Department of Botany, Plant Physiology, Biochemistry and PGPR Lab, Deen Dayal Upadhyaya Gorakhpur University, Gorakhpur, Uttar Pradesh, India Corresponding Author Kumari Sunita
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Artificial Intelligence (AI) is rapidly transforming agricultural systems by enhancing productivity, efficiency, and sustainability. This review explores the role of AI-driven agriculture in improving nutritional security and public health outcomes globally. With the increasing challenges of climate change, population growth, and resource constraints, conventional agricultural practices are often insufficient to meet rising food demands. AI technologies, including machine learning, remote sensing, and precision farming, provide innovative solutions for optimizing crop production, reducing resource wastage, and improving food quality. AI-based tools enable real-time monitoring of soil health, crop growth, and pest dynamics, allowing farmers to make data-driven decisions that enhance yield and nutritional value. Additionally, AI applications in supply chain management help reduce post-harvest losses and ensure efficient food distribution, thereby improving food accessibility and affordability. These advancements contribute significantly to reducing malnutrition and enhancing dietary diversity, AI-driven agriculture supports public health by minimizing excessive use of agrochemicals, improving environmental sustainability, and reducing exposure to harmful substances. However, challenges such as limited digital literacy, high implementation costs, and data accessibility issues hinder widespread adoption, particularly in developing regions. AI-driven agriculture holds immense potential to strengthen nutritional security and public health. Strategic investments, policy support, and capacity building are essential to harness its full benefits for sustainable development
Keywords: Artificial intelligence; nutritional security; smart farming; digital agriculture; food security; Artificialintelligence; precisionagriculture; nutritionalsecurity; smartfarming; digitalagriculture; foodsecurity
1.Introduction decisionsthatoptimizetheuseofinputssuchaswater, fertilizers,andpesticides(12,13).Thistargetedappli- Agricultureplaysavitalroleinensuringfoodsecurity, cationnotonlyimprovesresourceefficiencybutalso nutrition,andpublichealthacrosstheglobe(1).How- significantlyenhancescropyieldandquality(9).One ever, rapid population growth, climate change, and ofthemostimportantcontributionsofprecisionfarm- decliningnaturalresourceshaveplacedimmensepres- ingisitsroleinstrengtheningnutritionalsecurity(8,9). sure on agricultural systems (2,3). Traditional farm- Nutritionalsecuritygoesbeyondmerefoodavailability ingmethodsoftenlacktheefficiencyandprecisionre- andemphasizesaccesstosafe,diverse,andnutrient- quiredtomeetmodernchallenges,necessitatingtech- richfoodsnecessaryformaintaininghealthandwell- nologicalinterventionssuchasArtificialIntelligence being(16).Throughprecisemonitoringandmanage- (AI)(4).AI-drivenagricultureintegratestechnologies mentofcropgrowthconditions,AItoolshelpproduce likemachinelearning,bigdataanalytics,robotics,and cropswithimprovednutritionalprofiles.Forexample, remotesensingintofarmingsystems(5,6).Theseinno- optimalnutrientmanagementensuresthatplantsre- vationsallowreal-timemonitoringofcrops,prediction ceivetherightbalanceofmacroandmicronutrients, ofweatherpatterns,andefficientmanagementofre- leadingtohigherconcentrationsofessentialvitamins sources,therebyimprovingproductivityandsustain- and minerals in harvested produce (15,16). This di- ability(7).AIapplicationsenablefarmerstomakedata- rectlycontributestoimproveddietaryqualityforcon- drivendecisionsthatenhancebothyieldandquality sumers(15).Inadditiontoenhancingcropquality,pre- ofproduce(5,7). Nutritionalsecurityremainsama- cisionfarmingsupportscropdiversification,whichisa jor global concern, particularly in developing coun- keyfactorinimprovingdietarydiversity(17).AI-based trieswheremalnutritionandmicronutrientdeficien- decision support systems can recommend suitable ciesareprevalent(8,9).AItechnologiescontributeto cropcombinationsbasedonsoilconditions,climate improving food quality and diversity by supporting patterns,andmarketdemand(18,19).Byencouraging precisionfarmingandcropdiversification(10). This farmerstogrowavarietyofcropsratherthanrelyingon ensuresaccesstonutrient-richfoods,whichisessen- monoculturesystems,precisionfarmingpromotesthe tialforimprovingpublichealthoutcomes(8). Preci- availabilityofdiversefooditems,includingfruits,veg- sionagricultureenablesefficientuseofwater,fertiliz- etables,andpulses(18,19).Thisdiversityisessential