Pollen Grain Intensity Identification in the Air using YOLO and RNN: A Review

Pollen Grain Intensity Identification in the Air using YOLO and RNN: A Review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pollen Grain Intensity Identification in the Air using YOLO and RNN: A Review Umamaheswari E, Kanchana Devi V, Sruthakeerthi B, Adwaita Jha, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5179016/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Pollen allergies, also known as hay fever or allergic rhinitis, impact millions of people worldwide, especially during peak seasons when pollen concentrations in the air are elevated. Traditional methods of monitoring pollen levels, such as manual counting and analysis of pollen traps, often result in delayed reports that do not provide real-time data. This limitation leaves individuals with allergies vulnerable to sudden increases in pollen levels. Furthermore, regional variations in pollen concentrations and the influence of environmental factors such as wind patterns, temperature, and humidity make it challenging to offer accurate, localized pollen forecasts. With the advent of advanced machine learning technologies, there is now an opportunity to transform the way pollen is detected and predicted, providing more immediate and personalized solutions for allergy sufferers. The integration of YOLO and RNN models offers a groundbreaking approach to this problem, leveraging both real-time visual analysis and predictive analytics to deliver timely and accurate pollen data. The YOLO model, widely recognized for its ability to perform real-time object detection, is particularly suited for detecting and classifying pollen grains in outdoor environments. By deploying cameras in strategic locations—such as near parks, forests, or urban areas with high vegetation—this system can capture continuous video feeds or images of the air. YOLO then processes these visual inputs to detect pollen particles in real time, distinguishing them from other airborne particulates like dust or pollution. This visual detection is crucial for 1providing immediate updates on pollen presence in the air. The RNN model complements this by offering a predictive component, using historical pollen data, environmental factors, and trends to forecast future pollen levels. MSC Classification: 30C45 , 30C50 Pollen allergies YOLO RNN deep learning pollen intensity real-time detection forecasting allergy management YOLO Algorithm Pollen Data analysis Dynamic modelling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5179016","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":362065014,"identity":"b078d8fa-333a-42c4-b9f5-55b453067f5f","order_by":0,"name":"Umamaheswari 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Review","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pollen allergies, YOLO, RNN, deep learning, pollen intensity, real-time detection, forecasting, allergy management , YOLO Algorithm, Pollen Data analysis, Dynamic modelling","lastPublishedDoi":"10.21203/rs.3.rs-5179016/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5179016/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePollen allergies, also known as hay fever or allergic rhinitis, impact millions of\u0026nbsp;people worldwide, especially during peak seasons when pollen concentrations in\u0026nbsp;the air are elevated. Traditional methods of monitoring pollen levels, such as\u0026nbsp;manual counting and analysis of pollen traps, often result in delayed reports that\u0026nbsp;do not provide real-time data. This limitation leaves individuals with allergies\u0026nbsp;vulnerable to sudden increases in pollen levels. Furthermore, regional variations\u0026nbsp;in pollen concentrations and the influence of environmental factors such as wind\u0026nbsp;patterns, temperature, and humidity make it challenging to offer accurate, localized pollen forecasts. With the advent of advanced machine learning technologies,\u0026nbsp;there is now an opportunity to transform the way pollen is detected and predicted, providing more immediate and personalized solutions for allergy sufferers.\u0026nbsp;The integration of YOLO and RNN models offers a groundbreaking approach to\u0026nbsp;this problem, leveraging both real-time visual analysis and predictive analytics\u0026nbsp;to deliver timely and accurate pollen data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe YOLO model, widely recognized for its ability to perform real-time object\u0026nbsp;detection, is particularly suited for detecting and classifying pollen grains in outdoor environments. By deploying cameras in strategic locations—such as near\u0026nbsp;parks, forests, or urban areas with high vegetation—this system can capture\u0026nbsp;continuous video feeds or images of the air. YOLO then processes these visual\u0026nbsp;inputs to detect pollen particles in real time, distinguishing them from other\u0026nbsp;airborne particulates like dust or pollution. This visual detection is crucial for\u0026nbsp;1providing immediate updates on pollen presence in the air. The RNN model complements this by offering a predictive component, using historical pollen data,\u0026nbsp;environmental factors, and trends to forecast future pollen levels.\u003c/p\u003e\n\u003cp\u003eMSC Classification: 30C45 , 30C50\u003c/p\u003e","manuscriptTitle":"Pollen Grain Intensity Identification in the Air using YOLO and RNN: A Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-15 11:43:32","doi":"10.21203/rs.3.rs-5179016/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6bd2af2d-8927-4116-b5ba-d03a34f11119","owner":[],"postedDate":"November 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-30T01:23:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-15 11:43:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5179016","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5179016","identity":"rs-5179016","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}