Machine learning workflows detect ground-dwelling arthropods against heterogenous backgrounds

Robert J Boyd, Kayla I Perry, Thomas P Franzem, Christie Bahlai

Open source

DOI: 10.1093/jisesa/ieag091
Published: 2026-09-01
Container: Journal of Insect Science
Publisher: Oxford University Press (OUP)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1093/jisesa/ieag091,
  title = {Machine learning workflows detect ground-dwelling arthropods against heterogenous backgrounds},
  author = {Robert J Boyd and Kayla I Perry and Thomas P Franzem and Christie Bahlai},
  year = {2026},
  journal = {Journal of Insect Science},
  doi = {10.1093/jisesa/ieag091},
  url = {https://doi.org/10.1093/jisesa/ieag091}
}

RIS

TY  - JOUR
TI  - Machine learning workflows detect ground-dwelling arthropods against heterogenous backgrounds
AU  - Robert J Boyd
AU  - Kayla I Perry
AU  - Thomas P Franzem
AU  - Christie Bahlai
PY  - 2026
JO  - Journal of Insect Science
DO  - 10.1093/jisesa/ieag091
UR  - https://doi.org/10.1093/jisesa/ieag091
ER  -

APA

Boyd, R. J., Perry, K. I., Franzem, T. P., & Bahlai, C. (2026). Machine learning workflows detect ground-dwelling arthropods against heterogenous backgrounds. Journal of Insect Science. https://doi.org/10.1093/jisesa/ieag091

Source records

crossref · retrieved 2026-11-04T11:04:38.315Z