A Comprehensive Hybrid Model for Apple Fruit Disease Detection using Multi-Architecture Feature Extraction

A Comprehensive Hybrid Model for Apple Fruit Disease Detection using Multi-Architecture Feature Extraction | 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 A Comprehensive Hybrid Model for Apple Fruit Disease Detection using Multi-Architecture Feature Extraction Anil Sandhi, Dr. Rajeev Kumar, Prof. Dinesh Kumar, Dr. Reeta Bhardwaj This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5295240/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 agriculture industry is critical to the global economy, with product quality having a direct impact on marketability and waste management. Apples, one of the most extensively produced fruits, are affected by a variety of diseases that can reduce productivity and quality. Accurate diagnosis of diseases is critical, but traditional manual approaches are time-consuming, error-prone, and ineffective. Inadequate labeled data and a wide range of disease symptoms make it necessary to design an automated, robust, and accurate system. This article describes a hybrid model for Apple Fruit Disease Detection (HMAFDD) that combines the strengths of three pre-trained convolutional neural network (CNN) models: ResNet50, DenseNet121, and EfficientNetB0. It accomplish this by using multi-architecture feature extraction. The hybrid system, which combines both models, is able to recognize a wide range of features, from simple textures to complex patterns unique to a certain diseases. Grad-CAM, or gradient-weighted class activation mapping, creates heatmaps that highlight significant regions for prediction, which enhances the interpretability of the model.To increase robustness and accuracy, techniques such as spectral-shifted adversarial perturbation for data augmentation and spectrally-weighted global average pooling for feature aggregation are used. This technique provides 99.75% accuracy with minimal processing needs, making it acceptable for real-time applications in agricultural situations. This considerably improves apple disease management. Deep learning apple fruit disease convolutional neural network(CNN) Resnet50 Densenet121 EfficientnetB0 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-5295240","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":370933456,"identity":"54b88424-2c05-4ae2-9af2-5dcee16244d2","order_by":0,"name":"Anil Sandhi","email":"","orcid":"","institution":"DAV Institute of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Anil","middleName":"","lastName":"Sandhi","suffix":""},{"id":370933458,"identity":"fe1fc004-8522-41ba-855f-2b4cc3acf7ef","order_by":1,"name":"Dr. Rajeev Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYHCCBBDB2ABCDBVAJjNzAwlaDpwBaWEkqIUBqgUIDrYh2DiBefuBhw8+tjHI9s9Ibnz8cV5tNH87UMuPim04tcicSUg2nNnGYDzjRmKzwcFtx3NnHGZsYOw5cxunFgmGhDRpnjMMiQ1nDrZJHNx2LLcBqIWZsQ2PFv4H6b//ALXMP3Ow/cfBOcdy5xPUIpGQxgwM28QNxxvbGA421ORuIKzlQbJkT4WE8cbjjc0SZ44dyN0I1HIQr1/4cxI//DCwkZ13mP3hh4qautx55w8ffPCjArcWBgaeBHAoQMFhMHkAj3ogYEeRr8OveBSMglEwCkYkAAAegWOCpY1TEQAAAABJRU5ErkJggg==","orcid":"","institution":"DAV Institute of Engineering and Technology","correspondingAuthor":true,"prefix":"Dr.","firstName":"Rajeev","middleName":"","lastName":"Kumar","suffix":""},{"id":370933459,"identity":"6727090b-d252-4888-9375-a852657c05b4","order_by":2,"name":"Prof. Dinesh Kumar","email":"","orcid":"","institution":"Central University of Jammu","correspondingAuthor":false,"prefix":"","firstName":"Prof.","middleName":"Dinesh","lastName":"Kumar","suffix":""},{"id":370933461,"identity":"6ccf6a7f-e313-434d-a049-c98395148ee1","order_by":3,"name":"Dr. Reeta Bhardwaj","email":"","orcid":"","institution":"DAV Institute of Engineering and Technology","correspondingAuthor":false,"prefix":"Dr.","firstName":"Reeta","middleName":"","lastName":"Bhardwaj","suffix":""}],"badges":[],"createdAt":"2024-10-19 15:23:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5295240/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5295240/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68051309,"identity":"e4b94939-b8b6-4ba1-8f94-77b27bad7a2a","added_by":"auto","created_at":"2024-11-01 21:16:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1509725,"visible":true,"origin":"","legend":"","description":"","filename":"snarticletemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5295240/v1_covered_e3494ac0-c009-4fe2-ab88-ba5a3a638e79.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Comprehensive Hybrid Model for Apple Fruit Disease Detection using Multi-Architecture Feature Extraction","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":"Deep learning, apple fruit disease, convolutional neural network(CNN), Resnet50, Densenet121, EfficientnetB0","lastPublishedDoi":"10.21203/rs.3.rs-5295240/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5295240/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The agriculture industry is critical to the global economy, with product quality having a direct impact on marketability and waste management. Apples, one of the most extensively produced fruits, are affected by a variety of diseases that can reduce productivity and quality. Accurate diagnosis of diseases is critical, but traditional manual approaches are time-consuming, error-prone, and ineffective. Inadequate labeled data and a wide range of disease symptoms make it necessary to design an automated, robust, and accurate system. This article describes a hybrid model for Apple Fruit Disease Detection (HMAFDD) that combines the strengths of three pre-trained convolutional neural network (CNN) models: ResNet50, DenseNet121, and EfficientNetB0. It accomplish this by using multi-architecture feature extraction. The hybrid system, which combines both models, is able to recognize a wide range of features, from simple textures to complex patterns unique to a certain diseases. 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