Enhanced Plant Leaf Disease Detection Using Modified Logistic Regression for Sustainable Agriculture Practices

Enhanced Plant Leaf Disease Detection Using Modified Logistic Regression for Sustainable Agriculture Practices | 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 Enhanced Plant Leaf Disease Detection Using Modified Logistic Regression for Sustainable Agriculture Practices S. Kiran, N. Subramanyan, M. Bhavsingh, K Samunnisa, B. Swathika, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6241975/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 Pepper, Hibiscus, and Basil are medicinal plants with a rich history in traditional medicine and health benefits. They are essential in culinary and medicinal applications, contributing to natural health solutions. Disease detection is crucial to protect their agricultural, economic, and medicinal value. Early detection minimizes crop losses, maintains plant health, and ensures plant availability for traditional medicine and culinary uses. This promotes sustainable and eco-friendly agricultural practices. Traditional logistic regression for plant leaf disease detection struggles with imbalanced data and a fixed linear decision boundary, making it less effective in capturing complex disease patterns. The modified logistic regression model with the One Half Constant improves performance metrics and handling intricate features of leaf images by addressing class imbalance more effectively. It adjusts the decision boundary to handle imbalanced datasets, enhancing classification accuracy for minority classes while maintaining simplicity and interpretability. This study uses a dataset collected from Kaggle and surrounding of Kadapa district AP, India. For the evaluation of the proposed model in disease detection, the traditional logistic regression and other machine learning algorithms were used, and the corresponding key metrics of accuracy, precision, recall, false positive rate (FPR) and F-Measure were assessed. A comparison with existing methods show overwhelming improvement of 32.94% in accuracy, 17.64% in precision, 33.6% in recall, 86.91% in FPR improvement, 34.6% in F-Measure. The proposed approach seeks to improve overall diagnostic accuracy, thereby providing a reliable tool for early detection and treatment planning in clinical sectors. Accuracy Dull intensity images Disease detection Plant leaf diseases Logistic Regression FPR 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-6241975","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436523240,"identity":"cb3ed0fc-10c8-4a32-81d3-d5dc6af92cd1","order_by":0,"name":"S. Kiran","email":"","orcid":"","institution":"Y.S.R.E.C of YVU","correspondingAuthor":false,"prefix":"","firstName":"S.","middleName":"","lastName":"Kiran","suffix":""},{"id":436523241,"identity":"4d189709-0535-4300-b505-86b43d42a4e2","order_by":1,"name":"N. Subramanyan","email":"","orcid":"","institution":"S.R.N.M College","correspondingAuthor":false,"prefix":"","firstName":"N.","middleName":"","lastName":"Subramanyan","suffix":""},{"id":436523243,"identity":"941744e4-9b51-46e7-9ec3-97ccdc0f43ed","order_by":2,"name":"M. Bhavsingh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACAwkwCUTMDIwPgEwePhK0MDMbgLSwEacFrIuZDcwmqMVcuvmZ5I+CO3bb2fmPVX7NsZNhY2B++OgGHi2Wc46ZSfMYPEve2czMdlt2WzLQYWzGxjn4HHYjwdiYweBwssFhoBbJbcxALTxs0vi1pH82/AHVUiy5rZ4YLTmGD3gMDtuBtDB+3HaYsBbLGTmFj4FaEoBajKUZtx3nYWMm4BdzifQNB3/8OWxvcP7gw48/t1Xb87M3P3yMTwsMJDYACWYeEJOZCOUgYA8iGH8QqXoUjIJRMApGFgAAqJJD69zgUV4AAAAASUVORK5CYII=","orcid":"","institution":"Ashoka Womens Engineering College","correspondingAuthor":true,"prefix":"","firstName":"M.","middleName":"","lastName":"Bhavsingh","suffix":""},{"id":436523245,"identity":"89b695ba-f627-41c5-b7bc-9642a89745f5","order_by":3,"name":"K Samunnisa","email":"","orcid":"","institution":"Ashoka Womens Engineering College","correspondingAuthor":false,"prefix":"","firstName":"K","middleName":"","lastName":"Samunnisa","suffix":""},{"id":436523247,"identity":"9bf240ec-d1b4-48c2-b679-715e84049a09","order_by":4,"name":"B. Swathika","email":"","orcid":"","institution":"Y.S.R.E.C of