Study on Accurate Grading Prediction Model of Tobacco Weather Fleck Based on Internet of Things

Study on Accurate Grading Prediction Model of Tobacco Weather Fleck Based on Internet of Things | 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 Article Study on Accurate Grading Prediction Model of Tobacco Weather Fleck Based on Internet of Things Jiazhao Sun, Xiaoli Hao, Yuao Ran, GOH MARK, Zhenguo Wang, Yabo Jin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6175007/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Precise prediction of crop disease trends and accurate grading identification of diseases based on information technology represent a significant challenge in the application of IoT devices in agricultural production. Addressing this issue necessitates the development of predictive models for the efficient representation of Internet of Things (IoT) data. In this context, a graded precision forecasting model for Tobacco weather fleck has been developed. Additionally, a graded recognition model has been constructed to identify different levels of disease severity based on the aforementioned grading system. We utilized meteorological data collected from field Internet of Things (IoT) devices and employed the Generalized Additive Model (GAM) to identify factors significantly associated with the occurrence of this disease. The proposed composite model, which leverages the search capability of the Grey Wolf Optimizer (GWO) and the feature extraction advantage of Convolutional Neural Networks (CNN), optimized the Long Short-Term Memory (LSTM) model (GWO-CNN-LSTM) for the best grading prediction effect of tobacco climate spot disease (accuracy rate of 85.46%). The GoogleNet model, optimized with the Convolutional Block Attention Module (CBAM) and based on the Inception-ResNet-v2, achieved the highest accuracy rate for disease grading recognition (92.40%), which was significantly higher than the manual recognition accuracy rate (83.00%; P < 0.05). The experimental results demonstrate the precise prediction and identification of varying degrees of tobacco climate spot disease occurrence. The proposed model makes a positive contribution to the knowledge system of crop disease grading prediction and the advancement of Internet of Things (IoT) agriculture. Earth and environmental sciences/Ecology/Agri ecology Physical sciences/Mathematics and computing/Scientific data Physical sciences/Mathematics and computing/Information technology Accurate prediction Field Internet of Things (IoT) Hierarchical identification Significance factors Tobacco weather fleck Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Jun, 2025 Reviews received at journal 09 Jun, 2025 Reviews received at journal 16 May, 2025 Reviews received at journal 16 May, 2025 Reviewers agreed at journal 16 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviewers invited by journal 11 Apr, 2025 Editor assigned by journal 03 Apr, 2025 Submission checks completed at journal 24 Mar, 2025 First submitted to journal 24 Mar, 2025 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-6175007","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":441878467,"identity":"10e27193-b8d6-40d3-a284-a9cbed3d7a48","order_by":0,"name":"Jiazhao Sun","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Jiazhao","middleName":"","lastName":"Sun","suffix":""},{"id":441878468,"identity":"f8b7cb09-56b7-460f-b143-e219afdbc2de","order_by":1,"name":"Xiaoli Hao","email":"","orcid":"","institution":"Guizhou Minzu University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Hao","suffix":""},{"id":441878469,"identity":"d394d534-7b9c-4ec1-9641-fac9a13bac89","order_by":2,"name":"Yuao Ran","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Yuao","middleName":"","lastName":"Ran","suffix":""},{"id":441878470,"identity":"12bade98-c371-460d-83bb-2ee95b2160e6","order_by":3,"name":"GOH MARK","email":"","orcid":"","institution":"NUS Business School, National University of Singapore","correspondingAuthor":false,"prefix":"","firstName":"GOH","middleName":"","lastName":"MARK","suffix":""},{"id":441878471,"identity":"2acd7ca8-65a4-4def-851e-6cf862730e7d","order_by":4,"name":"Zhenguo Wang","email":"","orcid":"","institution":"China National Tobacco Corporation Fengjie Branch","correspondingAuthor":false,"prefix":"","firstName":"Zhenguo","middleName":"","lastName":"Wang","suffix":""},{"id":441878472,"identity":"6aafc22e-cfd9-4939-8f0a-fa196c06c8f0","order_by":5,"name":"Yabo Jin","email":"","orcid":"","institution":"China Tobacco Guangxi Industry Corporation Limited","correspondingAuthor":false,"prefix":"","firstName":"Yabo","middleName":"","lastName":"Jin","suffix":""},{"id":441878473,"identity":"e6e28515-470c-43f7-ad95-710e55102563","order_by":6,"name":"Wei