Preliminary assessment of hyperspectral imaging for detecting Halyomorpha halys and Nezara viridula feeding sites on bean pods: A proof-of-concept study

Preliminary assessment of hyperspectral imaging for detecting Halyomorpha halys and Nezara viridula feeding sites on bean pods: A proof-of-concept study | 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 Short Report Preliminary assessment of hyperspectral imaging for detecting Halyomorpha halys and Nezara viridula feeding sites on bean pods: A proof-of-concept study Julia Müller, Stefan Paulus, Ellen Richter, Erich-Christian Oerke This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7543685/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Jan, 2026 Read the published version in Journal of Plant Diseases and Protection → Version 1 posted 6 You are reading this latest preprint version Abstract This proof-of-concept study explores the potential of hyperspectral imaging (HSI) for nondestructive characterization of feeding damage by two invasive stink bug species on bean pods under controlled laboratory conditions. We compared spectral signatures of feeding sites caused by Halyomorpha halys and Nezara viridula on Phaseolus vulgaris seven days postinfestation. Using a limited dataset of 45 observations from 20 pods (12 individual plants), we identified distinct spectral modifications induced by both species. H. halys feeding increased visible spectrum reflectance (450-700 nm) with reduced near-infrared (NIR) reflectance, while N. viridula caused more severe spectral changes with substantial NIR decreases (up to 31% at 800 nm). Difference spectra revealed species-specific sensitivity patterns with maximum responses at 540 nm (green) and 740 nm (NIR) for both species, though N. viridula showed consistently larger deviations from healthy tissue. Key wavelengths for damage characterization were identified at 660-680 nm and 730-780 nm for both species. While these initial findings suggest potential spectral discrimination between stink bug feeding damage and healthy tissue, we emphasize that this preliminary study requires extensive validation with larger sample sizes, multiple cultivars, comparison with other herbivores, and field conditions before any practical application. The results provide first insights into spectral responses to stink bug feeding that warrant comprehensive follow-up investigation practical hyperspectralbased pest symptom monitoring tools that could enable more targeted management strategies against these economically significant pests. Stink bugs invasive species symptom detection hyperspectral imaging spectral signature machine learning Pentatomidae Full Text Cite Share Download PDF Status: Published Journal Publication published 04 Jan, 2026 Read the published version in Journal of Plant Diseases and Protection → Version 1 posted Editorial decision: Minor revisions 07 Oct, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviewers invited by journal 15 Sep, 2025 Editor invited by journal 08 Sep, 2025 Editor assigned by journal 06 Sep, 2025 First submitted to journal 05 Sep, 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. 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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-7543685","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":515108028,"identity":"b7f77151-0664-44ae-a81b-fa34b97c9cd5","order_by":0,"name":"Julia 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