The machine learning-powered BirdNET App reduces barriers to global bird research by enabling citizen science participation

Connor M Wood; Stefan Kahl; Ashakur Rahaman; Holger Klinck

doi:10.1371/journal.pbio.3001670

The machine learning-powered BirdNET App reduces barriers to global bird research by enabling citizen science participation

PLoS Biol. 2022 Jun 28;20(6):e3001670. doi: 10.1371/journal.pbio.3001670. eCollection 2022 Jun.

Authors

Connor M Wood¹, Stefan Kahl¹, Ashakur Rahaman¹, Holger Klinck¹

Affiliation

¹ K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, New York, United States of America.

Abstract

The BirdNET App, a free bird sound identification app for Android and iOS that includes over 3,000 bird species, reduces barriers to citizen science while generating tens of millions of bird observations globally that can be used to replicate known patterns in avian ecology.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Birds
Citizen Science*
Ecology
Machine Learning
Mobile Applications*

Grants and funding

Funding was provided by Jake Holshuh, the Arthur Vining Davis Foundation, the European Union, the European Social Fund for Germany, the Cornell Lab of Ornithology, the German Federal Ministry of Education and Research in the program of Entrepreneurial Regions InnoProfileTransfer in the project group localizIT (funding code 03IPT608X), and Chemnitz Technical University. All funding was secured by HK and SK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.