ers,andpesticides,reducingenvironmentaldegrada- forreducingmalnutritionandaddressingmicronutri- tionandimprovingfoodsafety(11).Excessiveuseof entdeficiencies,particularlyinvulnerablepopulations agrochemicalshasbeenlinkedtohealthriskssuchas (18).Anothercriticalaspectofprecisionfarmingisits cancerandneurologicaldisorders,whichAIcanhelp contributiontosustainableagriculturalpractices(23). mitigatethroughtargetedapplications(12).Addition- Efficientresourceutilizationreduceswasteandmini- ally,AIenhancesagriculturalsupplychainsbyimprov- mizesenvironmentalimpacts(21).Forinstance,smart ingstorage,transportation,anddistributionsystems irrigationsystemspoweredbyAIensurethatwateris (13).Thisreducespost-harvestlossesandensuresthat appliedonlywhenandwhereitisneeded,reducingwa- foodreachesconsumersinatimelyandsafemanner, terwastageandconservingthisvitalresource(22,23). therebyimprovingfoodaccessibilityandaffordability Similarly,precisefertilizerapplicationpreventsnutri- (13). Despitetheseadvantages,theadoptionofAIin entrunoffintowaterbodies,therebyprotectingecosys- agriculturefaceschallengessuchashighcosts,limited temsandmaintainingsoilhealth(22,23).Theseprac- digitalliteracy,andlackofinfrastructureinruralareas ticescontributetolong-termagriculturalsustainability (14).
whilesupportingfoodproduction(21).Furthermore, precisionfarmingenhancesresiliencetoclimatevari- 1.1 PrecisionFarmingandNutritionalSecurity ability(22).Byprovidingreal-timedataandpredictive Precisionfarmingrepresentsoneofthemosttransfor- insights,AIsystemsenablefarmerstoanticipateand mativeapplicationsofartificialintelligenceinmodern respondtochangingweatherconditions,therebyre- agriculture,offeringadata-drivenapproachtomanag- ducingtherisksassociatedwithdroughts,floods,and ingcropproductionsystemswithenhancedefficiency temperaturefluctuations(24,21). Thisresilienceen- andsustainability(13,14)Thisapproachintegratesad- suresconsistentcropproduction,whichisessential vanced technologies such as sensors, drones, satel- formaintainingstablefoodsupplies,precisionfarming liteimaging,andgeographicinformationsystemsto servesasapowerfultoolforintegratingtechnological monitor real-time agricultural conditions, including innovationwithagriculturalpractices(24).Byimprov- soilmoisture,nutrientlevels,crophealth,andclimatic ingcropyield,enhancingnutritionalquality,promot- variables.(10)Bycollectingandanalyzingthesedata, ingdiversification,andensuringsustainableresource AI-poweredsystemsenablefarmerstomakeinformed Impacton Application AITechnologies Benefitsto KeyFunctions Healthand Area Used Agriculture Nutrition Monitoringsoil, Improvedyield, Enhanced Sensors,drones, PrecisionFarming crophealth, optimizedresource nutritional satelliteimaging weatherconditions use qualityofcrops Reducedcrop Saferfoodwith PestandDisease Machinelearning, Earlydetection losses, reduced Management computervision ofpestsanddiseases targetedpesticide chemicalresidues use Real-time soilmoisture Improved IoTsensors, Efficientwateruse, SmartIrrigation monitoring environmental predictiveanalytics reducedwastage andwater health management Dataanalytics, Forecasting Betterplanningand Ensuresfood CropYieldPrediction machinelearning cropproductivity resourceallocation availability AIanalytics, Improvedfood Supply Demandforecasting, Reducedfoodwaste, blockchain, accessibilityand ChainManagement logisticsoptimization efficientdistribution automation affordability Robotics, Planting,harvesting, Reducedlaborcost, Consistentfood AutomatedFarming AI-baseddecision spraying increasedefficiency production systems
AI-Based Environmental Long-Term Parameter/Aspect PublicHealthBenefit Intervention Impact Outcome Reducedwater Sustainablewater Smartirrigation wastage,improved Availabilityof WaterManagement resource systems groundwater cleanwater management conservation Reduced Improvedsoil Precisionnutrient SoilHealth soildegradation Safefoodproduction fertilityand management andnutrientrunoff productivity Reducedchemical Lowergreenhouse Reducedrespiratory Climatechange AirQuality inputthrough gasemissions diseases mitigation AImonitoring Targetedpesticide Decreased Reducedexposureto Healthier PollutionControl and environmental toxicchemicals ecosystems fertilizerapplication contamination Predictiveanalytics Reducedriskoffood ClimateChange Improvedresilience Climate-resilient and shortagesand Adaptation toextremeweather agriculturalsystems climatemodeling malnutrition Reduced Traceabilityand Improvedfoodsafety Strongerfood FoodSafety contamination qualitymonitoring andnutrition securitysystems insupplychains use,itplaysavitalroleinachievingbothfoodandnutri- plantdiseasesandpestinfestationsoftenrelyonvisual tionalsecurityinanincreasinglyresource-constrained inspectionandexpertknowledge,whichcanbetime- world(25,26).
