Automated Insect Detection and Biomass Monitoring via AI and Electrical Field Sensor Technology

Automated Insect Detection and Biomass Monitoring via AI and Electrical Field Sensor Technology | 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 Automated Insect Detection and Biomass Monitoring via AI and Electrical Field Sensor Technology Freja Balmer Odgaard, Páll Vang Kjærbo, Amir Hossein Poorjam, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6429378/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Insects, vital for ecosystem stability, are declining globally, necessitating improved monitoring methods. Traditional approaches are labor-intensive, invasive, and limited in scope. This study presents a novel, automated, non-invasive insect monitoring system that detects electrical field modulations caused by flying insects. Utilizing in-field sensors, the system measures activity and biomass without physical trapping. It employs differential electric field measurements and convolutional neural networks for insect detection and wing-beat frequency analysis, along with a biomass algorithm estimating species-specific weights. Field validation within 2 sites at a Danish nature reserve showed moderate to strong correlations between sensor and Malaise trap measurements, particularly at site 1 (Spearman’s $\rho=0.725$ for counts, $0.644$ for biomass), validating the potential of our method. Additionally, sensor-sensor correlations ($\rho=0.758$ for counts, $0.867$ for biomass) exceeded Malaise-Malaise correlations ($\rho=0.597$ for counts, $0.641$ for biomass), suggesting greater measurement consistency for the sensors. However, these differences were not statistically significant ($P=0.304$ for counts, $P=0.057$ for biomass). While the biomass $P$-value did not reach the conventional significance threshold ($P<0.05$), its proximity suggests a more stable biomass estimate than Malaise trapping. Overall, this innovative approach bridges critical gaps in insect monitoring, offering scalable, ethical, and efficient solutions for insect conservation and ecosystem management. Physical sciences/Engineering/Electrical and electronic engineering Biological sciences/Zoology/Entomology Biological sciences/Computational biology and bioinformatics/Machine learning Insects Biomass Insect Monitoring Signal Processing Artificial Intelligence Convolutional Neural Network Wing Beat Frequency Full Text Additional Declarations Competing interest reported. All authors are current or former employees of FaunaPhotonics, the company that developed the electrical field sensor used in this study. Supplementary Files SupplementaryMaterials.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Jun, 2025 Reviews received at journal 02 Jun, 2025 Reviews received at journal 12 May, 2025 Reviewers agreed at journal 05 May, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers invited by journal 23 Apr, 2025 Editor assigned by journal 23 Apr, 2025 Editor invited by journal 22 Apr, 2025 Submission checks completed at journal 22 Apr, 2025 First submitted to journal 22 Apr, 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-6429378","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":455579417,"identity":"525bc8e4-a398-41a1-9800-e559c8df90fd","order_by":0,"name":"Freja Balmer Odgaard","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBAC9gYGBokPNmA2GxKJB/AcYGCQnJEGU5xApBZpHmQtBAGP9OGDt20S7BK3s5899uDjD5s8PvbjDxh+VGzDrYUvLdk6JyE5cWdPXrrhjIS0YjaeHAPGnjO3cWqx5+Exk879wZy44UCOmTRPwuHENgkeBmbGNtxaeEBaLBLqEzecfwPTwv6AsBYGoMoNN+C2MBgQ0MKWbNmTcNx4w413acDQTktsA/rlID6/8PAwH7zxI6FadsP53GPAOLVJnN9+/OGDHxW4tSDrRjAPEKMeVcsoGAWjYBSMAmQAAM7AUN7T7MibAAAAAElFTkSuQmCC","orcid":"","institution":"FaunaPhotonics","correspondingAuthor":true,"prefix":"","firstName":"Freja","middleName":"Balmer","lastName":"Odgaard","suffix":""},{"id":455579418,"identity":"39db5675-3c00-49c0-9a63-a8a96fc9d72d","order_by":1,"name":"Páll Vang Kjærbo","email":"","orcid":"","institution":"FaunaPhotonics","correspondingAuthor":false,"prefix":"","firstName":"Páll","middleName":"Vang","lastName":"Kjærbo","suffix":""},{"id":455579419,"identity":"9a2e2840-9e6c-447b-8534-90a63129da9c","order_by":2,"name":"Amir Hossein Poorjam","email":"","orcid":"","institution":"FaunaPhotonics","correspondingAuthor":false,"prefix":"","firstName":"Amir","middleName":"Hossein","lastName":"Poorjam","suffix":""},{"id":455579420,"identity":"41c336de-6afa-4875-8fab-13ab10b36033","order_by":3,"name":"Khaled Hechmi","email":"","orcid":"","institution":"FaunaPhotonics","correspondingAuthor":false,"prefix":"","firstName":"Khaled","middleName":"","lastName":"Hechmi","suffix":""},{"id":455579421,"identity":"d29476b0-9286-4f14-a1d2-bdd089cc7dbc","order_by":4,"name":"Rubens Monteiro Luciano","email":"","orcid":"","institution":"FaunaPhotonics","correspondingAuthor":false,"prefix":"","firstName":"Rubens","middleName":"Monteiro","lastName":"Luciano","suffix":""},{"id":455579422,"identity":"94edc5ef-8730-4554-bbd7-ab90dde1f5b8","order_by":5,"name":"Niels Krebs","email":"","orcid":"","institution":"FaunaPhotonics","correspondingAuthor":false,"prefix":"","firstName":"Niels","middleName":"","lastName":"Krebs","suffix":""}],"badges":[],"createdAt":"2025-04-11 14:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6429378/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6429378/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82747529,"identity":"8edebb78-d6ac-4c6e-a458-49edda595ab1","added_by":"auto","created_at":"2025-05-14 19:12:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1773581,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptrevised20252204.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6429378/v1_covered_11c16a33-4054-4b53-97d3-f2b8336d01fd.pdf"},{"id":82746947,"identity":"64b6e309-7473-440e-9e03-7af0755409f0","added_by":"auto","created_at":"2025-05-14 18:56:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":318991,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6429378/v1/ff8b023775362ec348f1f56e.pdf"}],"financialInterests":"Competing interest reported. 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Traditional approaches are labor-intensive, invasive, and limited in scope. This study presents a novel, automated, non-invasive insect monitoring system that detects electrical field modulations caused by flying insects. Utilizing in-field sensors, the system measures activity and biomass without physical trapping. It employs differential electric field measurements and convolutional neural networks for insect detection and wing-beat frequency analysis, along with a biomass algorithm estimating species-specific weights. Field validation within 2 sites at a Danish nature reserve showed moderate to strong correlations between sensor and Malaise trap measurements, particularly at site 1 (Spearman’s $\\rho=0.725$ for counts, $0.644$ for biomass), validating the potential of our method. Additionally, sensor-sensor correlations ($\\rho=0.758$ for counts, $0.867$ for biomass) exceeded Malaise-Malaise correlations ($\\rho=0.597$ for counts, $0.641$ for biomass), suggesting greater measurement consistency for the sensors. However, these differences were not statistically significant ($P=0.304$ for counts, $P=0.057$ for biomass). While the biomass $P$-value did not reach the conventional significance threshold ($P\u003c0.05$), its proximity suggests a more stable biomass estimate than Malaise trapping. 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