Unmanned Aerial System-Driven Data and Advanced Deep Learning Strategies for Elevating Weed Management in Agricuture

Unmanned Aerial System-Driven Data and Advanced Deep Learning Strategies for Elevating Weed Management in Agricuture | 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 Unmanned Aerial System-Driven Data and Advanced Deep Learning Strategies for Elevating Weed Management in Agricuture Dhiraj Srivastava, Vijay Singh, Song Li, Kevin Kochersberger This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3865180/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 United States, Palmer amaranth is a troublesome weed that competes with major crops, such as, soybean, and may lead to significant crop yield reduction if not managed properly. Integrated weed management practices using eco-friendly artificial intelligence based weeding robots and spot sprayers have been gaining popularity in agriculture. All of these robotic systems and weed recognition approaches, utilize a weed image database and a set of machine learning algorithms. This study investigates the performance of classification and object detection algorithms using unmanned aerial systems based red, green, and blue imageries acquired at different growth stages of soybean and Palmer amaranth. Vision transformer and EfficientnetB0 achieved test accuracies of 97.69% and 93.26% respectively, but Vision Transformer was 2.5-times slower than EfficientNetB0 on inference speed. Based on the tradeoff between speed and accuracy, experimentally it was observed that YOLOv6s is a suitable object detection model for real-time deployment with 82.6% mean average precision. Additionally, we present a self-supervised contrastive learning approach to label Palmer amaranth and soybean classes, achieving 98.5% test accuracy, demonstrating the potential for cost-efficient data acquisition and labeling to advance precision agriculture research. Biological sciences/Plant sciences Physical sciences/Engineering 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-3865180","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":267331546,"identity":"22beed87-24ee-4590-b971-4caa738b8bd3","order_by":0,"name":"Dhiraj Srivastava","email":"","orcid":"","institution":"Virginia Tech","correspondingAuthor":false,"prefix":"","firstName":"Dhiraj","middleName":"","lastName":"Srivastava","suffix":""},{"id":267331547,"identity":"64758e2a-f4f7-4424-be75-47fd93768969","order_by":1,"name":"Vijay Singh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYJCCA0AkB2bxkKLFmIGNFC0gXYkNRGsxOMD+8HDBrzvpG+43MD5420aUFh6DwzP7nuVuOMbAbDiXGC2SDTwMh3l7DufObGNgk+YlTgv7A5CWdMk2BvbfRGnhB7rtMM+Pwwn8bAxszMRpYQb6hbfhsGE/W2Kz5JxzRGhhY29//Jnnz2F5NubDBz+8KSNCCwMzEDOC3cPYQIx6GPhDiuJRMApGwSgYcQAAxuQ05DUYLzQAAAAASUVORK5CYII=","orcid":"","institution":"Virginia Tech","correspondingAuthor":true,"prefix":"","firstName":"Vijay","middleName":"","lastName":"Singh","suffix":""},{"id":267331548,"identity":"eb22f591-b1a0-4c6a-a1bb-86d70bb1813a","order_by":2,"name":"Song Li","email":"","orcid":"","institution":"Virginia Tech","correspondingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"Li","suffix":""},{"id":267331549,"identity":"2407ffb2-1461-4098-98c1-e146ed216359","order_by":3,"name":"Kevin Kochersberger","email":"","orcid":"","institution":"Virginia Tech","correspondingAuthor":false,"prefix":"","firstName":"Kevin","middleName":"","lastName":"Kochersberger","suffix":""}],"badges":[],"createdAt":"2024-01-15 02:29:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3865180/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3865180/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61930676,"identity":"31868f5f-6fce-4286-8c1d-ad031861acec","added_by":"auto","created_at":"2024-08-07 08:09:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1103442,"visible":true,"origin":"","legend":"","description":"","filename":"UASandMLScientificReportsManuscriptSrivastavaetal..pdf","url":"https://assets-eu.researchsquare.com/files/rs-3865180/v1_covered_477bc952-a40a-4285-bb01-9439603bba75.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unmanned Aerial System-Driven Data and Advanced Deep Learning Strategies for Elevating Weed Management in Agricuture","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-3865180/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3865180/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the United States, Palmer amaranth is a troublesome weed that competes with major crops, such as, soybean, and may lead to significant crop yield reduction if not managed properly. Integrated weed management practices using eco-friendly artificial intelligence based weeding robots and spot sprayers have been gaining popularity in agriculture. All of these robotic systems and weed recognition approaches, utilize a weed image database and a set of machine learning algorithms. This study investigates the performance of classification and object detection algorithms using unmanned aerial systems based red, green, and blue imageries acquired at different growth stages of soybean and Palmer amaranth. Vision transformer and EfficientnetB0 achieved test accuracies of 97.69% and 93.26% respectively, but Vision Transformer was 2.5-times slower than EfficientNetB0 on inference speed. Based on the tradeoff between speed and accuracy, experimentally it was observed that YOLOv6s is a suitable object detection model for real-time deployment with 82.6% mean average precision. Additionally, we present a self-supervised contrastive learning approach to label Palmer amaranth and soybean classes, achieving 98.5% test accuracy, demonstrating the potential for cost-efficient data acquisition and labeling to advance precision agriculture research. 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