-scale

monitoringoffarmconditionsat

affordablecosts,aidingdata-drivendecision-

making.Enhancedspatialandspectralcapabilitiesandincreaseddatacapturefrequencyaredrivingadoptioninagriculture,althoughthelevelof

accuracyislimitedinsmallandfragmented

farmlandsorwhenmulti-croppingispractised.

Robotics:Permitstheautomationoflabour-intensivetaskssuchasprecisionplanting,weedingandharvesting.

AdvancesinAI-enabledperceptionandcloud-edgeintegrationaredrivingitsadoption.However,highcapitalcostslimititsuptakeinlow-wage,labour-

abundantcountries.

CRISPR:Acceleratesthedevelopmentofcropswithenhancedtraitssuchas

droughttoleranceandpestresistance,bypassinglengthytraditionalbreedingcycles.ThepotentialprecisionandspeedofCRISPR-based

editingaresignificantdriversofuse,butregulatoryapprovalprocessesandnegativepublicperceptionarebarrierstocommercialization.

Nanotechnology:Offersprecisionin

nutrientandpesticidedelivery,reducing

inputuseandenvironmentalimpact.It

enablesawiderangeofusecases,rangingfrom

pestandnutrientmanagementtocontrolledreleaseofinputstobiosensing,thoughalackofresearchdataonthelong-termenvironmentalandhealth

impactsremainsabarriertoscale.

Thisreportidentifiesbreakthroughagrideep-techusecasesderivedfromthesedomains.Asseveralareyettobecommercialized,itfurtherprovides

recommendationstooptimizesupportforagrideeptech.Itelaboratescollaborativeeffortsinpolicy,

finance,humancapital,data/digitalinfrastructureandinnovationsupporttoseedpromisingagrideep-techideas,de-riskinnovationsandenableimpactatscale.

ShapingtheDeep-TechRevolutioninAgriculture5

Introduction

Globally,agritechisincreasinglybeing

acceptedasakeylevertodriveinclusivity,sustainabilityandefficiencyinsupplychains.

Overthepastdecade,therehasbeensignificant

innovationinthetechnologiesfortheagriculture

(oragritech)sector,creatingimpactatbothfarm

andsystemiclevels.Farmershaveusedagritech

toreducecultivationcosts,improveyields,

securebetterpricesandenhanceresilience.1

Agribusinesseshavedrawnonagritechforefficientsourcing,andtostreamlinefarmermanagement,

ensurecompliance(e.g.withtheEuropeanUnionDeforestationRegulation[EUDR]),mapsupplychainrisksandtransitiontocarbonneutrality.

Withagrowingpopulationandshrinkingresources,agritechcouldplayakeystoneroleinfoodsecurityandrurallivelihoods.Yetseveralbarriersremain

suchaslowadoption,limitedcontextualdatafordevelopingsolutions,highcapitalexpenditure

(capex)forcertainusecasesandrisingdatasilos.Toaddressthesechallenges,theWorldEconomicForumlaunchedtheArtificialIntelligencefor

AgricultureInnovation(AI4AI)initiativein2021.

AI4AIaimstoscaleagritechthroughanecosystemapproachthatencompassespublic–private

partnerships,thedevelopmentofdigitalpublicinfrastructureandagritechpolicies.

AkeycomponentofAI4AIistodrivethought

leadershiponagritech.In2021,theinitiative

launchedacommunitypaperthatdocumentedpromisingusecasesofagritechandpresentedaroadmapforscalingthem.2Thesecondinsightreportpresentedanoverviewofthenextwaveofagritechsolutions,especiallythoserelevanttoemergingeconomies.3

Acknowledgingtherapidpaceoftechnologydevelopment,thiseditionofthereportpivotstowardsthedeep-techrevolutioninagriculture.

