ADAM-DETR: An Intelligent Rice Disease Detection Method Based on Adaptive Multi-scale Feature Fusion

ADAM-DETR: An Intelligent Rice Disease Detection Method Based on Adaptive Multi-scale Feature Fusion | 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 ADAM-DETR: An Intelligent Rice Disease Detection Method Based on Adaptive Multi-scale Feature Fusion Hanyu Song, Xinyue Huang, Ziqiang Wang, Jianwei Hu, Huasheng Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6730485/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Rice diseases pose a severe threat to global food security, while traditional detection methods suffer from low efficiency and dependence on manual expertise. To address the challenges of insufficient feature extraction and poor multi-scale disease adaptability in existing deep learning approaches under complex field environments, this study proposes ADAM-DETR, a rice disease detection algorithm based on improved RT-DETR. We constructed the RiDDET-5 dataset containing 9,303 images covering five major disease categories. The algorithm innovatively designs three core modules: the AdaptiveVision Network (AVN) backbone for enhanced feature extraction, the Dual-Domain Enhanced Transformer (DDET) module for spatiotemporal-frequency domain collaboration, and the Adaptive Multi-scale Feature Model (AMFM) for improved feature fusion. Experimental results demonstrate that ADAM-DETR achieves 94.76% mAP@50 on the RiDDET-5 dataset, representing a 3.25% improvement over the baseline, and 83.32% mAP@50 on the public Kamatis dataset with a 2.19% enhancement, validating its cross-domain generalization capability. The algorithm requires only 42.8G FLOPs with 14.3M parameters, achieving an optimal balance between accuracy and efficiency, providing an effective technical solution for disease monitoring in smart agriculture rice disease detection deep learning multi-scale feature fusion attention mechanism precision agriculture smart agriculture Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Jul, 2025 Reviews received at journal 24 Jun, 2025 Reviews received at journal 21 Jun, 2025 Reviewers agreed at journal 18 Jun, 2025 Reviews received at journal 17 Jun, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviews received at journal 16 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviewers invited by journal 02 Jun, 2025 Editor assigned by journal 26 May, 2025 Submission checks completed at journal 26 May, 2025 First submitted to journal 23 May, 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-6730485","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466148832,"identity":"e0a02486-a3b8-4bf7-a7f1-67e1f697a93b","order_by":0,"name":"Hanyu Song","email":"","orcid":"","institution":"Jiangxi University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hanyu","middleName":"","lastName":"Song","suffix":""},{"id":466148833,"identity":"f59d662b-b6d8-47aa-9427-55573c623f53","order_by":1,"name":"Xinyue Huang","email":"","orcid":"","institution":"Jiangxi University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xinyue","middleName":"","lastName":"Huang","suffix":""},{"id":466148834,"identity":"996288b4-3a1e-4f89-bdef-39bddeee510b","order_by":2,"name":"Ziqiang Wang","email":"","orcid":"","institution":"Jiangxi University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ziqiang","middleName":"","lastName":"Wang","suffix":""},{"id":466148835,"identity":"6e117f94-b317-4eec-ad7a-dd0aed989184","order_by":3,"name":"Jianwei Hu","email":"","orcid":"","institution":"Jiangxi University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jianwei","middleName":"","lastName":"Hu","suffix":""},{"id":466148836,"identity":"6f43bda4-37f1-4e13-92ed-a618ad9ed614","order_by":4,"name":"Huasheng Zhang","email":"","orcid":"","institution":"Jiangxi University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Huasheng","middleName":"","lastName":"Zhang","suffix":""},{"id":466148837,"identity":"ea23cf24-2a9f-4db3-8621-f409164679e6","order_by":5,"name":"Hui Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYJACZgYGCTnGBggHRhPWYgxWeoAELQyJYJVEaTE4fvbw68I2i/TmGTnGnz8w2MhuOMD87AFeLWfy0qxntknkNvacMZM4wJBmvOEAm7kBXi0HcsyMeUFa2nvMgA47nLjhAA+bBF4t59+AtaQzNvMYfzjA8J8ILTdyjB8DtSQwtvcYAB12gLAWyRtvzJh5zkkYNvYcK5M4Y5BsPPMwmxleLXzngQHFU1YnbzgjefOHigo72b7jzc/walE4wABxhmED2J0MkGjCB+QbGJg/gBkEFI6CUTAKRsEIBgDWyksFnBPpfwAAAABJRU5ErkJggg==","orcid":"","institution":"Jiangxi University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Hui","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-05-23 07:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6730485/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6730485/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83974510,"identity":"cf902eb2-7f96-41d9-a571-227fe536b3d8","added_by":"auto","created_at":"2025-06-05 08:42:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1462912,"visible":true,"origin":"","legend":"","description":"","filename":"ADAMDETRAnIntelligentRiceDiseaseDetectionMethodBasedonAdaptiveMultiscaleFeatureFusio.