Automated Wheat Disease Detection Using A ROS-Based Autonomous Guided UAV

Automated Wheat Disease Detection Using A ROS-Based Autonomous Guided UAV | 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 Automated Wheat Disease Detection Using A ROS-Based Autonomous Guided UAV Behzad Safarijalal, Yousef Alborzi, Esmaeil Najafi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1251771/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 With the increase in world population, food resources have to be modified to be more productive, resistive, and reliable. Wheat is one of the most important food resources in the world, mainly because of the variety of wheat-based products. Wheat crops are threatened by three main types of diseases which cause large amounts of annual damage in crop yield. These diseases can be eliminated by using pesticides at the right time. While the task of manually spraying pesticides is burdensome and expensive, agricultural robotics can aid farmers by increasing the speed and decreasing the amount of chemicals. In this work, a smart autonomous system has been implemented on an unmanned aerial vehicle to automate the task of monitoring wheat fields. First, an image-based deep learning approach is used to detect and classify disease-infected wheat plants. To find the most optimal method, different approaches have been studied. Because of the lack of a public wheat-disease dataset, a custom dataset has been created and labeled. Second, an efficient mapping and navigation system is presented using a simulation in the robot operating system and Gazebo environments. A 2D simultaneous localization and mapping algorithm is used for mapping the workspace autonomously with the help of a frontier-based exploration method. Full Text Additional Declarations No competing interests reported. Supplementary Files alborzi2020.pdf 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-1251771","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":88189304,"identity":"f7cf1d36-948e-40a5-9e7c-ac2e6b30724e","order_by":0,"name":"Behzad Safarijalal","email":"","orcid":"","institution":"K.N.Toosi University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Behzad","middleName":"","lastName":"Safarijalal","suffix":""},{"id":88189305,"identity":"f97fef49-0fd6-4750-b1fd-35e7a56c0f37","order_by":1,"name":"Yousef Alborzi","email":"","orcid":"","institution":"University of Manitoba","correspondingAuthor":false,"prefix":"","firstName":"Yousef","middleName":"","lastName":"Alborzi","suffix":""},{"id":88189306,"identity":"b7fa5a80-c7b8-437e-a8e4-6238ea155463","order_by":2,"name":"Esmaeil Najafi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIie3OvQrCMBSG4U8Kdol0rQj2Fk4Q/AHxWiIFJwdHwcGC4FR0VfAuBOeAYJcUV8eKq0NHBwdToWvbUTAvJBDIkxzAZPrNBOYEOCw/W1WI0qQZohZUJYDSi1ROyurZ8p7KGdodFUcPhpEHu5EUkkEofFcSOt14M14x+DywbCokJIVopYTx6cq4JpaAVS8ejK6J/9K/LI/bL1lWIDcxyQYT1Agzcq5Ckklfkst3SvH9gSK+Lh9s6t/ke+g54ZTS53zhOc6lmABM6M3NXwBK/siyZfkdk8lk+u8+elM9XVIhl8gAAAAASUVORK5CYII=","orcid":"","institution":"K.N.Toosi University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Esmaeil","middleName":"","lastName":"Najafi","suffix":""}],"badges":[],"createdAt":"2022-01-11 23:59:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1251771/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1251771/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":18931483,"identity":"cd976201-6279-438c-aeab-d35a42dc60d6","added_by":"auto","created_at":"2022-03-07 15:54:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2570425,"visible":true,"origin":"","legend":"","description":"","filename":"SafarijalalScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1251771/v1_covered.pdf"},{"id":18931479,"identity":"605dba2f-af5c-4658-a91e-27efd66b467a","added_by":"auto","created_at":"2022-03-07 15:54:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3020702,"visible":true,"origin":"","legend":"","description":"","filename":"alborzi2020.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1251771/v1/81f247280760451a706dc4ea.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAutomated Wheat Disease Detection Using A ROS-Based Autonomous Guided UAV\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1251771/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"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":false,"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-1251771/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1251771/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"With the increase in world population, food resources have to be modified to be more productive, resistive, and reliable. 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