Multi-Class Banana Leaf Disease Detection via KHO-YOLOv8

Multi-Class Banana Leaf Disease Detection via KHO-YOLOv8 | 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 Multi-Class Banana Leaf Disease Detection via KHO-YOLOv8 Muzammal Hussain, Sufyan Ahmad, Warda Saleemi, Hina Sattar, Muhammad Ahsan Rafiq This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6812770/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 Plant diseases initiate major agricultural challenges because they lead to 16\% of worldwide crop loss in farms. The vulnerability of bananas to diseases including Xanthomonas Wilt and Sigatoka leaf spot puts severe threats to food security because of their extensive damage potential. These diseases possess the risk of damage to the complete harvest so their impact can reach 100%. While deep learning models, particularly YOLO-based architecture, have demonstrated success in plant disease identification, key research gaps remain. One major challenge is the lack of large-scale, annotated datasets for banana leaf diseases, limiting the development and evaluation of robust AI models. Addressing these challenges is crucial, and this study aims to do so by creating a large dataset and developing a robust disease detection model. The dataset comprises more than 5000 samples categorized into three classes: Healthy, Xanthomonas Wilt infected, and Sigatoka leaf spot infected. This study examines a novel framework by employing advanced optimization techniques such as Krill Herd Optimization (KHO) for YOLOv8 and its variants. Our research findings highlight the exceptional performance of the KHO-YOLOv8 model, achieving an impressive accuracy of 96.47%. Agricultural Engineering Theoretical Computer Science Banana disease detection Sigatoka Negra Deep Learning YOLOv8 Krill Herds Optimization Full Text Additional Declarations The authors declare no competing interests. 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-6812770","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":465964632,"identity":"e3eb9032-2235-4949-a889-5c017013a63f","order_by":0,"name":"Muzammal Hussain","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYBADGQYJMC3BwA+iHjAwGBDSwgPXItnAwNiQQIIWoNIDBLTozkh/+Olmjh0Pg3SP8ccfNRZ5xsd7zB8kVNgYM7AfProBixazGwnJ0rnbknkYZM6YSfMckyg2O3PGsCHhTJoZA09a2g3sWg4AtTADHZZjxszAJpG47UaOYUNi22EbBgkeM+xaEpt/526rB2kBOuyfROLmGQS1JLMBbTkM0mIgwdsmkbhBAqLFDKeWM8/YrHO3HQdqSSuT5u2TSJxx5ljhDKBfjNlw+eV4+uPbuduq5Rgkkjd//PGtLrG/vXnDhw8VNob97IePYdMCB/YH0EXY8CkfBaNgFIyCUYAXAAATk2Lbj8KYLAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-7320-4413","institution":"Government College University Faisalabad, Sahiwal Campus","correspondingAuthor":true,"prefix":"","firstName":"Muzammal","middleName":"","lastName":"Hussain","suffix":""},{"id":465965145,"identity":"3155e5cf-f34a-4a12-8d48-569ac1a83b29","order_by":1,"name":"Sufyan Ahmad","email":"","orcid":"","institution":"Government College University Faisalabad, Sahiwal Campus","correspondingAuthor":false,"prefix":"","firstName":"Sufyan","middleName":"","lastName":"Ahmad","suffix":""},{"id":465965598,"identity":"d75f0691-6c6c-457d-9ad3-74a3aae275c8","order_by":2,"name":"Warda Saleemi","email":"","orcid":"","institution":"University of Sahiwal","correspondingAuthor":false,"prefix":"","firstName":"Warda","middleName":"","lastName":"Saleemi","suffix":""},{"id":465965599,"identity":"454ba67f-df84-4564-8856-77c1c5b7ec2c","order_by":3,"name":"Hina Sattar","email":"","orcid":"","institution":"University of Sahiwal","correspondingAuthor":false,"prefix":"","firstName":"Hina","middleName":"","lastName":"Sattar","suffix":""},{"id":465965600,"identity":"04bc3623-f467-42a4-893e-3f1a8b1fea85","order_by":4,"name":"Muhammad Ahsan Rafiq","email":"","orcid":"","institution":"Department of Physics and Robotics, Punjab Group of Colleges, Faisalabad, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Ahsan","lastName":"Rafiq","suffix":""}],"badges":[],"createdAt":"2025-06-03 15:20:12","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6812770/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6812770/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83901269,"identity":"6f366143-41e6-4c49-b213-5073c4a2874b","added_by":"auto","created_at":"2025-06-04 09:31:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":820790,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6812770/v1_covered_6cb4a33a-947f-4c60-bd50-9115edeeb3fa.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eMulti-Class Banana Leaf Disease Detection via KHO-YOLOv8\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Government College University Faisalabad","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":"Banana disease detection, Sigatoka Negra, Deep Learning, YOLOv8, Krill Herds Optimization","lastPublishedDoi":"10.21203/rs.3.rs-6812770/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6812770/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePlant diseases initiate major agricultural challenges because they lead to 16\\% of worldwide crop loss in farms. The vulnerability of bananas to diseases including Xanthomonas Wilt and Sigatoka leaf spot puts severe threats to food security because of their extensive damage potential. These diseases possess the risk of damage to the complete harvest so their impact can reach 100%. While deep learning models, particularly YOLO-based architecture, have demonstrated success in plant disease identification, key research gaps remain. One major challenge is the lack of large-scale, annotated datasets for banana leaf diseases, limiting the development and evaluation of robust AI models. Addressing these challenges is crucial, and this study aims to do so by creating a large dataset and developing a robust disease detection model. The dataset comprises more than 5000 samples categorized into three classes: Healthy, Xanthomonas Wilt infected, and Sigatoka leaf spot infected. This study examines a novel framework by employing advanced optimization techniques such as Krill Herd Optimization (KHO) for YOLOv8 and its variants. Our research findings highlight the exceptional performance of the KHO-YOLOv8 model, achieving an impressive accuracy of 96.47%.\u003c/p\u003e","manuscriptTitle":"Multi-Class Banana Leaf Disease Detection via KHO-YOLOv8","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-04 09:23:13","doi":"10.21203/rs.3.rs-6812770/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":"3dd0ea9a-592d-4db6-95f0-ba7f10c1afba","owner":[],"postedDate":"June 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49507322,"name":"Agricultural Engineering"},{"id":49507323,"name":"Theoretical Computer Science"}],"tags":[],"updatedAt":"2025-06-04T09:23:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-04 09:23:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6812770","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6812770","identity":"rs-6812770","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}