SREL Reprint #3504

Machine learning to classify animal species in camera trap images: Applications in ecology

Michael A. Tabak1,2, Mohammad S. Norouzzadeh3, David W. Wolfson1, Steven J. Sweeney1,
Kurt C. Vercauteren4, Nathan P. Snow4, Joseph M. Halseth4, Paul A. Di Salvo1, Jesse S. Lewis5,
Michael D. White6, Ben Teton6, James C. Beasley7, Peter E. Schlichting7, Raoul K. Boughton8,
Bethany Wight8, Eric S. Newkirk9, Jacob S. Ivan9, Eric A. Odell9, Ryan K. Brook10, Paul M. Lukacs11,
Anna K. Moeller11, Elizabeth G. Mandeville2,12, Jeff Clune3, and Ryan S. Miller1

1Center for Epidemiology and Animal Health, United States Department of Agriculture, Fort Collins, Colorado
2Department of Zoology and Physiology, University of Wyoming, Laramie, Wyoming
3Computer Science Department, University of Wyoming, Laramie, Wyoming
4National Wildlife Research Center, United States Department of Agriculture, Fort Collins, Colorado
5College of Integrative Sciences and Arts, Arizona State University, Mesa, Arizona
6Tejon Ranch Conservancy, Lebec, California
7Savannah River Ecology Laboratory, Warnell School of Forestry and Natural Resources,
University of Georgia, Aiken, South Carolina
8Range Cattle Research and Education Center, Wildlife Ecology and Conservation,
University of Florida, Ona, Florida
9Colorado Parks and Wildlife, Fort Collins, Colorado
10Department of Animal and Poultry Science, University of Saskatchewan, Saskatoon, SK, Canada
11Wildlife Biology Program, Department of Ecosystem and Conservation Sciences,
W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, Montana
12Department of Botany, University of Wyoming, Laramie, Wyoming

Abstract:
1. Motion-activated cameras (“camera traps”) are increasingly used in ecological and management studies for remotely observing wildlife and are amongst the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analysed, typically by visually observing each image, in order to extract data that can be used in ecological analyses.
2. We trained machine learning models using convolutional neural networks with the ResNet-18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an independent subset of images not seen during training from the United States and on an out-of-sample (or “out-of-distribution” in the machine learning literature) dataset of ungulate images from Canada. We also tested the ability of our model to distinguish empty images from those with animals in another out-of-sample dataset from Tanzania, containing a faunal community that was novel to the model.
3. The trained model classified approximately 2,000 images per minute on a laptop computer with 16 gigabytes of RAM. The trained model achieved 98% accuracy at identifying species in the United States, the highest accuracy of such a model to date. Out-of-sample validation from Canada achieved 82% accuracy and correctly identified 94% of images containing an animal in the dataset from Tanzania. We provide an R package (Machine Learning for Wildlife Image Classification) that allows the users to (a) use the trained model presented here and (b) train their own model using classified images of wildlife from their studies.
4. The use of machine learning to rapidly and accurately classify wildlife in camera trap images can facilitate non-invasive sampling designs in ecological studies by reducing the burden of manually analysing images. Our R package makes these methods accessible to ecologists.

Keywords: artificial intelligence, camera trap, convolutional neural network, deep neural networks, image
classification, machine learning, R package, remote sensing

SREL Reprint #3504

Tabak, M. A., M. S. Norouzzadeh, D. W. Wolfson, S. J. Sweeney, K. C. Vercauteren, N. P. Snow, J. M. Halseth, P. A. DiSalvo, J. S. Lewis, M. D. White, B. Teton, J. C. Beasley, P. E. Schlichting, R. K. Boughton, B. Wight, E. S. Newkirk, J. S. Ivan, E. A. Odell, R. K. Brook, P. M. Lukacs, A. K. Moeller, E. G. Mandeville, J. Clune, and R. S. Miller. 2019. Machine learning to classify animal species in camera trap images: Applications in ecology. Methods in Ecology and Evolution 10(4): 585-590.

This information was provided by the University of Georgia's Savannah River Ecology Laboratory (srel.uga.edu).