Researchers from the University of Wyoming have developed a computer model that can identify wild animals in camera-trap photographs with remarkable accuracy and efficiency.
This breakthrough in artificial intelligence (AI), detailed in a paper recently published in the scientific journal Methods in Ecology and Evolution, represents a significant advancement in the study and conservation of wildlife. According to the paper’s authors, “the ability to rapidly identify millions of images from camera traps can fundamentally change the way ecologists design and implement wildlife studies.”
This study builds on previous research from the university in which a computer model analyzed 3.2 million images captured by camera traps in Africa. The A-I technique called deep learning categorized animal images at a 96.6% accuracy rate. This was the same accuracy rate as teams of human volunteers achieved, but the computer model worked at a much more rapid pace.
In the latest study, UW researchers trained a deep neural network on a powerful computer cluster to classify wildlife species using 3.37 million camera-trap images of 27 different animal species. The model was tested on nearly 375,000 images at a rate of about 2,000 images per minute. It achieved a 97.6% accuracy rate, which is likely the highest accuracy to date in using machine learning for wildlife image classification.
Artificial intelligence has been used in environmental science in other ways as well. For example, AI has been used to increase agricultural yields in farm fields and to help predict extreme weather.
Maybe artificial intelligence can prove to be a game changer for the environment.
Photo, posted January 8, 2012, courtesy of the U.S. Fish and Wildlife Service via Flickr.