PSHB-Net: An Attention-Based Deep Learning Model for Early Detection of Polyphagous Shot Hole Borer Infestations in Forestry

PSHB-Net: An Attention-Based Deep Learning Model for Early Detection of Polyphagous Shot Hole Borer Infestations in Forestry | 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 PSHB-Net: An Attention-Based Deep Learning Model for Early Detection of Polyphagous Shot Hole Borer Infestations in Forestry Rabbiya Younas, Hafiz Muhammad Raza Ur Rehman, Gyu Sang Choi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6528659/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 The Polyphagous Shot Hole Borer (PSHB) is a highly invasive beetle that has been spreading like an epidemic across agricultural and forestry landscapes in recent years. Its rapid and destructive spread has turned it into a major global threat, causing widespread damage that continues to grow with time. Countries like South Africa, the United States, and Australia have implemented extensive measures to control the spread of PSHB, including the establishment of specialized agricultural support centers for early detection. However, there is still a strong need for making PSHB detection more accessible, allowing even non-experts to easily identify infections at an early stage. Artificial Intelligence (AI) has shown great promise in plant disease detection, but a major challenge in the case of PSHB was the lack of a suitable dataset for training AI models. In our proposed work, we first created a dedicated dataset by collecting images of trees infected with PSHB. We applied a range of preprocessing techniques to refine the dataset and prepare it for AI applications. Building on this, we developed a novel AI-based method, where we trained a deep learning model using a multi-convolutional layer network combined with a Fourier transformation layer. Additionally, an attention mechanism and advanced feature extraction techniques were incorporated to further boost model performance. As a result, Our proposed approach achieved an impressive accuracy of 91% in detecting PSHB infections, showing the potential of AI to offer a simple, efficient, and highly accurate solution for early disease detection. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Natural hazards Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Nanoscience and technology 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-6528659","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":447985579,"identity":"902cf4a4-6131-492c-becf-479e3ffe4f50","order_by":0,"name":"Rabbiya Younas","email":"","orcid":"","institution":"Yeungnam University","correspondingAuthor":false,"prefix":"","firstName":"Rabbiya","middleName":"","lastName":"Younas","suffix":""},{"id":447985581,"identity":"b6a3a6c6-88bb-4e7c-95a3-4d636d7380ac","order_by":1,"name":"Hafiz Muhammad Raza Ur Rehman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYFADCRBRAeMdIFrLGZK1MLYRoUV39uHHH3623cvnl+5O/Fw473CewQHmhx8YztzDqcXsXJqBYW9bseXMOWc3S8/cdrjY4ACbsQTDjWLcWs4wGCTwtiUYGNzI3SDNu+1w4oYDDGYMDB8S8Ghh/3DwL1CL/Y3czb9554C0sH8joIXHsBlsi0TuNmneBpAWHqAtN/BqKWaWOZdgIHEjd5s1z7H0xJmHeYolEs7gddjmj2/KEgz4Z+Ruvs1TY53Yd7x944cPx3BrQQfNDAzMQIp4DQwMdSSoHQWjYBSMgpECAEHAWMJ4wLeiAAAAAElFTkSuQmCC","orcid":"","institution":"Yeungnam University","correspondingAuthor":true,"prefix":"","firstName":"Hafiz","middleName":"Muhammad Raza Ur","lastName":"Rehman","suffix":""},{"id":447985584,"identity":"b1ac1477-8a20-412f-b3c3-0d563ee5f054","order_by":2,"name":"Gyu Sang Choi","email":"","orcid":"","institution":"Yeungnam University","correspondingAuthor":false,"prefix":"","firstName":"Gyu","middleName":"Sang","lastName":"Choi","suffix":""}],"badges":[],"createdAt":"2025-04-25 11:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6528659/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6528659/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81844755,"identity":"b3df8a66-8e33-44d5-8bef-ce758d5c4730","added_by":"auto","created_at":"2025-05-02 17:21:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":933356,"visible":true,"origin":"","legend":"","description":"","filename":"TemplateforsubmissionstoScientificReports1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6528659/v1_covered_592d5626-8700-4204-b5d8-03b05245c98b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"PSHB-Net: An Attention-Based Deep Learning Model for Early Detection of Polyphagous Shot Hole Borer Infestations in Forestry","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-6528659/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6528659/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The Polyphagous Shot Hole Borer (PSHB) is a highly invasive beetle that has been spreading like an epidemic across agricultural and forestry landscapes in recent years. 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