consuming,labor-intensive,andpronetohumanerror (13). Incontrast, AItechnologies, particularlythose basedonmachinelearningandcomputervision,en- 1.2 AIinPestandDiseaseManagement ablerapidandaccuratedetectionofplanthealthissues Artificialintelligencehassignificantlytransformedpest atearlystages,evenbeforevisiblesymptomsbecome anddiseasemanagementinagriculturebyintroduc- severe(18,19).OneofthekeyapplicationsofAIinthis ing advanced tools for early detection, monitoring, domain is image recognition, where algorithms are andcontrol(15,17).Traditionalmethodsofidentifying trained to identify specific patterns associated with ityandpublichealth(25,26). Asthesetechnologies differentplantdiseasesandpests(24).Usingimages continue to evolve, they are expected to play an in- capturedbysmartphones, drones, orfieldcameras, creasinglyimportantroleinensuringsafeandefficient thesesystemscananalyzeleafdiscoloration,lesions, foodproductionsystems(27).
orinsectpresenceandprovideimmediatediagnostic results(21,22). Earlydetectioniscriticalinprevent- 1.3 SmartSupplyChainsandFoodAccessibility ingthespreadofdiseasesandminimizingcroplosses Artificial intelligence is revolutionizing food supply (25).Byidentifyingproblemsataninitialstage,farmers chainsbyimprovingefficiency,transparency,andreli- canimplementtimelyinterventionsthatprotectcrop abilityinthemovementofagriculturalproductsfrom healthandensurestableyields(25,26).Predictiveana- farmtoconsumer(12,13). Traditionalsupplychains lyticsfurtherenhancespestanddiseasemanagement oftenfacechallengessuchasinefficienciesinstorage, byforecastingpotentialoutbreaksbasedonenviron- transportationdelays, lackofreal-timeinformation, mentalconditions, historicaldata, andcropgrowth andsignificantpost-harvestlosses(17).AI-driventech- patterns(21).AImodelscananalyzevariablessuchas nologiesaddressthesechallengesbyintegratingdata temperature,humidity,rainfall,andsoilconditionsto analytics,automation,andpredictivemodelinginto predictthelikelihoodofpestinfestationsordiseaseoc- supplychainmanagement,therebyensuringthatfood currence(20).Thisproactiveapproachallowsfarmers reachesconsumersinatimelyandefficientmanner totakepreventivemeasuresratherthanreactingafter (14,15).OneoftheprimaryapplicationsofAIinsupply damagehasalreadyoccurred(19,20).Asaresult,crop chainsisdemandforecasting(22).Byanalyzinghistor- productivityismaintained,andtheriskoflarge-scale icalconsumptionpatterns,markettrends,andexter- lossesissignificantlyreduced(18).Anothermajorad- nalfactorssuchasweatherconditionsandeconomic vantageofAI-drivenpestmanagementisthereduction indicators,AIsystemscanaccuratelypredictfuturede- inexcessivepesticideuse(18).Conventionalpestcon- mandforvariousfoodproducts(16,17).Thisenables trolpracticesofteninvolvetheblanketapplicationof producers,distributors,andretailerstoplantheirop- chemicalpesticides,whichcanleadtoenvironmental erationsmoreeffectively,reducingtheriskofoverpro- pollution,soildegradation,andhealthrisksforboth ductionorshortages(15).Accuratedemandforecast- farmersandconsumers(25,26).AItechnologiesenable ingalsohelpsinmaintainingpricestability,whichises- targetedpesticideapplicationbyidentifyingspecificar- sentialforensuringfoodaffordability(3).AItechnolo- easorplantsthatrequiretreatment(22).Thisprecision giesalsoplayacrucialroleinreducingfoodwastage, reducesthequantityofchemicalsused, minimizing whichisamajorglobalissue.Significantquantitiesof theirnegativeimpactontheenvironmentandpromot- foodarelostduringstorageandtransportationdueto ingsaferfoodproductionsystems(25,26)Inadditionto poorhandling,inadequateinfrastructure,andlackof reducingchemicalinputs,AI-basedpestmanagement timelydecision-making.Smartsupplychainsystems supportsintegratedpestmanagementstrategiesthat usesensorsandreal-timemonitoringtoolstotrackthe combinebiological,cultural,andchemicalmethods