HSI-AgriFoodAnomaly: A Hyperspectral Dataset for Anomaly Detection in the Agri-Food  Industrial Inspection

HSI-AgriFoodAnomaly: A Hyperspectral Dataset for Anomaly Detection in the Agri-Food Industrial Inspection | 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 data-descriptor HSI-AgriFoodAnomaly: A Hyperspectral Dataset for Anomaly Detection in the Agri-Food Industrial Inspection Moahammed El Amine BECHAR, Nadine Abdallah Saab, Olga Assainova, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7819435/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 Ensuring food safety in agri-food production requires rapid and accurate anomaly detection. Hyperspectral imaging (HSI) is a promising modality for this task due to its rich spectral information; however, progress has been limited by the absence of realistic, publicly available datasets. This work introduces \textit{HSI-AgriFoodAnomaly}, the first open-access hyperspectral benchmark specifically designed for foreign objects anomaly detection in conveyor-based agri-food inspection. The dataset comprises 147 pixel-wise annotated HSI-cubes acquired under industrial-like conditions, covering the spectral range from 400 nm to 1000 nm and capturing 300 contiguous spectral bands, featuring a diverse set of anomaly categories including plastics, textiles, metals, glass, and paper. It supports multiple vision tasks such as binary classification, object detection, and semantic segmentation. To validate the dataset’s utility, we propose a baseline detection framework based on 2D convolutional neural networks adapted to full-spectrum HSI-cubes (300 bands). Experimental results demonstrate that models trained on HSI-cubes consistently outperform their RGB images, particularly in detecting subtle or previously unseen anomalies. The proposed dataset and baseline provide a solid foundation for advancing HSI-based food quality inspection systems. The dataset and source code are publicly available at: https://github.com/lsllabisen/HSI-AgriFoodAnomaly-Dataset.git. 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-7819435","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"data-descriptor","associatedPublications":[],"authors":[{"id":577476963,"identity":"b3cb8f28-eae3-4994-81b0-15befaf48df8","order_by":0,"name":"Moahammed El Amine 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Hyperspectral imaging (HSI) is a promising modality for this task due to its rich spectral information; however, progress has been limited by the absence of realistic, publicly available datasets. This work introduces \\textit{HSI-AgriFoodAnomaly}, the first open-access hyperspectral benchmark specifically designed for foreign objects anomaly detection in conveyor-based agri-food inspection. The dataset comprises 147 pixel-wise annotated HSI-cubes acquired under industrial-like conditions, covering the spectral range from 400 nm to 1000 nm and capturing 300 contiguous spectral bands, featuring a diverse set of anomaly categories including plastics, textiles, metals, glass, and paper. It supports multiple vision tasks such as binary classification, object detection, and semantic segmentation. To validate the dataset’s utility, we propose a baseline detection framework based on 2D convolutional neural networks adapted to full-spectrum HSI-cubes (300 bands). Experimental results demonstrate that models trained on HSI-cubes consistently outperform their RGB images, particularly in detecting subtle or previously unseen anomalies. The proposed dataset and baseline provide a solid foundation for advancing HSI-based food quality inspection systems. 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