Thelearningsarebasedonmorethan75

communityconsultations(asapartofAI4AI’s

programmes),agritechconveningsorganizedby

theinitiativeandmultipleon-the-groundpilots

facilitatedbycommunitymembers.Theinsights

arefurtherenrichedthroughstructuredinterviewswitharound20deep-techexperts.Thereport

identifiestechnologydomainsthatmaycurrentlybeatanearlystagebuthavethepotentialtoaddressconvergingchallengesinagriculture.Itdetailssevenofthesedomainsandseveralconvergentuse

cases.Further,thereporthighlightsmechanismsfordevelopingrobustsystemsforseeding,de-riskingandcommercializingagrideeptech.

since2021,theAI4Alinitiativehasunlockedcommitmentstoprovidedigitaltechnologiestomore

than895,000farmersinIndiaA4Alhaspromotedmultistakeholderpartnershipsincludingamong

governments,theprivatesector,academia,start-upsandcivilsocietytogenerateevidenceonthe

transformationalimpactoftechinagriculture:ltsworkincludesavaluechaintranisformationprojectinTelanganaimpacting50,000farmers(includingmorethan30%women),almostdoublingtheirincomes,establishingIndia'sfirstAgricultureDataExchange(inpartnershipwiththeGovemmentofTelangana)

andsupportingagritechandagridatamanagjementpoliciesinthreestates.BuildingonlessonslearnedinIndia,AI4AlhassupportedthleconceptualizationofsimilarinitiativesinthekingdomofsaudiArabia,colombiaandBrazil.

ShapingtheDeep-TechRevolutioninAgriculture6

1

Acknowledgingtheloomingcrisisin

globalagriculture

Intheyearstocome,agriculturegloballyisexpectedtofacechallengesthatwillreducetheresourcesavailableforfoodproductionalongwithasimultaneous

increaseindemand.

FIGURE1

Challengesinagriculturetoday

Climatechangeand

intensifyingweather

extremes

Risingfooddemand

anddemand–supply

mismatch

Shiftingworkforce

dynamics,resultinginlowerhumancapitalforagriculture

Source:ConsultationswithAI4AIcommunityexperts

Geopolitical

instabilityandfriction

degradation

Natural

resource

labouravailabilityisdecliningandisexpected

tofallfurtherduetorisingurbanmigrationandageingruralpopulations.Theglobalaverage

Agriculturetodayiswitnessinganunprecedentedconvergenceofchallengesthatarelikelyto

significantlyaffectsustainablefoodsecurityandfarmerincomes.

Thesechallengesinclude:

–Shiftingworkforcedynamics,resultingin

lowerhumancapitalforagriculture:Agricultural

ageoffarmersisaround60years.IntheUnitedStates,theaverageageofafarmeris58years,whileinEurope,one-thirdoffarmersaremorethan65yearsold.WithintheAsia-Pacificregion,theaveragefarmerageforThailandandthe

Philippinesis54and57yearsrespectively.4,5

ShapingtheDeep-TechRevolutioninAgriculture7

Anincreasingruraltourbanmigrationisalso

threateningproduction.In1960,morethan65%oftheworld’spopulationlivedinruralareas.6

Thisfellto43%in20237andisprojectedto

declinefurtherto32%by2050.8Suchtrends,whichresultinadeclineinproductiveresources,threatenfoodsecurityinthelongrun.

–Climatechangeandintensifyingweather

extremes:Climatevolatilityisprojectedto

significantlyaffectcropproductivityglobally.

