Cited by 1,230Advanced Neural Network ApplicationsWildlife Ecology and ConservationSmart Agriculture and AI

Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning

Open access
Norouzzadeh, Mohammad Sadegh, Nguyen, Anh, Kosmala, Margaret, Swanson, Alexandra, Palmer, Meredith S., Packer, Craig, Clune, Jeff·2018·Proceedings of the National Academy of Sciences

Abstract

Significance: Motion-sensor cameras in natural habitats offer the opportunity to inexpensively and unobtrusively gather vast amounts of data on animals in the wild. A key obstacle to harnessing their potential is the great cost of having humans analyze each image. Here, we demonstrate that a cutting-edge type of artificial intelligence called deep neural networks can automatically extract such invaluable information. For example, we show deep learning can automate animal identification for 99.3% of the 3.2 million-image Snapshot Serengeti dataset while performing at the same 96.6% accuracy of crowdsourced teams of human volunteers. Automatically, accurately, and inexpensively collecting such data could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, and animal behavior into “big data” sciences.

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  • Computer vision can be used to classify species from camera traps, supporting a real-time, less labor-intensive species count.

    “For instance, camera traps take photos automatically whenever a motion sensor is activated—computer vision can be used to classify the species that pass by, supporting a real-time, less labor-intensive species count [64,585,643].”

    From Tackling climate change with machine learning (2023) · cites this paper as [585]

  • Detecting and classifying animal species with DL for population estimates is becoming increasingly popular for images, acoustic spectrograms, and videos.

    “For these reasons, detecting and classifying animal species with DL for the purpose of population estimates is becoming increasingly popular for images52,53, acoustic spectrograms95, and videos96.”

    From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [53]

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Research works printed but not linked: 1

  1. [62]Ren S He K Girshick R Sun J 2015 Faster R-CNN: Towards real-time object detection with region proposal networks 2015 Advances in Neural Information Processing Systems (NIPS) Neural Information Processing Systems Foundation La Jolla, CA

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Authors
Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M. S., Packer, C., & Clune, J.
Affiliations
  • University of Wyoming (US)
  • Auburn University (US)
  • Harvard University (US)
  • University of Oxford (GB)
  • University of Minnesota (US)
Corresponding author
Clune, Jeff
Venue
Proceedings of the National Academy of Sciences
Published
2018-06-05
Volume
115
Issue
25
Pages
E5716-E5725
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2769210209
Cited by
1,230
References
77
Citations by year
2026: 170 · 2025: 155 · 2024: 147 · 2023: 177 · 2022: 157 · 2021: 174 · 2020: 136 · 2019: 83 · 2018: 29 · 2017: 2
FWCI
52.30
Type
journal article
Language
en
Open access
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
  • arXiv (Cornell University)
  • Auburn University Scholarly Repository
Funders
National Science Foundation (1453549)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
Keywords
camera traps, animal species identification, animal counting, animal behavior classification, convolutional neural networks, automated data extraction, wildlife conservation
MeSH terms
Machine Learning, Algorithms, Animals, Animals, Wild, Artificial Intelligence, Behavior, Animal, Ecology, Humans, Neural Networks, Computer, Ecosystem

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