YVU","correspondingAuthor":false,"prefix":"","firstName":"B.","middleName":"","lastName":"Swathika","suffix":""},{"id":436523249,"identity":"c1e40176-c18e-43dc-b9ac-9abd5abf80b8","order_by":5,"name":"Lavanya A","email":"","orcid":"","institution":"Universitat Politécnica De Valencia","correspondingAuthor":false,"prefix":"","firstName":"Lavanya","middleName":"","lastName":"A","suffix":""},{"id":436523250,"identity":"b64b73fd-e944-40a7-827f-f9c195f3c79f","order_by":6,"name":"Jaime Lloret","email":"","orcid":"","institution":"Universitat Politécnica De Valencia","correspondingAuthor":false,"prefix":"","firstName":"Jaime","middleName":"","lastName":"Lloret","suffix":""}],"badges":[],"createdAt":"2025-03-17 07:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6241975/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6241975/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92392002,"identity":"847f26f7-cec9-40cb-9ac3-5258e0505d36","added_by":"auto","created_at":"2025-09-29 08:48:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1149627,"visible":true,"origin":"","legend":"","description":"","filename":"FinalPaperDiseaseDetection.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6241975/v1_covered_39602405-8630-43a8-b620-e631dfd0cae1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhanced Plant Leaf Disease Detection Using Modified Logistic Regression for Sustainable Agriculture Practices","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":"Accuracy, Dull intensity images, Disease detection, Plant leaf diseases, Logistic Regression, FPR","lastPublishedDoi":"10.21203/rs.3.rs-6241975/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6241975/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePepper, Hibiscus, and Basil are medicinal plants with a rich history in traditional medicine and health benefits. They are essential in culinary and medicinal applications, contributing to natural health solutions. Disease detection is crucial to protect their agricultural, economic, and medicinal value. Early detection minimizes crop losses, maintains plant health, and ensures plant availability for traditional medicine and culinary uses. This promotes sustainable and eco-friendly agricultural practices. Traditional logistic regression for plant leaf disease detection struggles with imbalanced data and a fixed linear decision boundary, making it less effective in capturing complex disease patterns. The modified logistic regression model with the One Half Constant improves performance metrics and handling intricate features of leaf images by addressing class imbalance more effectively. It adjusts the decision boundary to handle imbalanced datasets, enhancing classification accuracy for minority classes while maintaining simplicity and interpretability. This study uses a dataset collected from Kaggle and surrounding of Kadapa district AP, India. For the evaluation of the proposed model in disease detection, the traditional logistic regression and other machine learning algorithms were used, and the corresponding key metrics of accuracy, precision, recall, false positive rate (FPR) and F-Measure were assessed. A comparison with existing methods show overwhelming improvement of 32.94% in accuracy, 17.64% in precision, 33.6% in recall, 86.91% in FPR improvement, 34.6% in F-Measure. The proposed approach seeks to improve overall diagnostic accuracy, thereby providing a reliable tool for early detection and treatment planning in clinical sectors.\u003c/p\u003e","manuscriptTitle":"Enhanced Plant Leaf Disease Detection Using Modified Logistic Regression for Sustainable Agriculture Practices","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-01 06:06:09","doi":"10.21203/rs.3.rs-6241975/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"9fd98ee3-9a88-4917-8371-b4fdb7f02729","owner":[],"postedDate":"April 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T08:41:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-01 06:06:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6241975","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6241975","identity":"rs-6241975","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}