Computer model successfully trained to identify animals in photos

A computer model developed at the University of Wyoming has demonstrated remarkable accuracy and efficiency in identifying images of wild animals from camera-trap photographs in North America. The artificial-intelligence breakthrough, is described as a significant advancement in the study and conservation of wildlife.

In the study, researchers developed a computer model and analyzed 3.2 million images captured by camera traps in Africa by a citizen science project called Snapshot Serengeti. The artificial-intelligence technique called "deep learning" categorized animal images at a 96.6 percent accuracy rate, the same as teams of human volunteers achieved, at a much more rapid pace than did the people.

In the latest study, the researchers trained a deep neural network to classify wildlife species using 3.37 million camera-trap images of 27 species of animals obtained from five states across the United States. The model then was tested on nearly 375,000 animal images at a rate of about 2,000 images per minute on a laptop computer, achieving 97.6 percent accuracy, likely the highest accuracy to date in using machine learning for wildlife image classification.

The researchers have made their model freely available in a software package in Program R. The package, "Machine Learning for Wildlife Image Classification in R (MLWIC)," allows other users to classify their images containing the 27 species in the dataset, but it also allows users to train their own machine learning models using images from new datasets.