Estimatessuggestthatevenafteradaptationmeasures,theglobalyieldofcaloriesfrom

staplecropscouldbe24%lowerin2100,if

emissionsarenotcurtailed.9InIndia,irregularrainfallandincreasingtemperatureshave

alreadyledtolossesofcloseto65%inseveralhorticulturalcrops.10

–Naturalresourcedegradation(soiland

water):Theagriculturesectorisresponsible

for70%offreshwaterwithdrawals.However,

currently71%ofgroundwateraquifersare

depleted,whichislikelytodriveagricultural

waterstress.11Similarly,95%ofglobalfood

productionisdependentonsoil.Whileone-thirdofglobalsoilisdegradedtoday,closeto90%oftheEarth’stopsoilislikelytobedegraded

by2050.12

–Risingfooddemandanddemand–supply

mismatch:Alongsideproductionpressures,

agrowingpopulationwillalsoincreasefood

demand.Incomparisonto2005–2007levels,by2050globalfoodproductionwillneedtoincreasebyabout70%,andtonearlydoubleindevelopingcountries.13Thisincreasewouldhavetobe

deliveredalongsiderisingresourceconstraints

andclimateuncertainties.Furthermore,thesectorischallengedbyademandandsupplymismatch,leadingtosignificantfoodlossandwastage.

Closeto13%ofglobalfoodproductionislostafterharvestatthefarmlevelandbeforeretail,14while19%offoodinretail,foodservicesandhouseholdsiswasted.15

–Geopoliticalinstabilityandfriction:

Geopoliticaltensionscoulddisruptagriculturalsupplychainsinthefuture.Toillustratethis,

inMay2022fertilizerpricesreachedrecord

highsduetoseveralsupplychaindisruptionsoftenattributedtotheRussia–Ukraineconflict.Furthermore,asignificantproportionof

countries(closeto36%)arenetimportersoffood,signifyingriskstofoodsecuritydueto

geopoliticalshifts.Inthepast,therehavebeenseveralinstanceswheretradedisruptionsintheRedSeahaveledtovolatilefoodpricesinimportingcountries.16

ShapingtheDeep-TechRevolutioninAgriculture8

2

Future-proofing

agriculture:Theroleofdeeptech

Severaldeep-techdomainscanbuildresilienceinagriculturalsystemsandaddresstheconvergingpressuresthesectorcurrentlyfaces.

Addressingtheloomingchallengesinagriculture

willrequiretransformativeshiftsinhowfoodis

produced,managedanddistributed,improving

yieldsandsupplychainefficiencywhilealsobuildingresilience.Inthiscontext,deeptechcanplaya

pivotalrole.

Deeptechcomprisesnovelscience-backed

technologiesthatcanaddresssystemicsocietal

andenvironmentalchallenges.Deep-techdomainsincludeartificialintelligence,robotics,edge

computing,quantumcomputing,remotesensingandsyntheticbiology.

Technologydomainvs.technologyusecase

UTechnologydomainnreferstoaspecificareaoftechnologyorfieldofstudyinwhichresearch,

developmentandinnovationtakeplace:A"'technologyusecase"isthepracticalapplicationofoneormoretechnologydomainstosolveareal-worldproblem:

Deeptechoffersagriculturethecriticalbreakthroughiturgently

needs.ByintegratingAI,remotesensinganddatascience,wecantransformfragmentedfarmingsystemsintoresilient,productive

andclimate-smartecosystems.Theseinnovationsempower

farmers,financialinstitutions,agribusinesses,governments

anddevelopmentagencieswithreal-timeintelligence,reduce

inefficienciesacrossthevaluechain,andenablecomplementaryservicessuchascreditandinsurance.AtCropin,webelieve

thatadvancingdeep-techusecasesinagricultureisnotjustanopportunitybutanecessitytoensurefoodsecurityinthefaceofclimatechangeandnaturalresourcedegradation.

KrishnaKumar,ChiefExecutiveOfficer,CropinTechnologies

2.1Understandingdeeptechinagriculture

Deeptechinagriculturehasfivemaincharacteristics.