Ding","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYDCCwyCiAsKWIEHLGZK0HABixjZStPAd5z38mndeXeKGA8wHb/Mw2OUR1CJ5mC/NmnfbYaAWtmRrHobkYoJaDA7zmBnnbjuQu+EAj5k0D8OBxAbitMypA2rh/0a0FuPHuQ3MIFvYiNMiCbSF+c+xw/UzD7MZW84xSCashe/8GeOPM2rqjPmONz+88abCjrAWIGCDRAcz2J1EqAep/UCculEwCkbBKBixAADvSzuXpPrBTgAAAABJRU5ErkJggg==","orcid":"","institution":"Southwest University","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Ding","suffix":""}],"badges":[],"createdAt":"2025-03-07 05:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6175007/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6175007/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80658302,"identity":"f5de89f0-fe28-44d3-a246-45883c044707","added_by":"auto","created_at":"2025-04-15 16:05:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1142805,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6175007/v1_covered_6813540e-3834-4fe7-b3a6-447f97d1b62d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Study on Accurate Grading Prediction Model of Tobacco Weather Fleck Based on Internet of Things","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Accurate prediction, Field Internet of Things (IoT), Hierarchical identification, Significance factors, Tobacco weather fleck","lastPublishedDoi":"10.21203/rs.3.rs-6175007/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6175007/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrecise prediction of crop disease trends and accurate grading identification of diseases based on information technology represent a significant challenge in the application of IoT devices in agricultural production. Addressing this issue necessitates the development of predictive models for the efficient representation of Internet of Things (IoT) data. In this context, a graded precision forecasting model for Tobacco weather fleck has been developed. Additionally, a graded recognition model has been constructed to identify different levels of disease severity based on the aforementioned grading system. We utilized meteorological data collected from field Internet of Things (IoT) devices and employed the Generalized Additive Model (GAM) to identify factors significantly associated with the occurrence of this disease. The proposed composite model, which leverages the search capability of the Grey Wolf Optimizer (GWO) and the feature extraction advantage of Convolutional Neural Networks (CNN), optimized the Long Short-Term Memory (LSTM) model (GWO-CNN-LSTM) for the best grading prediction effect of tobacco climate spot disease (accuracy rate of 85.46%). The GoogleNet model, optimized with the Convolutional Block Attention Module (CBAM) and based on the Inception-ResNet-v2, achieved the highest accuracy rate for disease grading recognition (92.40%), which was significantly higher than the manual recognition accuracy rate (83.00%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The experimental results demonstrate the precise prediction and identification of varying degrees of tobacco climate spot disease occurrence. The proposed model makes a positive contribution to the knowledge system of crop disease grading prediction and the advancement of Internet of Things (IoT) agriculture.\u003c/p\u003e","manuscriptTitle":"Study on Accurate Grading Prediction Model of Tobacco Weather Fleck Based on Internet of Things","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-15 15:33:10","doi":"10.21203/rs.3.rs-6175007/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-11T07:45:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-09T08:54:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-17T02:59:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-16T06:21:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326636482717207887061286245158153616156","date":"2025-05-16T05:48:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264890164111031296372079822974467149665","date":"2025-05-14T07:38:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302001774893844852794643598853198551392","date":"2025-05-14T05:45:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-11T23:02:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-03T08:10:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-24T07:11:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-24T07:09:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ebf5b606-ef9e-48ce-ac95-7a1cc1197d6b","owner":[],"postedDate":"April 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":47053830,"name":"Earth and environmental sciences/Ecology/Agri ecology"},{"id":47053831,"name":"Physical sciences/Mathematics and computing/Scientific data"},{"id":47053832,"name":"Physical sciences/Mathematics and computing/Information technology"}],"tags":[],"updatedAt":"2025-10-13T08:38:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-15 15:33:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6175007","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6175007","identity":"rs-6175007","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}