conditionofperishablegoods, suchastemperature forsustainablecontrol(18,19).Forexample,AIsystems andhumiditylevels(26,27).Ifanydeviationisdetected, canrecommendtheuseofnaturalpredatorsorresis- immediatecorrectiveactionscanbetakentoprevent tantcropvarietiesbasedontheidentifiedpestspecies spoilage(19). Thisnotonlyreducesfoodwastebut (17). This holistic approach not only controls pests alsoimprovestheoverallefficiencyofthesupplychain effectivelybutalsopreserveecologicalbalanceand (12,13). Transportationandlogisticsarefurtheropti- biodiversity(12,13).ThebenefitsofAIinpestanddis- mizedthroughAI-basedrouteplanningandscheduling easemanagementextendbeyondagriculturetopub- systems(12).Thesesystemsanalyzetrafficconditions, lichealth(13,14,15). Byreducingpesticideresidues fuelefficiency,anddeliverytimelinestodeterminethe infoodandtheenvironment,thesetechnologiescon- mostefficientroutesfortransportinggoods.Bymin- tributetosaferfoodconsumptionandlowerexposure imizingdelaysandreducingtransportationcosts,AI to harmful chemicals (17,18). Moreover, preventing enhancestheoverallperformanceofthesupplychain. croplossesensuresfoodavailability,whichisessential This is particularly important for perishable agricul- formaintainingfoodsecurityandsupportingpopula- turalproductsthatrequiretimelydeliverytomaintain tionhealth,AI-drivenpestanddiseasemanagement theirquality(26,27).Improvedsupplychainefficiency representsasignificantadvancementinagricultural directlycontributestobetterfoodaccessibility,espe- practices(25,26).Throughearlydetection,predictive ciallyinlow-incomeandremoteregions(19). Byen- analytics,andtargetedinterventions,itenhancescrop suringthatfoodisdistributedefficientlyandreaches protectionwhilepromotingenvironmentalsustainabil- marketswithoutsignificantlosses,AIhelpsincrease theavailabilityoffoodproducts(18,19).Additionally, improvescropproductivitybutalsominimizesenvi- reducedwastageandoptimizedlogisticslowerover- ronmentalimpacts,contributingtohealthierecosys- allcosts,makingfoodmoreaffordableforconsumers tems(18). AnotherimportantaspectofAIinsustain- (13).Thisisacriticalfactorinaddressinghungerand abilityisthereductionofgreenhousegasemissions improvingnutritionaloutcomesinvulnerablepopula- (3).Byoptimizingfarmoperations,suchasmachinery tions(12). AIalsoenhancestransparencyandtrace- use,irrigation,andinputapplication,AIhelpsreduce abilitywithinsupplychains. Advancedtrackingsys- energyconsumptionandemissionsassociatedwith temsallowstakeholderstomonitorthemovementof agriculturalactivities(7,8).Additionally,AI-drivenpre- foodproductsateverystage,fromproductiontocon- cisionfarmingpracticesreducetheneedforchemical sumption(12,13).Thistransparencyhelpsinensuring inputs,furtherloweringtheenvironmentalfootprint foodsafetyandquality,asanycontaminationorissue ofagriculture(9,10).TheenvironmentalbenefitsofAI canbequicklyidentifiedandaddressedConsumers havedirectimplicationsforpublichealth. Improved alsobenefitfromincreasedtrustinthefoodsystem,as airandwaterqualityresultingfromreducedpollution theyhaveaccesstoinformationabouttheoriginand contributetobetterhealthoutcomesforcommunities qualityoftheproductstheyconsume(25,26)Insum- (6,7).Forexample,minimizingpesticideusereducesex- mary,AI-drivensmartsupplychainsplayavitalrolein posuretoharmfulchemicals,whichcanhaveadverse improvingfoodaccessibilityandreducinginefficien- effectsonhumanhealth. Similarly,preventingnutri- ciesinfooddistributionsystems(19). Byenhancing entrunoffprotectswaterbodiesfromcontamination, demandforecasting,minimizingwastage,optimizing ensuringtheavailabilityofcleandrinkingwater(23,24). logistics,andensuringtransparency,thesetechnolo- AIalsoplaysaroleinmitigatingtheimpactsofclimate giescontributesignificantlytoglobalfoodsecurityand change,whichiscloselylinkedtopublichealth(17).By publichealth(17,18).