TABLE1Fivecharacteristicsofdeeptech–andtheiragriculturalrelevance

CharacteristicsRelevancefortheagriculturesector

1

Scienceandengineering-backeditecigdnliebi,rcieia-rgilnlaallwineitrpi,csisui.asAI,

innovation

2

Agrideep-techusecasesbringtogethermultipletechnologydomains,deliveringimpactbeyond

Usecasesrelyontechnologywhatcanbedeliveredbyasingledomaininisolation.

convergence

3Agrideep-techusecasesrequireyearsofresearchanddevelopment(R&D)andthereis

gncantycapta-ntensve,wttestingacrossmultipleseasons/locations.Theirdevelopmentdemandshighupfrontinvestment,

Siifililiiihuncertaintyaboutthetimehorizonsformaturity.Beforedeployment,thesetechnologiesrequire

uncertaintimehorizonsformaturitycreatingbarrierstoentry.

thereforeneedtounderwriteacomplextechnologyrisk.Evenafterdevelopment,usingagri

Agrideep-techusecasesarehigh-riskinvestmentsduringthedevelopmentphase.Investors

Highrisksdeeptechcancarryrisksthatmaybesocial(e.g.displacementoffarmworkers),ethical(e.g.

misuseoffarmerdataandbiases),environmental(e.g.highassociatedemissions)orregulatory(e.g.challengeswithcommercializationduetodelayedapprovals).Further,themarginforerrorwithagrideep-techusecasesmustbeclosetozerobeforedeployment,becausefailuresin

techcandirectlyaffectfarmerlivelihoodsandfoodsecurity.

5

Agrideep-techusecasesarepurposelybuilttoaddresssystemicchallenges.Besidesbeing

Abletosolvecomplexchallengesintheeffectiveatthefarmlevel,mostusecaseshavethecapacitytoaltertheeconomicsofanentire

agriculturalsectorsupplychain,shiftingfoodproductiontowardssystemicsustainability,inclusivityandefficiency.

ShapingtheDeep-TechRevolutioninAgriculture9

ShapingtheDeep-TechRevolutioninAgriculture10

3

Adetailedlookatpromisingagri

deep-techdomains

Agrideep-techusecasestypically

drawondistincttechnologydomains,eachwithauniqueapplicabilityandtransformativepotential.

Severaltechnologydomainsconvergetoproduceagrideep-techusecasesthatcouldunlock

breakthroughsinthesector.Thissectionexplores

sevensuchdomains.TheAnnexedetailsthemethodologyforshortlistingthesedomains.

3.1

GenerativeAI

GenAIisasubsetofAIthatgeneratesnewand

originaldata/contentbyanalysingpre-existing

datasets.17Thegeneratedcontentcantakevariousforms,includingtext,videoandaudio,whichare

createdinresponsetoaprompt/query.GenAIhasfarmer-facingusecases,includingcustomized

adviceforcropmanagement,generationofa

hyperlocalpackageofpracticesorpredicting

marketprices.Whencombinedwithnatural

languageprocessing,GenAIcaninterfaceeffectively

withfarmers,providingquickandscientific

resolutionstoqueries,andcanalsofacilitatethe

developmentofautonomousagenticAIsystems

forfarmmanagement.18Usecasesextendbeyondfarmersasendusers.Forinstance,GenAIcan

supportthedevelopmentofmacrocropplansforgovernments,simulateclimaterisksforbusinessesandacceleratethedevelopmentofnewcrop

varieties(byidentifyingdesirablegenesorpredictinggene-editingoutcomes).

FIGURE2

IntegrationofGenAI:Beforeandafter

BEFORE

J

Farmersrelyonstaticadvisory(predefinedmessagesnotcustomizedtotheindividualfarmers)forplanningtheirnextcroppingcycle;manyoftheseadvisoriesarerelevanttoabroadgeography.Whenapestoutbreakhits,thefarmersdonotgettimelyadvice.Additionally,mostoftheadvisoryisone-waycommunication,withnofollow-ups.Whentwo-wayqueryresolutioniscritical,thefarmerswaitfor

extensionagents.Sometimestheygoforadvicetoinputproviderswhomayhaveavestedinterestinpromoting

specificinputs.Overall,mostfarmdecisionsarereactive,fragmented,time-consumingandheavilyreliantongenericsupport.Additionally,farmrecord-keepingismanualandthereforethequalityoffarmdatathatiscapturedremainspoor.Thisaffectsfuturedecision-making.

