Few-Shot Learning for Plant Disease Detection using DeepBDC

Few-Shot Learning for Plant Disease Detection using DeepBDC | 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 Few-Shot Learning for Plant Disease Detection using DeepBDC Rakesh Ranjan, Jyoti Prakash Singh, Ankit Kumar Titoriya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6569821/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Sep, 2025 Read the published version in Soft Computing → Version 1 posted 4 You are reading this latest preprint version Abstract Plant disease detection in low-data scenarios is a major challenge for computer vision applications in agriculture. Traditional deep learning needs big, labeled datasets, but these are often not available for rare or new plant diseases. To address this issue, this paper presents a few-shot learning (FSL) method for classifying plant diseases using an improved Deep Brownian Distance Covariance (DeepBDC) framework. The model uses a ResNet-12 as a backbone network which uses a Convolutional Block Attention Module (CBAM) to help the network focus on important spatial and channel-wise features. In network, dropout regularization is also applied to reduce overfitting, which is common in few-shot tasks. The DeepBDC module is improved by using L2 normalization and temperature scaling, which makes the feature representations more stable and better for classification. The model uses cosine similarity for 1-shot learning and Gaussian kernel similarity for 5-shot and 10-shot settings. The method is evaluated on two public plant disease datasets, PlantVillage and CCMT, using standard 5-way classification with 15 queries per class and 2000 episodes for each configuration. On the PlantVillage dataset, the model gets 46.53% accuracy for 1-shot, 65.86% for 5-shot, and 69.67% for 10-shot tasks. On the CCMT dataset, it reaches 40.19%, 49.19%, and 53.67% accuracy for the same settings. These results show that the proposed method is a useful way to detect plant diseases when there is less data. Few-shot Learning Plant Disease Computer Vision Image Classification Full Text Cite Share Download PDF Status: Published Journal Publication published 15 Sep, 2025 Read the published version in Soft Computing → Version 1 posted Reviewers agreed at journal 13 May, 2025 Reviewers invited by journal 13 May, 2025 Editor invited by journal 10 May, 2025 First submitted to journal 30 Apr, 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. 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-6569821","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":455834880,"identity":"d2d4108d-9bb4-439b-be51-7ea8d512faea","order_by":0,"name":"Rakesh Ranjan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIiWNgGAWjYFACNgYGxgYgLcFgcOADkOaHcIGAmbAWw4MzgLRkA2NjA7FajA/zAGmDA3BrsAPz9mOJD3/uuMdgLt28AWiLnb3x7eb2xwUMdvIM7LwHsGmROZN22Jj3TDGD5ZxjBUC/JCduu3OwsXkGQ7JhAzNfAjYtEgzpbdKMbQkMBjdyDIC2HEgwu5HY2MzDwJzAwMxjgFUL//P2nz+hWoB+OWBvPAOspR63Fom0Ywy8SFoYN0iAtRzGo+VZsjRQC4/ljLSCgzMMkhNnAB02e4bBccM2nA5LM/wIdJicuUTy5g8fKuzs+WekP/hcUFEtz89/BqsWGIAaCFXDDGKw4VOPpBimZRSMglEwCkYBAgAAozhci3jMT84AAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0004-2513-765X","institution":"National Institute of Technology Patna","correspondingAuthor":true,"prefix":"","firstName":"Rakesh","middleName":"","lastName":"Ranjan","suffix":""},{"id":455834881,"identity":"1c1635d4-7bef-4079-b484-863528abadc8","order_by":1,"name":"Jyoti Prakash Singh","email":"","orcid":"","institution":"National Institute of Technology Patna","correspondingAuthor":false,"prefix":"","firstName":"Jyoti","middleName":"Prakash","lastName":"Singh","suffix":""},{"id":455834882,"identity":"222a2c72-8883-4a86-a0ab-194632c7d6aa","order_by":2,"name":"Ankit Kumar Titoriya","email":"","orcid":"","institution":"National Institute of Technology Patna","correspondingAuthor":false,"prefix":"","firstName":"Ankit","middleName":"Kumar","lastName":"Titoriya","suffix":""}],"badges":[],"createdAt":"2025-05-01 07:01:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6569821/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6569821/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00500-025-10884-6","type":"published","date":"2025-09-15T15:56:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91889869,"identity":"911234c1-edcf-4ad5-8d5f-fd3f092fd9ed","added_by":"auto","created_at":"2025-09-22 16:02:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1102245,"visible":true,"origin":"","legend":"","description":"","filename":"FewShotLearningforPlantDiseaseDetectionusingDeepBDC.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6569821/v1_covered_7fafc924-5cc4-49d6-916b-96506edbc65f.pdf"}],"financialInterests":"","formattedTitle":"Few-Shot Learning for Plant Disease Detection using DeepBDC","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Few-shot Learning, Plant Disease, Computer Vision, Image Classification","lastPublishedDoi":"10.21203/rs.3.rs-6569821/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6569821/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Plant disease detection in low-data scenarios is a major challenge for computer vision applications in agriculture. Traditional deep learning needs big, labeled datasets, but these are often not available for rare or new plant diseases. To address this issue, this paper presents a few-shot learning (FSL) method for classifying plant diseases using an improved Deep Brownian Distance Covariance (DeepBDC) framework. The model uses a ResNet-12 as a backbone network which uses a Convolutional Block Attention Module (CBAM) to help the network focus on important spatial and channel-wise features. In network, dropout regularization is also applied to reduce overfitting, which is common in few-shot tasks. The DeepBDC module is improved by using L2 normalization and temperature scaling, which makes the feature representations more stable and better for classification. The model uses cosine similarity for 1-shot learning and Gaussian kernel similarity for 5-shot and 10-shot settings. The method is evaluated on two public plant disease datasets, PlantVillage and CCMT, using standard 5-way classification with 15 queries per class and 2000 episodes for each configuration. On the PlantVillage dataset, the model gets 46.53% accuracy for 1-shot, 65.86% for 5-shot, and 69.67% for 10-shot tasks. On the CCMT dataset, it reaches 40.19%, 49.19%, and 53.67% accuracy for the same settings. 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