Impact of target domain integration on unsupervised anomaly detection in hydroponic agriculture

Impact of target domain integration on unsupervised anomaly detection in hydroponic agriculture | 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 Impact of target domain integration on unsupervised anomaly detection in hydroponic agriculture Matthis Lessing, Maximilian Stryczek, Jan Philipp Weiß, Fabian Gerz, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7900963/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract In modern hydroponic agriculture systems, precise monitoring of environmentaland growth parameters is essential. Unsupervised machine learning, particularlyanomaly detection (AD), offers a promising tool for automation and quality control.This study investigates the behavior of patch distribution modeling (PaDiM)under domain shift using strawberry images from two datasets. As the sourcedomain, the Riseholme-2021 dataset is employed, which contains high-resolutionimages of outdoor-grown strawberry fruits. The target domain is a novel datasetcomprising lower-resolution images of hydroponically grown strawberry plantsin an indoor environment. Experiments were conducted across thirteen domainmixingratios, with performance evaluated using the area under the receiveroperating characteristic curve (AUROC) and F1-score. The results show that theF1-score increases with a higher proportion of target-domain data, while AUROCpeaks at intermediate mixtures, indicating that combining source and target dataenhances generalization. Qualitative analysis confirms complementary strengths:source data improves robustness under low-light and blurred conditions, whereastarget data performs better under high exposure but tends to misclassify foreignobjects as anomalous. Physical sciences/Engineering Physical sciences/Mathematics and computing Biological sciences/Plant sciences Hydroponic Farming Machine Vision Visual Anomaly Detection Transfer Learning Domain Mixing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 04 Nov, 2025 Reviewers agreed at journal 26 Oct, 2025 Reviewers invited by journal 23 Oct, 2025 Editor invited by journal 23 Oct, 2025 Editor assigned by journal 21 Oct, 2025 Submission checks completed at journal 21 Oct, 2025 First submitted to journal 19 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. 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-7900963","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":535027441,"identity":"68a4ba87-3059-4a39-a97f-9a32298eca53","order_by":0,"name":"Matthis Lessing","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Matthis","middleName":"","lastName":"Lessing","suffix":""},{"id":535027442,"identity":"dd7abccd-556f-47e1-be05-306aa09e6c30","order_by":1,"name":"Maximilian Stryczek","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Maximilian","middleName":"","lastName":"Stryczek","suffix":""},{"id":535027443,"identity":"ab42f8ee-5be5-476c-b9c0-e93584b20dda","order_by":2,"name":"Jan Philipp Weiß","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"Philipp","lastName":"Weiß","suffix":""},{"id":535027448,"identity":"88c83a3d-afa2-4d71-b54c-5ede1bcecec9","order_by":3,"name":"Fabian Gerz","email":"","orcid":"","institution":"TH Köln - 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Unsupervised machine learning, particularlyanomaly detection (AD), offers a promising tool for automation and quality control.This study investigates the behavior of patch distribution modeling (PaDiM)under domain shift using strawberry images from two datasets. As the sourcedomain, the Riseholme-2021 dataset is employed, which contains high-resolutionimages of outdoor-grown strawberry fruits. The target domain is a novel datasetcomprising lower-resolution images of hydroponically grown strawberry plantsin an indoor environment. Experiments were conducted across thirteen domainmixingratios, with performance evaluated using the area under the receiveroperating characteristic curve (AUROC) and F1-score. The results show that theF1-score increases with a higher proportion of target-domain data, while AUROCpeaks at intermediate mixtures, indicating that combining source and target dataenhances generalization. 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