AFTER

FarmersuseaGenAI–poweredchatbotontheir

smartphones,whichallowsfortwo-waycommunication.

Itgeneratestailoredadvicefordifferentplotsizes,soil

conditionsandweatherpatternsinpreferredlanguages.Thefarmersreceiveinstantresponsesonpestoutbreaks,includingpossibletreatmentstomanagepests.Insteadofrecordingdatamanually,thefarmersspeaktothechatbot,whichdigitizestheirdatainaformatthatcanbeusedbysupplychainactors.Italsodraftsgovernmentsubsidy

applicationsautomatically.AfterGenAIintegration,mostdecisionsareproactiveanddata-driven.

FIGURE3

DriversandbarrierstotheuseofGenAIinagriculture

BARRIERS

DRIVERS

Advancesinlargelanguagemodels(LLMs):

ThesignificantsuccessofLLMssuchasChatGPTandothergenerativemodelshasboostedinvestmentin

languagemodels,includinginagriculture.AdvancesinnaturallanguageprocessinghavealsoimprovedtheusabilityofGenAIinsettingswithlowdigitalliteracy.

C

Dataqualityissues:

Whiletheavailabilityofdataforagricultureisincreasing,accessingqualitydataisstillchallenging,especiallyinsmallholdercontexts.Thisdecreasesthereliabilityof

contentcreatedthroughGenAImodels.

Increasingavailabilityofagriculturaldata:

Thegrowingaccessibilityofagriculturaldatais

providingthefoundationalbaseforGenAIinagriculture.Additionally,thelowercostofdatacollectionthrough

complementarytechnologiessuchassatellite-enabledremotesensingisfuellingitsgrowth.

Hallucinationsanderrors:

GenAIsometimesproducesinformationthatsoundsconvincingbutisfactuallyincorrectorirrelevant.

Withoutrobustvalidation,farmersriskactingonfalseinsightsthatmayharmyieldsorincomesandaffectcontinueduse.

IncreasedinvestmentsinGenAIforagriculture:

TherehasbeenanincreaseininvestmentinGenAI,

withseveralbigtechfirmsinvestinginGenAIfor

agriculture.Thegrowingpartnershipbetweenthesetechcompaniesandlocalinnovatorsandresearch

organizationsisdrivingthecreationofcontext-specificandlocalizedsolutionsthatarebetteracceptedin

farmingregions.

Modeltransferabilityacrossdomainsandregions:

WhileGenAIchatbotshavereportedsuccessincertainpilots,theirtransferabilityforuseindomainssuch

asregenerativeagricultureoracrossvaluechainsis

limited.TherelianceonLLMsalsolimitstheavailabilityofcontext-specificrecommendationsandimpairs

farmertrustandadoption.

ShapingtheDeep-TechRevolutioninAgriculture11

Source:ConsultationswithAI4AIcommunityexperts

ShapingtheDeep-TechRevolutioninAgriculture12

CASESTUDY1

ImprovingagriculturaldatacollectionandqualitythroughGenAI–WadhwaniInstituteforArtificialIntelligence,India

Datacollectioninsmallholderfarmingecosystemsisacriticalbarriertohigh-qualityservicedelivery.Inmanyeconomies,agriculturaldatacollectionisstillmanualandpaper-basedorconductedusingbasicsoftware,andispronetoerrorsandinefficiencies.Thesesystemsalsoresultintheexclusionof

farmerswithlowliteracy.

Toaddressthis,theWadhwaniInstituteforAIdevelopedAgriAICollect.AgriAICollectusesautomaticspeech

recognitiontotranscribemultilingualvoiceinputs,LLMs

toextractstructuredresponsesandahuman-in-the-loop

systemtovalidatelow-conf