Novel Swarm Intelligence Optimized Quantum CNN Framework for Accurate Multi Class Soil Image Classification

Novel Swarm Intelligence Optimized Quantum CNN Framework for Accurate Multi Class Soil Image Classification | 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 Novel Swarm Intelligence Optimized Quantum CNN Framework for Accurate Multi Class Soil Image Classification Paparao Nalajala, S Vinoth This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8997049/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Soil image classification aids precision agriculture, but current methods often fail in multi-class identification due to poor features and parameter tuning. This research presents a framework integrating Deep Learning (DL) and swarm intelligence optimization to enhance classification performance. A dataset of 1,378 distinct soil images from Kaggle was evaluated, featuring various soil categories including alluvial, black, cinder, clay, laterite, peat, red, and yellow soils. The proposed Local Gabor Rank Pattern with Quantum Convolutional Neural Network (LGRP + Q-CNN) extracts micro-texture features via LGRP and global semantic patterns via Q-CNN, while quantum computing improves feature encoding, accelerates optimization, and enhances classification robustness. In preprocessing, images were resized, noise removed with a Gaussian filter, and contrast enhanced. The Ring Toss Game-Based Algorithm (RTBA) optimized hyperparameters including learning rate, batch size, number of filters, and kernel size, aiming to maximize validation accuracy as the fitness function. The optimized LGRP + Q-CNN model, trained in Python 3.10, achieves superior performance over traditional DL and feature-based models with 98.5% accuracy, 96.8% precision, 96.65% recall, and 96.72% F1-score, supported by statistical analysis using Friedman and paired t-tests.The findings demonstrate that combining DL with swarm optimization offers a robust and accurate method for multi-class soil image classification. Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Soil Image Classification Precision Agriculture Ring Toss Game–based Algorithm (RTBA) Quantum CNN Friedman Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 28 Apr, 2026 Reviews received at journal 28 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers invited by journal 01 Apr, 2026 Editor invited by journal 05 Mar, 2026 Editor assigned by journal 02 Mar, 2026 Submission checks completed at journal 02 Mar, 2026 First submitted to journal 28 Feb, 2026 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-8997049","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":615879081,"identity":"0745414e-5d5c-4e8f-9c0b-1347c85f95b8","order_by":0,"name":"Paparao Nalajala","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYFADZv6PD4AUDx/xWtgbjA1AWtiI18JzwEwCRBPUYs7Ae/BzQc09eX6JhLTKrzl2MmwMzA8f3cCjxbKBL1l6xrFiw5kzEo7dlt2WDHQYm7FxDh4tBgd4DKR52BIYN9xIbLstuY0ZqIWHTZqAFuPfPP8S7DfcSGYrltxWT5QWM2netoTEDWeOsTF+3HaYCC2H+dKsZ/YlJM9s72GWZtx2nIeNmZBfjvcevl3wLcG2n5mH8ePPbdX2/OzNDx/j08LAzMPAjMQGkfiUgwGSFsYfBFWPglEwCkbBSAQAYa5B3TS0cnoAAAAASUVORK5CYII=","orcid":"","institution":"Manakula Vinayagar Institute of Technology (Affiliated with Pondicherry University)","correspondingAuthor":true,"prefix":"","firstName":"Paparao","middleName":"","lastName":"Nalajala","suffix":""},{"id":615879082,"identity":"1e85fc4f-fc26-4bee-9f9e-f0981030487d","order_by":1,"name":"S Vinoth","email":"","orcid":"","institution":"Manakula Vinayagar Institute of Technology (Affiliated with Pondicherry University)","correspondingAuthor":false,"prefix":"","firstName":"S","middleName":"","lastName":"Vinoth","suffix":""}],"badges":[],"createdAt":"2026-02-28 17:09:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8997049/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8997049/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106243762,"identity":"5993e528-59b4-484e-a75a-e742b7e53010","added_by":"auto","created_at":"2026-04-06 15:35:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":664272,"visible":true,"origin":"","legend":"","description":"","filename":"MultiClassSoilImageClassificationRevisionPaperv223.02.26111.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8997049/v1_covered_95fc1cbb-4e70-4876-8054-2d19da5f37ae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Novel Swarm Intelligence Optimized Quantum CNN Framework for Accurate Multi Class Soil Image Classification","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Soil Image Classification, Precision Agriculture, Ring Toss Game–based Algorithm (RTBA), Quantum CNN, Friedman","lastPublishedDoi":"10.21203/rs.3.rs-8997049/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8997049/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoil image classification aids precision agriculture, but current methods often fail in multi-class identification due to poor features and parameter tuning. 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