enablingadaptiveagriculturalpractices,AIhelpsfarm- ersrespondtochangingclimaticconditions,reducing theriskofcropfailuresandfoodshortages(12).Stable 1.4 Environmental Sustainability and Public foodproductionisessentialformaintainingfoodsecu- Health rityandpreventingmalnutrition,whichisamajorpub- Artificialintelligenceisplayinganincreasinglyimpor- lichealthconcern(8).Furthermore,sustainableagri- tant role in promoting environmental sustainability culturalpracticessupportedbyAIcontributetolong- withinagriculturalsystems,withsignificantimplica- termecosystemhealth(6).Healthyecosystemsprovide tionsforpublichealth(12).Modernagriculturalprac- essentialservicessuchascleanair,water,andfertile ticesoftenplaceconsiderablepressureonnaturalre- soil,allofwhicharecriticalforhumanwell-being(11). sources, leading to issues such as soil degradation, Bypreservingtheseresources, AI-drivenagriculture waterscarcity,andgreenhousegasemissions(12,13). supportsbothenvironmentalsustainabilityandpub- AI-basedtechnologiesprovideinnovativesolutionsto lichealth,theintegrationofAIintoagricultureoffersa thesechallengesbyenablingmoreefficientandsus- comprehensiveapproachtoaddressingenvironmental tainableresourcemanagement(19). Oneofthekey andhealthchallenges(15).Byoptimizingresourceuse, contributionsofAItoenvironmentalsustainabilityis reducingpollution,andsupportingclimateresilience, theoptimizationofwaterusagethroughsmartirriga- AIcontributestothedevelopmentofsustainableagri- tionsystems(12,13).Thesesystemsusesensorsand culturalsystemsthatpromotebothecologicalbalance dataanalyticstomonitorsoilmoisturelevels,weather andhumanhealth(3).
conditions,andcropwaterrequirementsinrealtime (17). Basedonthisinformation,AIalgorithmsdeter- 2.Conclusion minethepreciseamountofwaterneededforirriga- tion,reducingwastageandensuringefficientuseof AI-drivenagricultureofferssignificantpotentialtoen- waterresources(23,24).Thisisparticularlyimportant hancefoodsecurity,nutritionaloutcomes,andpublic inregionsfacingwaterscarcity,whereefficientwater health(1).Byimprovingprecisionfarming,pestman- management is essential for sustaining agricultural agement,andsupplychainefficiency,AIcontributes production (25). AI also supports sustainable nutri- tosustainableandresilientagriculturalsystems(5,13). entmanagementbyoptimizingfertilizerapplication. However,challengessuchashighcosts,lackofaware- Overuseoffertilizerscanleadtosoildegradationand ness,andlimitedinfrastructuremustbeaddressedto waterpollutionduetonutrientrunoff(7).AIsystems ensurewidespreadadoption(14). Policymakersand analyzesoilhealthdataandcroprequirementstorec- stakeholdersmustpromoteinclusivestrategiestoin- ommend appropriate fertilizer quantities and appli- tegrateAIintoagricultureeffectively(15,20).AI-driven cationtimings(8,9).Thistargetedapproachnotonly agriculture represents a promising pathway toward achievingglobalfoodsecurityandimprovingpublic tionsofPeat-BasedGrowingMediaonCarbon healthoutcomes.Itsadoptioncancontributesignifi- BalanceandGreenhouseGasEmissionsinHorti- cantlytosustainabledevelopmentgoalsandlong-term culturalSystems.XploreEnvironment:AnInter- environmentalsustainability(19,20).
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