{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T12:21:28Z","timestamp":1788351688734,"version":"build-2803163510"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"US National Science Foundation","doi-asserted-by":"publisher","award":["DBI 2045721"],"award-info":[{"award-number":["DBI 2045721"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Greenacres Foundation Ecology & Environment"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Camera-based monitoring of arthropods is an emerging tool for studying activity, abundance, and behavior. However, many current approaches rely on custom-built camera systems and complex analytical workflows, which limit accessibility and broader adoption. In addition, most arthropod camera-monitoring systems image subjects against uniform backgrounds, conditions that may alter natural activity patterns and reduce ecological realism. Here, we present a camera-monitoring workflow designed to increase accessibility by using commercially available game cameras coupled with a streamlined image-processing pipeline. We evaluated the performance of this approach by imaging arthropods against a complex, heterogeneous background to assess its effectiveness under conditions more representative of field environments. Our object-detection model performed reliably across evaluation metrics and accurately identified arthropods captured in the images despite background complexity. These results demonstrate that low-cost, widely available camera systems can be integrated with simplified analytical workflows to enable effective arthropod monitoring without specialized hardware. This approach provides a scalable and accessible method for studying arthropods in more natural contexts and may facilitate broader adoption of camera-based monitoring in ecological research and biodiversity assessments.<\/jats:p>","DOI":"10.1093\/jisesa\/ieag091","type":"journal-article","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T11:40:52Z","timestamp":1786448452000},"source":"Crossref","is-referenced-by-count":0,"title":["Machine learning workflows detect ground-dwelling arthropods against heterogenous backgrounds"],"prefix":"10.1093","volume":"26","author":[{"suffix":"III","given":"Robert J","family":"Boyd","sequence":"first","affiliation":[{"name":"Department of Biological Sciences, Kent State University , Kent, OH,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9903-8057","authenticated-orcid":false,"given":"Kayla I","family":"Perry","sequence":"additional","affiliation":[{"name":"Department of Entomology, The Ohio State University , Wooster, OH,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5234-0363","authenticated-orcid":false,"given":"Thomas P","family":"Franzem","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, Kent State University , Kent, OH,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8937-8709","authenticated-orcid":false,"given":"Christie","family":"Bahlai","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, Kent State University , Kent, OH,","place":["USA"]},{"name":"Kent State University Environmental Science and Design Research Institute , Kent, OH,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,9,1]]},"reference":[{"key":"2026090207593434800_ieag091-B1","author":"Abadi","year":"2016"},{"key":"2026090207593434800_ieag091-B2","doi-asserted-by":"publisher","first-page":"10167","DOI":"10.3390\/app121910167","article-title":"Deep learning based detector YOLOv5 for identifying insect pests","volume":"12","author":"Ahmad","year":"2022","journal-title":"Appl. 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