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6730485/v1_covered_31f9ace1-f8ad-4b37-b28f-b23bb70edc1f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ADAM-DETR: An Intelligent Rice Disease Detection Method Based on Adaptive Multi-scale Feature Fusion","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":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-methods","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plme","sideBox":"Learn more about [Plant Methods](http://plantmethods.biomedcentral.com/)","snPcode":"13007","submissionUrl":"https://submission.nature.com/new-submission/13007/3","title":"Plant Methods","twitterHandle":"@PlantMethods","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"rice disease detection, deep learning, multi-scale feature fusion, attention mechanism, precision agriculture, smart agriculture","lastPublishedDoi":"10.21203/rs.3.rs-6730485/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6730485/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRice diseases pose a severe threat to global food security, while traditional detection methods suffer from low efficiency and dependence on manual expertise. To address the challenges of insufficient feature extraction and poor multi-scale disease adaptability in existing deep learning approaches under complex field environments, this study proposes ADAM-DETR, a rice disease detection algorithm based on improved RT-DETR. We constructed the RiDDET-5 dataset containing 9,303 images covering five major disease categories. The algorithm innovatively designs three core modules: the AdaptiveVision Network (AVN) backbone for enhanced feature extraction, the Dual-Domain Enhanced Transformer (DDET) module for spatiotemporal-frequency domain collaboration, and the Adaptive Multi-scale Feature Model (AMFM) for improved feature fusion. Experimental results demonstrate that ADAM-DETR achieves 94.76% mAP@50 on the RiDDET-5 dataset, representing a 3.25% improvement over the baseline, and 83.32% mAP@50 on the public Kamatis dataset with a 2.19% enhancement, validating its cross-domain generalization capability. The algorithm requires only 42.8G FLOPs with 14.3M parameters, achieving an optimal balance between accuracy and efficiency, providing an effective technical solution for disease monitoring in smart agriculture\u003c/p\u003e","manuscriptTitle":"ADAM-DETR: An Intelligent Rice Disease Detection Method Based on Adaptive Multi-scale Feature Fusion","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-05 08:34:41","doi":"10.21203/rs.3.rs-6730485/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-05T02:09:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-25T03:43:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-21T05:31:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295931078365869511911182610665613076241","date":"2025-06-18T17:13:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-18T02:39:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85230662196360296726222501968082572892","date":"2025-06-18T00:05:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-17T03:50:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220294248644282118975414214811774875618","date":"2025-06-17T01:03:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114527877702428765146229146143844873870","date":"2025-06-16T18:55:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-02T14:17:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-26T21:43:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-26T14:40:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant Methods","date":"2025-05-23T07:29:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-methods","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plme","sideBox":"Learn more about [Plant Methods](http://plantmethods.biomedcentral.com/)","snPcode":"13007","submissionUrl":"https://submission.nature.com/new-submission/13007/3","title":"Plant Methods","twitterHandle":"@PlantMethods","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2b2343c5-963a-47fe-a4f8-2c81767f417f","owner":[],"postedDate":"June 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-03T00:23:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-05 08:34:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6730485","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6730485","identity":"rs-6730485","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}