Performance Assessment of the Deep Learning Weed Reorganization System

Performance Assessment of the Deep Learning Weed Reorganization System | 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 Performance Assessment of the Deep Learning Weed Reorganization System Abd Abrahim Mosslah, Reyadh Hazim Mahdi, Hassan Kassim Albahadily This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7223170/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 Numerous approaches based on machine learning have emerged in recent years to enhance crop protection efficiency. One example is the utilisation of deep neural networks to differentiate between various weed types in actual event scenarios. Nevertheless, these methods often need substantial input from experts who work iteratively to design robust deep learning systems. To simplify such processes and conserve resources, researchers have explored a fresh method known as automated deep learning. The our technology's recognition of weeds through the use of machine learning was evaluated using plant seedlings and weed collection from plants as a dataset to address the issue of weed recognition. The study compared various configurations, including plant segmentation, using a collection of classifiers in place of Softmax, and training on datasets that contain noise. The findings indicated ensuring performance, with F1-scores of 93.1% and 90.2% based on the dataset utilised. These results align with AutoML-linked studies while falling short of manually fine-tuned deep-learning-based systems created by human specialists. To conclude, exploring the potential of combining manual expert work and automated deep learning could be a promising direction for enhancing efficiency in plant defence. Artificial Intelligence and Machine Learning AutoML SVD Deep Learning and weeds Full Text Additional Declarations The authors declare no competing interests. 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-7223170","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":491353322,"identity":"229118dd-1989-4416-90ad-db9222229b62","order_by":0,"name":"Abd Abrahim Mosslah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYBACNgbmBgbGBgnGBvYGEP8AWFQCjHBqYYRq4TlApBYGiBaQrgQULbgBH//BBoafOyxk+yXfGD74ueOOvHkD88HbPAwWsg24HCaR2MDYe0bCeObsHGPD3jPPDOccYEu25mGQMMatBegq3jaJxA23c8wkeNsOM85g4DGTBmpJxKkF6DDGv0At+2+eMZP823bYfgYD/zf8WhgSG5jBtkgADQfakgi0hQ2/FqDUYVmgX2acSSs2lm17ljyDmc3Yco4Bbr/I9x8++PDtjjrZ/vbDGx++bbtjO4O9+eGNNxV1OEMMBA6gcplBhAEoskgFZGgZBaNgFIyCYQoALChU2mKKhrMAAAAASUVORK5CYII=","orcid":"","institution":"University of Anbar","correspondingAuthor":true,"prefix":"","firstName":"Abd","middleName":"Abrahim","lastName":"Mosslah","suffix":""},{"id":491353323,"identity":"d3f4579a-fd70-456d-bf77-40ca03e96a76","order_by":1,"name":"Reyadh Hazim Mahdi","email":"","orcid":"","institution":"University of Mustansiriyah,","correspondingAuthor":false,"prefix":"","firstName":"Reyadh","middleName":"Hazim","lastName":"Mahdi","suffix":""},{"id":491353628,"identity":"daca8bff-740a-477a-bdad-bec999b883fe","order_by":2,"name":"Hassan Kassim Albahadily","email":"","orcid":"","institution":"University of Mustansiriyah,","correspondingAuthor":false,"prefix":"","firstName":"Hassan","middleName":"Kassim","lastName":"Albahadily","suffix":""}],"badges":[],"createdAt":"2025-07-26 20:45:46","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7223170/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7223170/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87797385,"identity":"a55cbba8-4be7-477e-95f6-7de507524f23","added_by":"auto","created_at":"2025-07-29 07:13:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":492004,"visible":true,"origin":"","legend":"","description":"","filename":"paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7223170/v1_covered_7e9202b1-cfed-4ceb-9e3e-819104bda1ee.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003ePerformance Assessment of the Deep Learning Weed Reorganization System\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Anbar","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":"AutoML, SVD, Deep Learning, and weeds","lastPublishedDoi":"10.21203/rs.3.rs-7223170/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7223170/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNumerous approaches based on machine learning have emerged in recent years to enhance crop protection efficiency. One example is the utilisation of deep neural networks to differentiate between various weed types in actual event scenarios. Nevertheless, these methods often need substantial input from experts who work iteratively to design robust deep learning systems. To simplify such processes and conserve resources, researchers have explored a fresh method known as automated deep learning. The our technology's recognition of weeds through the use of machine learning was evaluated using plant seedlings and weed collection from plants as a dataset to address the issue of weed recognition. The study compared various configurations, including plant segmentation, using a collection of classifiers in place of Softmax, and training on datasets that contain noise. The findings indicated ensuring performance, with F1-scores of 93.1% and 90.2% based on the dataset utilised. These results align with AutoML-linked studies while falling short of manually fine-tuned deep-learning-based systems created by human specialists. To conclude, exploring the potential of combining manual expert work and automated deep learning could be a promising direction for enhancing efficiency in plant defence.\u003c/p\u003e","manuscriptTitle":"Performance Assessment of the Deep Learning Weed Reorganization System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 06:49:38","doi":"10.21203/rs.3.rs-7223170/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":"8fd1f7b0-9c60-4071-a7d8-136b2a88d48c","owner":[],"postedDate":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52171202,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2025-07-29T06:49:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-29 06:49:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7223170","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7223170","identity":"rs-7223170","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}