SegFormer Inspired Multi Head Spectral Attention with Edge Gating light weight model for Leaf Area Segmentation

SegFormer Inspired Multi Head Spectral Attention with Edge Gating light weight model for Leaf Area Segmentation | 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 SegFormer Inspired Multi Head Spectral Attention with Edge Gating light weight model for Leaf Area Segmentation A. Shamim Banu, S. Deivalakshmi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7496305/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 Accurate segmentation of leaf area is a critical task in plant phenotyping and precision agriculture, as it directly impacts yield estimation, disease monitoring, and weed management. Conventional Convolutional Neural Networks (CNNs), such as UNet and its variants, often struggle with capturing long range contextual dependencies and preserving fine structural boundaries, while pure transformer based architectures like the Vision Transformer (ViT) suffer from poor inductive bias and limited data efficiency. To overcome these challenges , we propose a SegFormer inspired model that integrates Edge Gated Multi Head Spectral Attention (EG MHSA) for robust leaf area segmentation. The spectral attention mechanism captures discriminative frequency domain representations across spectral bands, while the edge gating module enhances boundary preservation by adaptively fusing multiscale edge features. Evaluated on the benchmark CWFID dataset, the proposed model achieves superior performance with an F1score of 97.33%, IoU of 95.84%, and the lowest loss of 0.0395, outperforming UNet variants and transformer based baselines. Qualitative analysis further demonstrates its effectiveness in accurately delineating fine leaf boundaries under complex field conditions. The ablation results highlight the complementary contributions of spectral attention and edge gating in boosting segmentation performance. With its lightweight architecture, edge focused refinement, and strong generalization capability, the proposed approach sets a new benchmark for leaf area segmentation and provides a practical, scalable solution for agricultural applications. Leaf segmentation Precision agriculture Transformer Multi head spectral attention Edge gating SegFormer Deep learning 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-7496305","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":510181845,"identity":"75806d7f-fb0e-4386-b5c1-f65dacec31b4","order_by":0,"name":"A. Shamim Banu","email":"","orcid":"","institution":"National Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"A.","middleName":"Shamim","lastName":"Banu","suffix":""},{"id":510181846,"identity":"68541b10-0d75-4bf5-80c3-0262201848ce","order_by":1,"name":"S. Deivalakshmi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYDCCA4wNDAwVDDxgDg/xWs4w8PCwEa8FiBnbgKqJ1sJ3vLlN4ue8OzL28g2MD962EaFF8szBNsnebc9ADmM2nEuMFoMbic0GvNsOg7SwSfMSpeX+w2bDv3PAWth/E6flBmPjY94GiC3MRGmRPJPY+FjmGFDLscRmyTnniNDCd/z4g4Nvag7bszcfPvjhTRkRWpAAKE5HwSgYBaNgFFAHAAAoTjTCJlkqRwAAAABJRU5ErkJggg==","orcid":"","institution":"National Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"S.","middleName":"","lastName":"Deivalakshmi","suffix":""}],"badges":[],"createdAt":"2025-08-30 15:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7496305/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7496305/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100547706,"identity":"fe059d59-f50e-4832-b54d-73860de8343f","added_by":"auto","created_at":"2026-01-19 08:16:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":859144,"visible":true,"origin":"","legend":"","description":"","filename":"EGMHSAFINALCOPY.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7496305/v1_covered_8e33cacd-dd2e-4e42-845b-591d6e2d2ef1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SegFormer Inspired Multi Head Spectral Attention with Edge Gating light weight model for Leaf Area Segmentation","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":"Leaf segmentation, Precision agriculture, Transformer, Multi head spectral attention, Edge gating, SegFormer, Deep learning","lastPublishedDoi":"10.21203/rs.3.rs-7496305/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7496305/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurate segmentation of leaf area is a critical task in plant phenotyping and precision agriculture, as it directly impacts yield estimation, disease monitoring, and weed management. Conventional Convolutional Neural Networks (CNNs), such as UNet and its variants, often struggle with capturing long range contextual dependencies and preserving fine structural boundaries, while pure transformer based architectures like the Vision Transformer (ViT) suffer from poor inductive bias and limited data efficiency. To overcome these challenges , we propose a SegFormer inspired model that integrates Edge Gated Multi Head Spectral Attention (EG MHSA) for robust leaf area segmentation. The spectral attention mechanism captures discriminative frequency domain representations across spectral bands, while the edge gating module enhances boundary preservation by adaptively fusing multiscale edge features. Evaluated on the benchmark CWFID dataset, the proposed model achieves superior performance with an F1score of 97.33%, IoU of 95.84%, and the lowest loss of 0.0395, outperforming UNet variants and transformer based baselines. Qualitative analysis further demonstrates its effectiveness in accurately delineating fine leaf boundaries under complex field conditions. The ablation results highlight the complementary contributions of spectral attention and edge gating in boosting segmentation performance. With its lightweight architecture, edge focused refinement, and strong generalization capability, the proposed approach sets a new benchmark for leaf area segmentation and provides a practical, scalable solution for agricultural applications.","manuscriptTitle":"SegFormer Inspired Multi Head Spectral Attention with Edge Gating light weight model for Leaf Area Segmentation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-05 17:06:39","doi":"10.21203/rs.3.rs-7496305/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":"de75fc19-8b78-4824-9cbf-161c8403d8ca","owner":[],"postedDate":"September 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-17T03:53:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-05 17:06:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7496305","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7496305","identity":"rs-7496305","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}