Agriculture surrounding monitoring and object identification based on optimized You Only Look Once and Single Shot Multibox Detector setups using combined vision and thermal images

Agriculture surrounding monitoring and object identification based on optimized You Only Look Once and Single Shot Multibox Detector setups using combined vision and thermal images | 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 Agriculture surrounding monitoring and object identification based on optimized You Only Look Once and Single Shot Multibox Detector setups using combined vision and thermal images Krzysztof Tarasiuk, Arkadiusz Mystkowski, Michał Ostaszewski, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7930774/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract This paper presents a monitoring and object identification method in agricultural environments using both vision and thermal images. We evaluate two distinct approaches: a dual-network architecture, where separate models are trained for each image, and a unified network that integrates both data types into a single processing stream. Multiple prototypes based on You Only Look Once version 8 (YOLOv8) and Single Shot Multibox Detector (SSD) architectures were developed. YOLOv8 abandons the use of Cross Stage Partial (CSP) layers in favor of a simplified architecture based on C2f modules. In this work, we show that this modification reduces architectural complexity and enhances both computational efficiency and inference speed, during object class identification. The SSD design includes the removal of conv5_x , avgpool, fc and softmax layers from the original model and the setting of all strides in conv4_x to 1×1. The backbone is followed by 5 additional convolutional layers, to which five detection heads are attached, and the sixth head is attached to the conv4_x layer. Experimental results show differences between dual and single networks, where the mean Average Precision ( [email protected] ) changes from 0.88 to 0.90. The unified model provides improvement in overall performance due to information fusion during object identification from vision and thermal imagery data streams. The most significant variation was observed when transitioning from YOLOv8 to SSD architecture, where YOLOv8 outperformed SSD by achieving higher [email protected] scores of 0.98 for the Harvester class and 0.94 for the Tractor class. Compared to SSD where [email protected] achieved 0.91 and 0.88, respectively. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Physical sciences/Mathematics and computing Agriculture machine vision and thermal image surrounding monitoring object detection You Only Look Once algorithm Single Shot Multibox Detector algorithm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Dec, 2025 Reviews received at journal 05 Dec, 2025 Reviewers agreed at journal 27 Nov, 2025 Reviews received at journal 16 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers invited by journal 03 Nov, 2025 Editor invited by journal 30 Oct, 2025 Editor assigned by journal 26 Oct, 2025 Submission checks completed at journal 26 Oct, 2025 First submitted to journal 23 Oct, 2025 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. 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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-7930774","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":541559567,"identity":"7be5a4fd-74d0-4a38-a61b-918bc212c24d","order_by":0,"name":"Krzysztof Tarasiuk","email":"","orcid":"","institution":"Bialystok University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Krzysztof","middleName":"","lastName":"Tarasiuk","suffix":""},{"id":541559568,"identity":"dff283e3-35fa-4f14-b404-bbb58b1007b9","order_by":1,"name":"Arkadiusz Mystkowski","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIie3PMQrCMBSA4VcqOhjpmiLUK0Q6iYpXaREyKXR07KSLxdVriFAcIwFdCl0ruAmdHDrWzVRLdUodHfJDCBm+5AVApfrfHLGawDJg5cGrFW9y3FaE/Eh09Asx/DY1Hzm1SHxy+fhwBaM1I7onIZh1wi5yZjZJKOPzKAVzfSf6VjZYQcBZuGHS8vl8yYEk4hUkIT1BzLwg8dnnA0EmdYQIgsVgbsjEYFrxCq4hfd7ZDxGl9iShzjGIUoSj1OOyv1jnYHfJR1PL3Jzs7HG4WsZqurt5C8n3dWjg6qD5gIqdS8ALZd+kvEelUqlUn57OSFYsQ/0C9QAAAABJRU5ErkJggg==","orcid":"","institution":"Bialystok University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Arkadiusz","middleName":"","lastName":"Mystkowski","suffix":""},{"id":541559569,"identity":"412c73ad-c2f4-4481-9f5c-86790ea121dd","order_by":2,"name":"Michał Ostaszewski","email":"","orcid":"","institution":"Bialystok University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Michał","middleName":"","lastName":"Ostaszewski","suffix":""},{"id":541559570,"identity":"9a6e76c6-5ac0-442d-9146-30687dd4afd8","order_by":3,"name":"Konrad K. 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thermal image, surrounding monitoring, object detection, You Only Look Once algorithm, Single Shot Multibox Detector algorithm","lastPublishedDoi":"10.21203/rs.3.rs-7930774/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7930774/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents a monitoring and object identification method in agricultural environments using both vision and thermal images. We evaluate two distinct approaches: a dual-network architecture, where separate models are trained for each image, and a unified network that integrates both data types into a single processing stream. Multiple prototypes based on You Only Look Once version 8 (YOLOv8) and Single Shot Multibox Detector (SSD) architectures were developed. YOLOv8 abandons the use of Cross Stage Partial (CSP) layers in favor of a simplified architecture based on C2f modules. In this work, we show that this modification reduces architectural complexity and enhances both computational efficiency and inference speed, during object class identification. The SSD design includes the removal of \u003cem\u003econv5_x\u003c/em\u003e, avgpool, fc and softmax layers from the original model and the setting of all strides in \u003cem\u003econv4_x\u003c/em\u003e to 1×1. The backbone is followed by 5 additional convolutional layers, to which five detection heads are attached, and the sixth head is attached to the \u003cem\u003econv4_x\u003c/em\u003e layer. Experimental results show differences between dual and single networks, where the mean Average Precision ([email protected]) changes from 0.88 to 0.90. The unified model provides improvement in overall performance due to information fusion during object identification from vision and thermal imagery data streams. The most significant variation was observed when transitioning from YOLOv8 to SSD architecture, where YOLOv8 outperformed SSD by achieving higher [email protected] scores of 0.98 for the \u003cem\u003eHarvester \u003c/em\u003eclass and 0.94 for the \u003cem\u003eTractor\u003c/em\u003e class. Compared to SSD where [email protected] achieved 0.91 and 0.88, respectively.\u003c/p\u003e","manuscriptTitle":"Agriculture surrounding monitoring and object identification based on optimized You Only Look Once and Single Shot Multibox Detector setups using combined vision and thermal images","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 13:53:02","doi":"10.21203/rs.3.rs-7930774/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-10T05:12:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-06T03:35:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71719974228707116021306832619307474999","date":"2025-11-27T09:21:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-16T19:38:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227984205793138468004810714415356361405","date":"2025-11-07T12:49:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177531748281018895056744672157365710799","date":"2025-11-05T08:19:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-03T12:27:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-30T07:18:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-27T01:08:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-27T01:08:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-23T09:27:46+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"f027f296-b3e2-4691-aaea-280eddda7f13","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":57616670,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":57616671,"name":"Physical sciences/Engineering"},{"id":57616672,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-01-19T16:49:06+00:00","versionOfRecord":{"articleIdentity":"rs-7930774","link":"https://doi.org/10.1038/s41598-026-36181-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-13 16:29:57","publishedOnDateReadable":"January 13th, 2026"},"versionCreatedAt":"2025-11-13 13:53:02","video":"","vorDoi":"10.1038/s41598-026-36181-2","vorDoiUrl":"https://doi.org/10.1038/s41598-026-36181-2","workflowStages":[]},"version":"v1","identity":"rs-7930774","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7930774","identity":"rs-7930774","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}