Image-Based Honeybee Colony Conditions Detection Using a Hybrid CNN–ANN Framework

Image-Based Honeybee Colony Conditions Detection Using a Hybrid CNN–ANN Framework | 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 Image-Based Honeybee Colony Conditions Detection Using a Hybrid CNN–ANN Framework Seloua HADDAOUI, Soheil VARASTEHPOUR, Salim CHIKHI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7839898/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Honeybee health is critical for agriculture and ecosystems, yet traditional hive inspections are time-consuming and prone to error. This paper presents a hybrid Deep Learning (DL) framework for image-based detection of six common honeybee conditions. The method integrates a dual-branch Convolutional Neural Network (CNN) for multi-scale feature extraction with a Multi-Layer Feedback Artificial Neural Network (MLFB-ANN) classifier, replacing the conventional Softmax layer to improve generalization on fine-grained classes. A curated dataset was used to train and evaluate the model. Experimental results show that the hybrid approach achieves 97.61% accuracy and a macro-F1 score of 0.96, outperforming a baseline CNN+Softmax model (93.6% accuracy). The proposed system demonstrates strong robustness across classes, particularly in reducing confusion between visually similar conditions such as Varroa and SHB. These findings highlight the potential of feedback-driven classifiers for challenging multi-class image recognition tasks and support the development of real-time, automated hive monitoring systems. Convolutional Neural Network Image Classification Multi-Layer Feedback ANN (MLFB-ANN) Bee Disease Detection Honeybee Monitoring Computer Vision in Apiculture Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Apr, 2026 Reviews received at journal 25 Apr, 2026 Reviews received at journal 05 Apr, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviewers invited by journal 29 Mar, 2026 Editor assigned by journal 16 Oct, 2025 Submission checks completed at journal 16 Oct, 2025 First submitted to journal 15 Oct, 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. 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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-7839898","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614571090,"identity":"f9fde8af-70bd-4c8e-afbf-09857afcf080","order_by":0,"name":"Seloua HADDAOUI","email":"data:image/png;base64,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","orcid":"","institution":"University of Abdelhamid Mehri (constantine 2), Algeria","correspondingAuthor":true,"prefix":"","firstName":"Seloua","middleName":"","lastName":"HADDAOUI","suffix":""},{"id":614571091,"identity":"49803094-7855-421b-bb26-09610ceaeb0e","order_by":1,"name":"Soheil VARASTEHPOUR","email":"","orcid":"","institution":"Unitec Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Soheil","middleName":"","lastName":"VARASTEHPOUR","suffix":""},{"id":614571093,"identity":"42150abb-a776-457a-9c8e-146d74fa92e5","order_by":2,"name":"Salim CHIKHI","email":"","orcid":"","institution":"University of Abdelhamid Mehri (constantine 2), Algeria","correspondingAuthor":false,"prefix":"","firstName":"Salim","middleName":"","lastName":"CHIKHI","suffix":""}],"badges":[],"createdAt":"2025-10-12 10:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7839898/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7839898/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106094166,"identity":"4b4a53a4-57f6-43d1-b7bc-60d7462f3baa","added_by":"auto","created_at":"2026-04-03 11:41:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":671769,"visible":true,"origin":"","legend":"","description":"","filename":"CNNsMLFBANN.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7839898/v1_covered_692e0370-c56a-49d0-8e58-eabe03a2c487.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Image-Based Honeybee Colony Conditions Detection Using a Hybrid CNN–ANN Framework","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":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Convolutional Neural Network, Image Classification, Multi-Layer Feedback ANN (MLFB-ANN), Bee Disease Detection, Honeybee Monitoring, Computer Vision in Apiculture","lastPublishedDoi":"10.21203/rs.3.rs-7839898/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7839898/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Honeybee health is critical for agriculture and ecosystems, yet traditional hive inspections are time-consuming and prone to error. 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