Noise reduction method for agricultural monitoring system signals based on Adaptive Kalman Filtering

Noise reduction method for agricultural monitoring system signals based on Adaptive Kalman Filtering | 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 Noise reduction method for agricultural monitoring system signals based on Adaptive Kalman Filtering yuhang wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8765244/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract The smart agricultural monitoring system is playing a crucial role in modern agriculture; it provides accurate and detailed information about fields and crops. However, relatively high noise outside or inside the system affects data analysis and signal transmission, reducing the system’s overall precision. Existing research that addresses noise reduction in the field of agricultural systems is limited. Therefore, a noise reduction method for agricultural monitoring system based on Adaptive Kalman Filtering is proposed. This method achieves precise noise reduction for changing parameters such as soil temperature and humidity, and it achieves moisture monitoring by real-time estimation of the process noise variance ( Q) and observation noise variance (R) of agricultural monitoring signals as well as in the method of dynamic adjustment of Kalman gain. In performance tests, compared with traditional Kalman Filtering and SMA, the RMSE of Adaptive Kalman Filtering is 0.56–1.12%; the rate of smoothness of data is 0.41%, with relatively fastest response time at about 10 seconds. According to experimental results, Adaptive Kalman Filtering has excellent smoothness of data, and quick response effect on mutation occurrence. And Adaptive Kalman Filtering can effectively adapt to the time-varying interference in agricultural environment compared with traditional Kalman Filtering, which can be flexibly applied to agricultural monitoring systems. Physical sciences/Engineering Physical sciences/Mathematics and computing Noise reduction Agricultural monitoring system Adaptive Kalman Filtering Smart agriculture Full Text Additional Declarations No competing interests reported. Supplementary Files data.zip Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 31 Mar, 2026 Editor invited by journal 10 Feb, 2026 Submission checks completed at journal 07 Feb, 2026 First submitted to journal 07 Feb, 2026 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-8765244","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":620980092,"identity":"79f10a4f-4335-453b-a8fe-b6d3e07aea17","order_by":0,"name":"yuhang wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACeWaGhAMf/9nw8DMzHyBOi2E7w8OHM9jS5CTb2RKItOY842NjHrbDxgbneQyI08HYzJwmOYPncOKGwzwfb7xhsJPTbSCghZ2ZLU3ig0R64szDvJst5zAkG5sdIGgLD9AWA+vEvsO826R5GA4kbiOkheEw/zdpngTmxIbDPM+I1QJ0C88BZ2OBwzxsxGkxbGZIfDizARjIzWzGlnMMiPCLPP8BYFQ2AKOS//DDG28q7OQIakEBEsRGDbIWUnWMglEwCkbBiAAAEoxBsY4024kAAAAASUVORK5CYII=","orcid":"","institution":"Guizhou University of Finance and Economics","correspondingAuthor":true,"prefix":"","firstName":"yuhang","middleName":"","lastName":"wang","suffix":""}],"badges":[],"createdAt":"2026-02-02 12:57:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8765244/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8765244/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106959995,"identity":"1fadca3e-4b1b-488f-b4aa-6e22301b3c37","added_by":"auto","created_at":"2026-04-15 09:17:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":659420,"visible":true,"origin":"","legend":"","description":"","filename":"MANUSCRIPT20260112revised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8765244/v1_covered_3a6737b2-7b2d-4d0d-8414-d251af8afe5d.pdf"},{"id":106830254,"identity":"caaa1bec-062c-492d-82c0-d29cf466f480","added_by":"auto","created_at":"2026-04-13 23:19:57","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":670517,"visible":true,"origin":"","legend":"","description":"","filename":"data.zip","url":"https://assets-eu.researchsquare.com/files/rs-8765244/v1/a1196b96e5e24a939fc0342e.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Noise reduction method for agricultural monitoring system signals based on Adaptive Kalman Filtering","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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":"Noise reduction, Agricultural monitoring system, Adaptive Kalman Filtering, Smart agriculture","lastPublishedDoi":"10.21203/rs.3.rs-8765244/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8765244/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe smart agricultural monitoring system is playing a crucial role in modern agriculture; it provides accurate and detailed information about fields and crops. However, relatively high noise outside or inside the system affects data analysis and signal transmission, reducing the system\u0026rsquo;s overall precision. Existing research that addresses noise reduction in the field of agricultural systems is limited. Therefore, a noise reduction method for agricultural monitoring system based on Adaptive Kalman Filtering is proposed. This method achieves precise noise reduction for changing parameters such as soil temperature and humidity, and it achieves moisture monitoring by real-time estimation of the process noise variance (\u003cem\u003eQ)\u003c/em\u003e and observation noise variance \u003cem\u003e(R)\u003c/em\u003e of agricultural monitoring signals as well as in the method of dynamic adjustment of Kalman gain. In performance tests, compared with traditional Kalman Filtering and SMA, the RMSE of Adaptive Kalman Filtering is 0.56\u0026ndash;1.12%; the rate of smoothness of data is 0.41%, with relatively fastest response time at about 10 seconds. According to experimental results, Adaptive Kalman Filtering has excellent smoothness of data, and quick response effect on mutation occurrence. And Adaptive Kalman Filtering can effectively adapt to the time-varying interference in agricultural environment compared with traditional Kalman Filtering, which can be flexibly applied to agricultural monitoring systems.\u003c/p\u003e","manuscriptTitle":"Noise reduction method for agricultural monitoring system signals based on Adaptive Kalman Filtering","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-13 23:19:54","doi":"10.21203/rs.3.rs-8765244/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-07T09:28:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T19:13:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-10T12:23:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-07T14:44:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-07T14:33:44+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":"934e9689-4fde-46e7-90a9-b8d9896c164e","owner":[],"postedDate":"April 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66089612,"name":"Physical sciences/Engineering"},{"id":66089613,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-13T23:19:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-13 23:19:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8765244","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8765244","identity":"rs-8765244","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}