A Novel Deep Learning Model with Optimized Sparrow Search for Efficient Plant Stress Identification

A Novel Deep Learning Model with Optimized Sparrow Search for Efficient Plant Stress Identification | 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 Article A Novel Deep Learning Model with Optimized Sparrow Search for Efficient Plant Stress Identification Manjit Kaur, Dr. Upinder kaur, Dr. Osamah Ibrahim Khalaf, Dr. Sameer Algburi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4129365/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 In the current economic climate, one of the biggest problems facing the agriculture industry is the accurate and real-timeidentification of biotic and abiotic plant stressors that impact crop yield. The upshot is that illness detection models can’t handlethe pressures of practical, real-time use. By stressing the significance of adapting deep learning models to the intricacies ofdisease-specific datasets, our research presented a fresh approach to solving this critical problem. We provide a novel hybridsparrow search method that can optimise deep convolutional neural network hyperparameters (CNNs). Consequently, themethod for detecting plant stress has become more accurate and precise. An unprecedented leap in model performance isachieved by DeepHSSA, which merges the exploratory dynamics of Sparrow Search with the adaptive procedures of Searchand Rescue Optimization. The robustness of DeepHSSA was evaluated in response to dynamic, real-time field circumstancesacross diverse agricultural landscapes in India using a comprehensive dataset consisting of seven abiotic stress indicators andseven biotic stress indicators. In comparison to more conventional models, DeepHSSA achieved an impressive accuracy rateof 99.94 percent. Biological sciences/Computational biology and bioinformatics Biological sciences/Computational biology and bioinformatics/Computational models Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing Physical sciences/Mathematics and computing/Computational science 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. 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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-4129365","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":289673422,"identity":"1d1d66cd-d037-41f1-802e-5451ca865eae","order_by":0,"name":"Manjit Kaur","email":"","orcid":"","institution":"Akal University","correspondingAuthor":false,"prefix":"","firstName":"Manjit","middleName":"","lastName":"Kaur","suffix":""},{"id":289673423,"identity":"34eb812f-56c4-47d3-a92c-bfa905c0e468","order_by":1,"name":"Dr. Upinder kaur","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYFACxgYGHjCD+QBc7AB2pRha2BIYGBKI0gIEEC08BnAteIHB7ea2B28q7jHw95/5+Jn3Rx0Df/sBxsMF+LTcOdhuOOdMMYPEjdzN0jwJhxkkziQwHJ6BT8uNxDZp3rYEBgMJ3g1ALUBf3GBgOMxDUMs/oBb+M49/8yTUMcgTp6UBqIUhhw1oCzNQhIAWyTsH2yTnHEsA+iXNzHJO2mEewzOJDXi18N1ufybxpiYBGGKHH994Y1MnJ3f88OHP+LQwSECo+gYonwccuXiBBH7pUTAKRsEoGAUMDADVvEpE/ZH3hAAAAABJRU5ErkJggg==","orcid":"","institution":"Akal University","correspondingAuthor":true,"prefix":"Dr.","firstName":"Upinder","middleName":"","lastName":"kaur","suffix":""},{"id":289673424,"identity":"0157325e-d3b7-47b1-a7fd-42336843166e","order_by":2,"name":"Dr. Osamah Ibrahim Khalaf","email":"","orcid":"","institution":"Nahrain University","correspondingAuthor":false,"prefix":"Dr.","firstName":"Osamah","middleName":"Ibrahim","lastName":"Khalaf","suffix":""},{"id":289673425,"identity":"ab377b8b-f416-4414-85f8-7329dbdf9c57","order_by":3,"name":"Dr. Sameer Algburi","email":"","orcid":"","institution":"AL Kitab University, Kirkul","correspondingAuthor":false,"prefix":"Dr.","firstName":"Sameer","middleName":"","lastName":"Algburi","suffix":""},{"id":289673426,"identity":"f90b7cb2-7fb5-4424-9de2-a8882ce8cff5","order_by":4,"name":"Habib Hamam","email":"","orcid":"","institution":"Université de Moncton","correspondingAuthor":false,"prefix":"","firstName":"Habib","middleName":"","lastName":"Hamam","suffix":""}],"badges":[],"createdAt":"2024-03-19 10:12:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4129365/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4129365/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89453005,"identity":"98bc3625-0756-47c1-86c2-fec051af0fe9","added_by":"auto","created_at":"2025-08-20 06:38:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1895149,"visible":true,"origin":"","legend":"","description":"","filename":"SparrowPaper9april24.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4129365/v1_covered_52fae9b7-c803-4b72-8ad3-c4de3c6ed27a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Deep Learning Model with Optimized Sparrow Search for Efficient Plant Stress Identification","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":"","lastPublishedDoi":"10.21203/rs.3.rs-4129365/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4129365/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the current economic climate, one of the biggest problems facing the agriculture industry is the accurate and real-timeidentification of biotic and abiotic plant stressors that impact crop yield. The upshot is that illness detection models can’t handlethe pressures of practical, real-time use. By stressing the significance of adapting deep learning models to the intricacies ofdisease-specific datasets, our research presented a fresh approach to solving this critical problem. We provide a novel hybridsparrow search method that can optimise deep convolutional neural network hyperparameters (CNNs). Consequently, themethod for detecting plant stress has become more accurate and precise. An unprecedented leap in model performance isachieved by DeepHSSA, which merges the exploratory dynamics of Sparrow Search with the adaptive procedures of Searchand Rescue Optimization. The robustness of DeepHSSA was evaluated in response to dynamic, real-time field circumstancesacross diverse agricultural landscapes in India using a comprehensive dataset consisting of seven abiotic stress indicators andseven biotic stress indicators. 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